Title: Artificial intelligence

URL Source: https://en.wikipedia.org/wiki/Artificial_intelligence

Published Time: 2001-10-08T16:55:49Z

Markdown Content:
**Artificial intelligence** (**AI**), in its broadest sense, is [intelligence](https://en.wikipedia.org/wiki/Intelligence "Intelligence") exhibited by [machines](https://en.wikipedia.org/wiki/Machine "Machine"), particularly [computer systems](https://en.wikipedia.org/wiki/Computer_systems "Computer systems"). It is a [field of research](https://en.wikipedia.org/wiki/Field_of_research "Field of research") in [computer science](https://en.wikipedia.org/wiki/Computer_science "Computer science") that develops and studies methods and [software](https://en.wikipedia.org/wiki/Software "Software") that enable machines to [perceive their environment](https://en.wikipedia.org/wiki/Machine_perception "Machine perception") and uses [learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning") and intelligence to take actions that maximize their chances of achieving defined goals.[\[1\]](#cite_note-FOOTNOTERussellNorvig20211–4-1) Such machines may be called AIs.

[AI technology](https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence "Applications of artificial intelligence") is widely used [throughout industry](https://en.wikipedia.org/wiki/Artificial_intelligence_in_industry "Artificial intelligence in industry"), [government](https://en.wikipedia.org/wiki/Artificial_intelligence_in_government "Artificial intelligence in government"), and [science](https://en.wikipedia.org/wiki/Science "Science"). Some high-profile applications include advanced [web search engines](https://en.wikipedia.org/wiki/Web_search_engine "Web search engine") (e.g., [Google Search](https://en.wikipedia.org/wiki/Google_Search "Google Search")); [recommendation systems](https://en.wikipedia.org/wiki/Recommender_system "Recommender system") (used by [YouTube](https://en.wikipedia.org/wiki/YouTube "YouTube"), [Amazon](https://en.wikipedia.org/wiki/Amazon_\(company\) "Amazon (company)"), and [Netflix](https://en.wikipedia.org/wiki/Netflix "Netflix")); interacting [via human speech](https://en.wikipedia.org/wiki/Natural-language_understanding "Natural-language understanding") (e.g., [Google Assistant](https://en.wikipedia.org/wiki/Google_Assistant "Google Assistant"), [Siri](https://en.wikipedia.org/wiki/Siri "Siri"), and [Alexa](https://en.wikipedia.org/wiki/Amazon_Alexa "Amazon Alexa")); [autonomous vehicles](https://en.wikipedia.org/wiki/Autonomous_vehicles "Autonomous vehicles") (e.g., [Waymo](https://en.wikipedia.org/wiki/Waymo "Waymo")); [generative](https://en.wikipedia.org/wiki/Generative_artificial_intelligence "Generative artificial intelligence") and [creative](https://en.wikipedia.org/wiki/Computational_creativity "Computational creativity") tools (e.g., [ChatGPT](https://en.wikipedia.org/wiki/ChatGPT "ChatGPT") and [AI art](https://en.wikipedia.org/wiki/AI_art "AI art")); and [superhuman](https://en.wikipedia.org/wiki/Superintelligence "Superintelligence") play and analysis in [strategy games](https://en.wikipedia.org/wiki/Strategy_game "Strategy game") (e.g., [chess](https://en.wikipedia.org/wiki/Chess "Chess") and [Go](https://en.wikipedia.org/wiki/Go_\(game\) "Go (game)")).[\[2\]](#cite_note-FOOTNOTEGoogle2016-2) However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's [not labeled AI anymore](https://en.wikipedia.org/wiki/AI_effect "AI effect")."[\[3\]](#cite_note-3)[\[4\]](#cite_note-andreas-4)

[Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing "Alan Turing") was the first person to conduct substantial research in the field that he called machine intelligence.[\[5\]](#cite_note-turing-5) Artificial intelligence was founded as an academic discipline in 1956.[\[6\]](#cite_note-Dartmouth_workshop-6) The field went through multiple cycles of optimism,[\[7\]](#cite_note-AI_in_the_60s-7)[\[8\]](#cite_note-AI_in_the_80s-8) followed by periods of disappointment and loss of funding, known as [AI winter](https://en.wikipedia.org/wiki/AI_winter "AI winter").[\[9\]](#cite_note-First_AI_winter-9)[\[10\]](#cite_note-Second_AI_winter-10) Funding and interest vastly increased after 2012 when [deep learning](https://en.wikipedia.org/wiki/Deep_learning "Deep learning") surpassed all previous AI techniques,[\[11\]](#cite_note-Deep_learning_revolution-11) and after 2017 with the [transformer architecture](https://en.wikipedia.org/wiki/Transformer_\(machine_learning_model\) "Transformer (machine learning model)").[\[12\]](#cite_note-FOOTNOTEToews2023-12) This led to the [AI boom](https://en.wikipedia.org/wiki/AI_boom "AI boom") of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant [advances in artificial intelligence](https://en.wikipedia.org/wiki/Advances_in_artificial_intelligence "Advances in artificial intelligence").[\[13\]](#cite_note-FOOTNOTEFrank2023-13)

The growing use of artificial intelligence in the 21st century is influencing [a societal and economic shift](https://en.wikipedia.org/wiki/AI_era "AI era") towards increased [automation](https://en.wikipedia.org/wiki/Automation "Automation"), [data-driven decision-making](https://en.wikipedia.org/wiki/Data-driven_decision-making "Data-driven decision-making"), and the [integration of AI systems](https://en.wikipedia.org/wiki/Artificial_intelligence_systems_integration "Artificial intelligence systems integration") into various economic sectors and areas of life, [impacting job markets](https://en.wikipedia.org/wiki/Workplace_impact_of_artificial_intelligence "Workplace impact of artificial intelligence"), [healthcare](https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare "Artificial intelligence in healthcare"), government, industry, and [education](https://en.wikipedia.org/wiki/Artificial_intelligence_in_education "Artificial intelligence in education"). This raises questions about [the long-term effects](https://en.wikipedia.org/wiki/AI_aftermath_scenarios "AI aftermath scenarios"), [ethical implications](https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence "Ethics of artificial intelligence"), and [risks of AI](https://en.wikipedia.org/wiki/AI_risk "AI risk"), prompting discussions about [regulatory policies](https://en.wikipedia.org/wiki/Regulation_of_artificial_intelligence "Regulation of artificial intelligence") to ensure the [safety and benefits of the technology](https://en.wikipedia.org/wiki/AI_safety "AI safety").

The various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include [reasoning](https://en.wikipedia.org/wiki/Automated_reasoning "Automated reasoning"), [knowledge representation](https://en.wikipedia.org/wiki/Knowledge_representation "Knowledge representation"), [planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling "Automated planning and scheduling"), [learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning"), [natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing"), perception, and support for [robotics](https://en.wikipedia.org/wiki/Robotics "Robotics").[\[a\]](#cite_note-Problems_of_AI-14) [General intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence")—the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals.[\[14\]](#cite_note-AGI-15)

To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including [search](https://en.wikipedia.org/wiki/State_space_search "State space search") and [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization "Mathematical optimization"), [formal logic](https://en.wikipedia.org/wiki/Logic#Formal_logic "Logic"), [artificial neural networks](https://en.wikipedia.org/wiki/Artificial_neural_network "Artificial neural network"), and methods based on [statistics](https://en.wikipedia.org/wiki/Statistics "Statistics"), [operations research](https://en.wikipedia.org/wiki/Operations_research "Operations research"), and [economics](https://en.wikipedia.org/wiki/Economics "Economics").[\[b\]](#cite_note-Tools_of_AI-16) AI also draws upon [psychology](https://en.wikipedia.org/wiki/Psychology "Psychology"), [linguistics](https://en.wikipedia.org/wiki/Linguistics "Linguistics"), [philosophy](https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence "Philosophy of artificial intelligence"), [neuroscience](https://en.wikipedia.org/wiki/Neuroscience "Neuroscience"), and other fields.[\[15\]](#cite_note-AI_influences-17)

Goals
-----

The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.[\[a\]](#cite_note-Problems_of_AI-14)

### Reasoning and problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical [deductions](https://en.wikipedia.org/wiki/Deductive_reasoning "Deductive reasoning").[\[16\]](#cite_note-18) By the late 1980s and 1990s, methods were developed for dealing with [uncertain](https://en.wikipedia.org/wiki/Uncertainty "Uncertainty") or incomplete information, employing concepts from [probability](https://en.wikipedia.org/wiki/Probability "Probability") and [economics](https://en.wikipedia.org/wiki/Economics "Economics").[\[17\]](#cite_note-19)

Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow.[\[18\]](#cite_note-Intractability-20) Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[\[19\]](#cite_note-Psychological_evidence_of_sub-symbolic_reasoning-21) Accurate and efficient reasoning is an unsolved problem.

### Knowledge representation

[![Image 1](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e8/General_Formal_Ontology.svg/260px-General_Formal_Ontology.svg.png)](https://en.wikipedia.org/wiki/File:General_Formal_Ontology.svg)

An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.

[Knowledge representation](https://en.wikipedia.org/wiki/Knowledge_representation "Knowledge representation") and [knowledge engineering](https://en.wikipedia.org/wiki/Knowledge_engineering "Knowledge engineering")[\[20\]](#cite_note-22) allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,[\[21\]](#cite_note-FOOTNOTESmoliarZhang1994-23) scene interpretation,[\[22\]](#cite_note-FOOTNOTENeumannMöller2008-24) clinical decision support,[\[23\]](#cite_note-FOOTNOTEKupermanReichleyBailey2006-25) knowledge discovery (mining "interesting" and actionable inferences from large [databases](https://en.wikipedia.org/wiki/Database "Database")),[\[24\]](#cite_note-FOOTNOTEMcGarry2005-26) and other areas.[\[25\]](#cite_note-FOOTNOTEBertiniDel_BimboTorniai2006-27)

A [knowledge base](https://en.wikipedia.org/wiki/Knowledge_base "Knowledge base") is a body of knowledge represented in a form that can be used by a program. An [ontology](https://en.wikipedia.org/wiki/Ontology_\(information_science\) "Ontology (information science)") is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.[\[26\]](#cite_note-FOOTNOTERussellNorvig2021272-28) Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;[\[27\]](#cite_note-Representing_categories_and_relations-29) situations, events, states, and time;[\[28\]](#cite_note-Representing_time-30) causes and effects;[\[29\]](#cite_note-Representing_causation-31) knowledge about knowledge (what we know about what other people know);[\[30\]](#cite_note-Representing_knowledge_about_knowledge-32) [default reasoning](https://en.wikipedia.org/wiki/Default_reasoning "Default reasoning") (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[\[31\]](#cite_note-Default_reasoning_and_non-monotonic_logic-33) and many other aspects and domains of knowledge.

Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);[\[32\]](#cite_note-Breadth_of_commonsense_knowledge-34) and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally).[\[19\]](#cite_note-Psychological_evidence_of_sub-symbolic_reasoning-21) There is also the difficulty of [knowledge acquisition](https://en.wikipedia.org/wiki/Knowledge_acquisition "Knowledge acquisition"), the problem of obtaining knowledge for AI applications.[\[c\]](#cite_note-37)

### Planning and decision-making

An "agent" is anything that perceives and takes actions in the world. A [rational agent](https://en.wikipedia.org/wiki/Rational_agent "Rational agent") has goals or preferences and takes actions to make them happen.[\[d\]](#cite_note-38)[\[35\]](#cite_note-FOOTNOTERussellNorvig2021528-39) In [automated planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling "Automated planning and scheduling"), the agent has a specific goal.[\[36\]](#cite_note-40) In [automated decision-making](https://en.wikipedia.org/wiki/Automated_decision-making "Automated decision-making"), the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "[utility](https://en.wikipedia.org/wiki/Utility_\(economics\) "Utility (economics)")") that measures how much the agent prefers it. For each possible action, it can calculate the "[expected utility](https://en.wikipedia.org/wiki/Expected_utility "Expected utility")": the [utility](https://en.wikipedia.org/wiki/Utility "Utility") of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.[\[37\]](#cite_note-41)

In [classical planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling#classical_planning "Automated planning and scheduling"), the agent knows exactly what the effect of any action will be.[\[38\]](#cite_note-42) In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.[\[39\]](#cite_note-43)

In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with [inverse reinforcement learning](https://en.wikipedia.org/wiki/Inverse_reinforcement_learning "Inverse reinforcement learning")), or the agent can seek information to improve its preferences.[\[40\]](#cite_note-44) [Information value theory](https://en.wikipedia.org/wiki/Information_value_theory "Information value theory") can be used to weigh the value of exploratory or experimental actions.[\[41\]](#cite_note-45) The space of possible future actions and situations is typically [intractably](https://en.wikipedia.org/wiki/Intractable_problem "Intractable problem") large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.

A [Markov decision process](https://en.wikipedia.org/wiki/Markov_decision_process "Markov decision process") has a [transition model](https://en.wikipedia.org/wiki/Finite-state_machine "Finite-state machine") that describes the probability that a particular action will change the state in a particular way and a [reward function](https://en.wikipedia.org/wiki/Reward_function "Reward function") that supplies the utility of each state and the cost of each action. A [policy](https://en.wikipedia.org/wiki/Reinforcement_learning#Policy "Reinforcement learning") associates a decision with each possible state. The policy could be calculated (e.g., by [iteration](https://en.wikipedia.org/wiki/Policy_iteration "Policy iteration")), be [heuristic](https://en.wikipedia.org/wiki/Heuristic "Heuristic"), or it can be learned.[\[42\]](#cite_note-46)

[Game theory](https://en.wikipedia.org/wiki/Game_theory "Game theory") describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.[\[43\]](#cite_note-47)

### Learning

[Machine learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning") is the study of programs that can improve their performance on a given task automatically.[\[44\]](#cite_note-machine_learning-48) It has been a part of AI from the beginning.[\[e\]](#cite_note-51)

There are several kinds of machine learning. [Unsupervised learning](https://en.wikipedia.org/wiki/Unsupervised_learning "Unsupervised learning") analyzes a stream of data and finds patterns and makes predictions without any other guidance.[\[47\]](#cite_note-52) [Supervised learning](https://en.wikipedia.org/wiki/Supervised_learning "Supervised learning") requires a human to label the input data first, and comes in two main varieties: [classification](https://en.wikipedia.org/wiki/Statistical_classification "Statistical classification") (where the program must learn to predict what category the input belongs in) and [regression](https://en.wikipedia.org/wiki/Regression_analysis "Regression analysis") (where the program must deduce a numeric function based on numeric input).[\[48\]](#cite_note-Supervised_learning-53)

In [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning "Reinforcement learning"), the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".[\[49\]](#cite_note-54) [Transfer learning](https://en.wikipedia.org/wiki/Transfer_learning "Transfer learning") is when the knowledge gained from one problem is applied to a new problem.[\[50\]](#cite_note-55) [Deep learning](https://en.wikipedia.org/wiki/Deep_learning "Deep learning") is a type of machine learning that runs inputs through biologically inspired [artificial neural networks](https://en.wikipedia.org/wiki/Artificial_neural_networks "Artificial neural networks") for all of these types of learning.[\[51\]](#cite_note-56)

[Computational learning theory](https://en.wikipedia.org/wiki/Computational_learning_theory "Computational learning theory") can assess learners by [computational complexity](https://en.wikipedia.org/wiki/Computational_complexity "Computational complexity"), by [sample complexity](https://en.wikipedia.org/wiki/Sample_complexity "Sample complexity") (how much data is required), or by other notions of [optimization](https://en.wikipedia.org/wiki/Optimization_theory "Optimization theory").[\[52\]](#cite_note-57)

### Natural language processing

[Natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing") (NLP)[\[53\]](#cite_note-58) allows programs to read, write and communicate in human languages such as [English](https://en.wikipedia.org/wiki/English_\(language\) "English (language)"). Specific problems include [speech recognition](https://en.wikipedia.org/wiki/Speech_recognition "Speech recognition"), [speech synthesis](https://en.wikipedia.org/wiki/Speech_synthesis "Speech synthesis"), [machine translation](https://en.wikipedia.org/wiki/Machine_translation "Machine translation"), [information extraction](https://en.wikipedia.org/wiki/Information_extraction "Information extraction"), [information retrieval](https://en.wikipedia.org/wiki/Information_retrieval "Information retrieval") and [question answering](https://en.wikipedia.org/wiki/Question_answering "Question answering").[\[54\]](#cite_note-59)

Early work, based on [Noam Chomsky](https://en.wikipedia.org/wiki/Noam_Chomsky "Noam Chomsky")'s [generative grammar](https://en.wikipedia.org/wiki/Generative_grammar "Generative grammar") and [semantic networks](https://en.wikipedia.org/wiki/Semantic_network "Semantic network"), had difficulty with [word-sense disambiguation](https://en.wikipedia.org/wiki/Word-sense_disambiguation "Word-sense disambiguation")[\[f\]](#cite_note-60) unless restricted to small domains called "[micro-worlds](https://en.wikipedia.org/wiki/Blocks_world "Blocks world")" (due to the common sense knowledge problem[\[32\]](#cite_note-Breadth_of_commonsense_knowledge-34)). [Margaret Masterman](https://en.wikipedia.org/wiki/Margaret_Masterman "Margaret Masterman") believed that it was meaning and not grammar that was the key to understanding languages, and that [thesauri](https://en.wikipedia.org/wiki/Thesauri "Thesauri") and not dictionaries should be the basis of computational language structure.

Modern deep learning techniques for NLP include [word embedding](https://en.wikipedia.org/wiki/Word_embedding "Word embedding") (representing words, typically as [vectors](https://en.wikipedia.org/wiki/Vector_space "Vector space") encoding their meaning),[\[55\]](#cite_note-FOOTNOTERussellNorvig2021856–858-61) [transformers](https://en.wikipedia.org/wiki/Transformer_\(machine_learning_model\) "Transformer (machine learning model)") (a deep learning architecture using an [attention](https://en.wikipedia.org/wiki/Attention_\(machine_learning\) "Attention (machine learning)") mechanism),[\[56\]](#cite_note-FOOTNOTEDickson2022-62) and others.[\[57\]](#cite_note-63) In 2019, [generative pre-trained transformer](https://en.wikipedia.org/wiki/Generative_pre-trained_transformer "Generative pre-trained transformer") (or "GPT") language models began to generate coherent text,[\[58\]](#cite_note-FOOTNOTEVincent2019-64)[\[59\]](#cite_note-FOOTNOTERussellNorvig2021875–878-65) and by 2023 these models were able to get human-level scores on the [bar exam](https://en.wikipedia.org/wiki/Bar_exam "Bar exam"), [SAT](https://en.wikipedia.org/wiki/Scholastic_aptitude_test "Scholastic aptitude test") test, [GRE](https://en.wikipedia.org/wiki/Graduate_Record_Examinations "Graduate Record Examinations") test, and many other real-world applications.[\[60\]](#cite_note-FOOTNOTEBushwick2023-66)

### Perception

[Machine perception](https://en.wikipedia.org/wiki/Machine_perception "Machine perception") is the ability to use input from sensors (such as cameras, microphones, wireless signals, active [lidar](https://en.wikipedia.org/wiki/Lidar "Lidar"), sonar, radar, and [tactile sensors](https://en.wikipedia.org/wiki/Tactile_sensor "Tactile sensor")) to deduce aspects of the world. [Computer vision](https://en.wikipedia.org/wiki/Computer_vision "Computer vision") is the ability to analyze visual input.[\[61\]](#cite_note-67)

The field includes [speech recognition](https://en.wikipedia.org/wiki/Speech_recognition "Speech recognition"),[\[62\]](#cite_note-FOOTNOTERussellNorvig2021849–850-68) [image classification](https://en.wikipedia.org/wiki/Image_classification "Image classification"),[\[63\]](#cite_note-FOOTNOTERussellNorvig2021895–899-69) [facial recognition](https://en.wikipedia.org/wiki/Facial_recognition_system "Facial recognition system"), [object recognition](https://en.wikipedia.org/wiki/Object_recognition "Object recognition"),[\[64\]](#cite_note-FOOTNOTERussellNorvig2021899–901-70) and [robotic perception](https://en.wikipedia.org/wiki/Robotic_sensing "Robotic sensing").[\[65\]](#cite_note-FOOTNOTERussellNorvig2021931–938-71)

[![Image 2](https://upload.wikimedia.org/wikipedia/commons/thumb/2/27/Kismet-IMG_6007-gradient.jpg/220px-Kismet-IMG_6007-gradient.jpg)](https://en.wikipedia.org/wiki/File:Kismet-IMG_6007-gradient.jpg)

[Kismet](https://en.wikipedia.org/wiki/Kismet_\(robot\) "Kismet (robot)"), a robot head which was made in the 1990s; a machine that can recognize and simulate emotions.[\[66\]](#cite_note-FOOTNOTEMIT_AIL2014-72)

[Affective computing](https://en.wikipedia.org/wiki/Affective_computing "Affective computing") is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human [feeling, emotion, and mood](https://en.wikipedia.org/wiki/Affect_\(psychology\) "Affect (psychology)").[\[67\]](#cite_note-73) For example, some [virtual assistants](https://en.wikipedia.org/wiki/Virtual_assistant "Virtual assistant") are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate [human–computer interaction](https://en.wikipedia.org/wiki/Human%E2%80%93computer_interaction "Human–computer interaction").

However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.[\[68\]](#cite_note-FOOTNOTEWaddell2018-74) Moderate successes related to affective computing include textual [sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis "Sentiment analysis") and, more recently, [multimodal sentiment analysis](https://en.wikipedia.org/wiki/Multimodal_sentiment_analysis "Multimodal sentiment analysis"), wherein AI classifies the affects displayed by a videotaped subject.[\[69\]](#cite_note-FOOTNOTEPoriaCambriaBajpaiHussain2017-75)

### General intelligence

A machine with [artificial general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence") should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.[\[14\]](#cite_note-AGI-15)

Techniques
----------

AI research uses a wide variety of techniques to accomplish the goals above.[\[b\]](#cite_note-Tools_of_AI-16)

### Search and optimization

AI can solve many problems by intelligently searching through many possible solutions.[\[70\]](#cite_note-76) There are two very different kinds of search used in AI: [state space search](https://en.wikipedia.org/wiki/State_space_search "State space search") and [local search](https://en.wikipedia.org/wiki/Local_search_\(optimization\) "Local search (optimization)").

#### State space search

[State space search](https://en.wikipedia.org/wiki/State_space_search "State space search") searches through a tree of possible states to try to find a goal state.[\[71\]](#cite_note-State_space_search-77) For example, [planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling "Automated planning and scheduling") algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [means-ends analysis](https://en.wikipedia.org/wiki/Means-ends_analysis "Means-ends analysis").[\[72\]](#cite_note-FOOTNOTERussellNorvig2021§11.2-78)

[Simple exhaustive searches](https://en.wikipedia.org/wiki/Brute_force_search "Brute force search")[\[73\]](#cite_note-Uninformed_search-79) are rarely sufficient for most real-world problems: the [search space](https://en.wikipedia.org/wiki/Search_algorithm "Search algorithm") (the number of places to search) quickly grows to [astronomical numbers](https://en.wikipedia.org/wiki/Astronomically_large "Astronomically large"). The result is a search that is [too slow](https://en.wikipedia.org/wiki/Computation_time "Computation time") or never completes.[\[18\]](#cite_note-Intractability-20) "[Heuristics](https://en.wikipedia.org/wiki/Heuristics "Heuristics")" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.[\[74\]](#cite_note-Informed_search-80)

[Adversarial search](https://en.wikipedia.org/wiki/Adversarial_search "Adversarial search") is used for [game-playing](https://en.wikipedia.org/wiki/Game_AI "Game AI") programs, such as chess or Go. It searches through a [tree](https://en.wikipedia.org/wiki/Game_tree "Game tree") of possible moves and counter-moves, looking for a winning position.[\[75\]](#cite_note-81)

#### Local search

[![Image 3](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a3/Gradient_descent.gif/220px-Gradient_descent.gif)](https://en.wikipedia.org/wiki/File:Gradient_descent.gif)

Illustration of [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent "Gradient descent") for 3 different starting points. Two parameters (represented by the plan coordinates) are adjusted in order to minimize the [loss function](https://en.wikipedia.org/wiki/Loss_function "Loss function") (the height).

[Local search](https://en.wikipedia.org/wiki/Local_search_\(optimization\) "Local search (optimization)") uses [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization "Mathematical optimization") to find a solution to a problem. It begins with some form of guess and refines it incrementally.[\[76\]](#cite_note-Local_search2-82)

[Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent "Gradient descent") is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a [loss function](https://en.wikipedia.org/wiki/Loss_function "Loss function"). Variants of gradient descent are commonly used to train neural networks.[\[77\]](#cite_note-83)

Another type of local search is [evolutionary computation](https://en.wikipedia.org/wiki/Evolutionary_computation "Evolutionary computation"), which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, [selecting](https://en.wikipedia.org/wiki/Artificial_selection "Artificial selection") only the fittest to survive each generation.[\[78\]](#cite_note-84)

Distributed search processes can coordinate via [swarm intelligence](https://en.wikipedia.org/wiki/Swarm_intelligence "Swarm intelligence") algorithms. Two popular swarm algorithms used in search are [particle swarm optimization](https://en.wikipedia.org/wiki/Particle_swarm_optimization "Particle swarm optimization") (inspired by bird [flocking](https://en.wikipedia.org/wiki/Flocking_\(behavior\) "Flocking (behavior)")) and [ant colony optimization](https://en.wikipedia.org/wiki/Ant_colony_optimization "Ant colony optimization") (inspired by [ant trails](https://en.wikipedia.org/wiki/Ant_trail "Ant trail")).[\[79\]](#cite_note-FOOTNOTEMerkleMiddendorf2013-85)

### Logic

Formal [logic](https://en.wikipedia.org/wiki/Logic "Logic") is used for [reasoning](https://en.wikipedia.org/wiki/Automatic_reasoning "Automatic reasoning") and [knowledge representation](https://en.wikipedia.org/wiki/Knowledge_representation "Knowledge representation").[\[80\]](#cite_note-Logic-86) Formal logic comes in two main forms: [propositional logic](https://en.wikipedia.org/wiki/Propositional_logic "Propositional logic") (which operates on statements that are true or false and uses [logical connectives](https://en.wikipedia.org/wiki/Logical_connective "Logical connective") such as "and", "or", "not" and "implies")[\[81\]](#cite_note-Propositional_logic-87) and [predicate logic](https://en.wikipedia.org/wiki/Predicate_logic "Predicate logic") (which also operates on objects, predicates and relations and uses [quantifiers](https://en.wikipedia.org/wiki/Quantifier_\(logic\) "Quantifier (logic)") such as "_Every_ _X_ is a _Y_" and "There are _some_ _X_s that are _Y_s").[\[82\]](#cite_note-Predicate_logic-88)

[Deductive reasoning](https://en.wikipedia.org/wiki/Deductive_reasoning "Deductive reasoning") in logic is the process of [proving](https://en.wikipedia.org/wiki/Logical_proof "Logical proof") a new statement ([conclusion](https://en.wikipedia.org/wiki/Logical_consequence "Logical consequence")) from other statements that are given and assumed to be true (the [premises](https://en.wikipedia.org/wiki/Premise "Premise")).[\[83\]](#cite_note-Inference-89) Proofs can be structured as proof [trees](https://en.wikipedia.org/wiki/Tree_structure "Tree structure"), in which nodes are labelled by sentences, and children nodes are connected to parent nodes by [inference rules](https://en.wikipedia.org/wiki/Inference_rule "Inference rule").

Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or [axioms](https://en.wikipedia.org/wiki/Axiom "Axiom"). In the case of [Horn clauses](https://en.wikipedia.org/wiki/Horn_clause "Horn clause"), problem-solving search can be performed by reasoning [forwards](https://en.wikipedia.org/wiki/Forward_chaining "Forward chaining") from the premises or [backwards](https://en.wikipedia.org/wiki/Backward_chaining "Backward chaining") from the problem.[\[84\]](#cite_note-Logic_as_search-90) In the more general case of the clausal form of [first-order logic](https://en.wikipedia.org/wiki/First-order_logic "First-order logic"), [resolution](https://en.wikipedia.org/wiki/Resolution_\(logic\) "Resolution (logic)") is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.[\[85\]](#cite_note-Resolution-91)

Inference in both Horn clause logic and first-order logic is [undecidable](https://en.wikipedia.org/wiki/Undecidable_problem "Undecidable problem"), and therefore [intractable](https://en.wikipedia.org/wiki/Intractable_problem "Intractable problem"). However, backward reasoning with Horn clauses, which underpins computation in the [logic programming](https://en.wikipedia.org/wiki/Logic_programming "Logic programming") language [Prolog](https://en.wikipedia.org/wiki/Prolog "Prolog"), is [Turing complete](https://en.wikipedia.org/wiki/Turing_completeness "Turing completeness"). Moreover, its efficiency is competitive with computation in other [symbolic programming](https://en.wikipedia.org/wiki/Symbolic_programming "Symbolic programming") languages.[\[86\]](#cite_note-92)

[Fuzzy logic](https://en.wikipedia.org/wiki/Fuzzy_logic "Fuzzy logic") assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.[\[87\]](#cite_note-Fuzzy_logic-93)

[Non-monotonic logics](https://en.wikipedia.org/wiki/Non-monotonic_logic "Non-monotonic logic"), including logic programming with [negation as failure](https://en.wikipedia.org/wiki/Negation_as_failure "Negation as failure"), are designed to handle [default reasoning](https://en.wikipedia.org/wiki/Default_reasoning "Default reasoning").[\[31\]](#cite_note-Default_reasoning_and_non-monotonic_logic-33) Other specialized versions of logic have been developed to describe many complex domains.

### Probabilistic methods for uncertain reasoning

[![Image 4](https://upload.wikimedia.org/wikipedia/commons/thumb/0/0e/SimpleBayesNet.svg/380px-SimpleBayesNet.svg.png)](https://en.wikipedia.org/wiki/File:SimpleBayesNet.svg)

A simple [Bayesian network](https://en.wikipedia.org/wiki/Bayesian_network "Bayesian network"), with the associated [conditional probability tables](https://en.wikipedia.org/wiki/Conditional_probability_table "Conditional probability table")

Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from [probability](https://en.wikipedia.org/wiki/Probability "Probability") theory and economics.[\[88\]](#cite_note-Uncertain_reasoning-94) Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [decision theory](https://en.wikipedia.org/wiki/Decision_theory "Decision theory"), [decision analysis](https://en.wikipedia.org/wiki/Decision_analysis "Decision analysis"),[\[89\]](#cite_note-Decisions_theory_and_analysis-95) and [information value theory](https://en.wikipedia.org/wiki/Information_value_theory "Information value theory").[\[90\]](#cite_note-Information_value_theory-96) These tools include models such as [Markov decision processes](https://en.wikipedia.org/wiki/Markov_decision_process "Markov decision process"),[\[91\]](#cite_note-Markov_decision_process-97) dynamic [decision networks](https://en.wikipedia.org/wiki/Decision_network "Decision network"),[\[92\]](#cite_note-Stochastic_temporal_models-98) [game theory](https://en.wikipedia.org/wiki/Game_theory "Game theory") and [mechanism design](https://en.wikipedia.org/wiki/Mechanism_design "Mechanism design").[\[93\]](#cite_note-Game_theory_and_mechanism_design-99)

[Bayesian networks](https://en.wikipedia.org/wiki/Bayesian_network "Bayesian network")[\[94\]](#cite_note-Bayesian_networks-100) are a tool that can be used for [reasoning](https://en.wikipedia.org/wiki/Automated_reasoning "Automated reasoning") (using the [Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_inference "Bayesian inference") algorithm),[\[g\]](#cite_note-102)[\[96\]](#cite_note-Bayesian_inference-103) [learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning") (using the [expectation-maximization algorithm](https://en.wikipedia.org/wiki/Expectation-maximization_algorithm "Expectation-maximization algorithm")),[\[h\]](#cite_note-105)[\[98\]](#cite_note-Bayesian_learning-106) [planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling "Automated planning and scheduling") (using [decision networks](https://en.wikipedia.org/wiki/Decision_network "Decision network"))[\[99\]](#cite_note-Bayesian_decision_networks-107) and [perception](https://en.wikipedia.org/wiki/Machine_perception "Machine perception") (using [dynamic Bayesian networks](https://en.wikipedia.org/wiki/Dynamic_Bayesian_network "Dynamic Bayesian network")).[\[92\]](#cite_note-Stochastic_temporal_models-98)

Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping [perception](https://en.wikipedia.org/wiki/Machine_perception "Machine perception") systems analyze processes that occur over time (e.g., [hidden Markov models](https://en.wikipedia.org/wiki/Hidden_Markov_model "Hidden Markov model") or [Kalman filters](https://en.wikipedia.org/wiki/Kalman_filter "Kalman filter")).[\[92\]](#cite_note-Stochastic_temporal_models-98)

[![Image 5](https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/EM_Clustering_of_Old_Faithful_data.gif/260px-EM_Clustering_of_Old_Faithful_data.gif)](https://en.wikipedia.org/wiki/File:EM_Clustering_of_Old_Faithful_data.gif)

[Expectation-maximization](https://en.wikipedia.org/wiki/Expectation-maximization "Expectation-maximization") [clustering](https://en.wikipedia.org/wiki/Cluster_analysis "Cluster analysis") of [Old Faithful](https://en.wikipedia.org/wiki/Old_Faithful "Old Faithful") eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.

### Classifiers and statistical learning methods

The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. [Classifiers](https://en.wikipedia.org/wiki/Classifier_\(mathematics\) "Classifier (mathematics)")[\[100\]](#cite_note-Statistical_classifiers-108) are functions that use [pattern matching](https://en.wikipedia.org/wiki/Pattern_matching "Pattern matching") to determine the closest match. They can be fine-tuned based on chosen examples using [supervised learning](https://en.wikipedia.org/wiki/Supervised_learning "Supervised learning"). Each pattern (also called an "[observation](https://en.wikipedia.org/wiki/Random_variate "Random variate")") is labeled with a certain predefined class. All the observations combined with their class labels are known as a [data set](https://en.wikipedia.org/wiki/Data_set "Data set"). When a new observation is received, that observation is classified based on previous experience.[\[48\]](#cite_note-Supervised_learning-53)

There are many kinds of classifiers in use. The [decision tree](https://en.wikipedia.org/wiki/Decision_tree "Decision tree") is the simplest and most widely used symbolic machine learning algorithm.[\[101\]](#cite_note-109) [K-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbor "K-nearest neighbor") algorithm was the most widely used analogical AI until the mid-1990s, and [Kernel methods](https://en.wikipedia.org/wiki/Kernel_methods "Kernel methods") such as the [support vector machine](https://en.wikipedia.org/wiki/Support_vector_machine "Support vector machine") (SVM) displaced k-nearest neighbor in the 1990s.[\[102\]](#cite_note-110) The [naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier "Naive Bayes classifier") is reportedly the "most widely used learner"[\[103\]](#cite_note-FOOTNOTEDomingos2015152-111) at Google, due in part to its scalability.[\[104\]](#cite_note-112) [Neural networks](https://en.wikipedia.org/wiki/Artificial_neural_network "Artificial neural network") are also used as classifiers.[\[105\]](#cite_note-Neural_networks-113)

### Artificial neural networks

[![Image 6](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/Artificial_neural_network.svg/220px-Artificial_neural_network.svg.png)](https://en.wikipedia.org/wiki/File:Artificial_neural_network.svg)

A neural network is an interconnected group of nodes, akin to the vast network of [neurons](https://en.wikipedia.org/wiki/Neuron "Neuron") in the [human brain](https://en.wikipedia.org/wiki/Human_brain "Human brain").

An artificial neural network is based on a collection of nodes also known as [artificial neurons](https://en.wikipedia.org/wiki/Artificial_neurons "Artificial neurons"), which loosely model the [neurons](https://en.wikipedia.org/wiki/Neurons "Neurons") in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the [weight](https://en.wikipedia.org/wiki/Weighting "Weighting") crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.[\[105\]](#cite_note-Neural_networks-113)

Learning algorithms for neural networks use [local search](https://en.wikipedia.org/wiki/Local_search_\(optimization\) "Local search (optimization)") to choose the weights that will get the right output for each input during training. The most common training technique is the [backpropagation](https://en.wikipedia.org/wiki/Backpropagation "Backpropagation") algorithm.[\[106\]](#cite_note-Backpropagation-114) Neural networks learn to model complex relationships between inputs and outputs and [find patterns](https://en.wikipedia.org/wiki/Pattern_recognition "Pattern recognition") in data. In theory, a neural network can learn any function.[\[107\]](#cite_note-115)

In [feedforward neural networks](https://en.wikipedia.org/wiki/Feedforward_neural_network "Feedforward neural network") the signal passes in only one direction.[\[108\]](#cite_note-116) [Recurrent neural networks](https://en.wikipedia.org/wiki/Recurrent_neural_network "Recurrent neural network") feed the output signal back into the input, which allows short-term memories of previous input events. [Long short term memory](https://en.wikipedia.org/wiki/Long_short_term_memory "Long short term memory") is the most successful network architecture for recurrent networks.[\[109\]](#cite_note-117) [Perceptrons](https://en.wikipedia.org/wiki/Perceptron "Perceptron")[\[110\]](#cite_note-118) use only a single layer of neurons, deep learning[\[111\]](#cite_note-Deep_learning-119) uses multiple layers. [Convolutional neural networks](https://en.wikipedia.org/wiki/Convolutional_neural_network "Convolutional neural network") strengthen the connection between neurons that are "close" to each other—this is especially important in [image processing](https://en.wikipedia.org/wiki/Image_processing "Image processing"), where a local set of neurons must [identify an "edge"](https://en.wikipedia.org/wiki/Edge_detection "Edge detection") before the network can identify an object.[\[112\]](#cite_note-120)

### Deep learning

[![Image 7](https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/AI_hierarchy.svg/170px-AI_hierarchy.svg.png)](https://en.wikipedia.org/wiki/File:AI_hierarchy.svg)

Deep learning[\[111\]](#cite_note-Deep_learning-119) uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in [image processing](https://en.wikipedia.org/wiki/Image_processing "Image processing"), lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.[\[113\]](#cite_note-FOOTNOTEDengYu2014199–200-121)

Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including [computer vision](https://en.wikipedia.org/wiki/Computer_vision "Computer vision"), [speech recognition](https://en.wikipedia.org/wiki/Speech_recognition "Speech recognition"), [natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing"), [image classification](https://en.wikipedia.org/wiki/Image_classification "Image classification"),[\[114\]](#cite_note-FOOTNOTECiresanMeierSchmidhuber2012-122) and others. The reason that deep learning performs so well in so many applications is not known as of 2023.[\[115\]](#cite_note-FOOTNOTERussellNorvig2021751-123) The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s)[\[i\]](#cite_note-131) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to [GPUs](https://en.wikipedia.org/wiki/Graphics_processing_units "Graphics processing units")) and the availability of vast amounts of training data, especially the giant [curated datasets](https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research "List of datasets for machine-learning research") used for benchmark testing, such as [ImageNet](https://en.wikipedia.org/wiki/ImageNet "ImageNet").[\[j\]](#cite_note-133)

### GPT

[Generative pre-trained transformers](https://en.wikipedia.org/wiki/Generative_pre-trained_transformer "Generative pre-trained transformer") (GPT) are [large language models](https://en.wikipedia.org/wiki/Large_language_model "Large language model") that are based on the semantic relationships between words in sentences ([natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing")). Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next [token](https://en.wikipedia.org/wiki/Lexical_analysis "Lexical analysis") (a token being usually a word, subword, or punctuation). Throughout this pre-training, GPT models accumulate knowledge about the world, and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful and harmless, usually with a technique called [reinforcement learning from human feedback](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback "Reinforcement learning from human feedback") (RLHF). Current GPT models are still prone to generating falsehoods called "[hallucinations](https://en.wikipedia.org/wiki/Hallucination_\(artificial_intelligence\) "Hallucination (artificial intelligence)")", although this can be reduced with RLHF and quality data. They are used in [chatbots](https://en.wikipedia.org/wiki/Chatbot "Chatbot"), which allow you to ask a question or request a task in simple text.[\[124\]](#cite_note-FOOTNOTESmith2023-134)[\[125\]](#cite_note-135)

Current models and services include: [Gemini](https://en.wikipedia.org/wiki/Gemini_\(chatbot\) "Gemini (chatbot)") (formerly Bard), [ChatGPT](https://en.wikipedia.org/wiki/ChatGPT "ChatGPT"), [Grok](https://en.wikipedia.org/wiki/Grok_\(chatbot\) "Grok (chatbot)"), [Claude](https://en.wikipedia.org/wiki/Anthropic#Claude "Anthropic"), [Copilot](https://en.wikipedia.org/wiki/Microsoft_Copilot "Microsoft Copilot") and [LLaMA](https://en.wikipedia.org/wiki/LLaMA "LLaMA").[\[126\]](#cite_note-136) [Multimodal](https://en.wikipedia.org/wiki/Multimodal_learning "Multimodal learning") GPT models can process different types of data ([modalities](https://en.wikipedia.org/wiki/Modality_\(human%E2%80%93computer_interaction\) "Modality (human–computer interaction)")) such as images, videos, sound, and text.[\[127\]](#cite_note-FOOTNOTEMarmouyet2023-137)

### Specialized hardware and software

In the late 2010s, [graphics processing units](https://en.wikipedia.org/wiki/Graphics_processing_unit "Graphics processing unit") (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized [TensorFlow](https://en.wikipedia.org/wiki/TensorFlow "TensorFlow") software had replaced previously used [central processing unit](https://en.wikipedia.org/wiki/Central_processing_unit "Central processing unit") (CPUs) as the dominant means for large-scale (commercial and academic) [machine learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning") models' training.[\[128\]](#cite_note-FOOTNOTEKobielus2019-138) Historically, specialized languages, such as [Lisp](https://en.wikipedia.org/wiki/Lisp_\(programming_language\) "Lisp (programming language)"), [Prolog](https://en.wikipedia.org/wiki/Prolog "Prolog"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") and others, had been used.

Applications
------------

AI and machine learning technology is used in most of the essential applications of the 2020s, including: [search engines](https://en.wikipedia.org/wiki/Search_engines "Search engines") (such as [Google Search](https://en.wikipedia.org/wiki/Google_Search "Google Search")), [targeting online advertisements](https://en.wikipedia.org/wiki/Targeted_advertising "Targeted advertising"), [recommendation systems](https://en.wikipedia.org/wiki/Recommender_system "Recommender system") (offered by [Netflix](https://en.wikipedia.org/wiki/Netflix "Netflix"), [YouTube](https://en.wikipedia.org/wiki/YouTube "YouTube") or [Amazon](https://en.wikipedia.org/wiki/Amazon_\(company\) "Amazon (company)")), driving [internet traffic](https://en.wikipedia.org/wiki/Internet_traffic "Internet traffic"), [targeted advertising](https://en.wikipedia.org/wiki/Marketing_and_artificial_intelligence "Marketing and artificial intelligence") ([AdSense](https://en.wikipedia.org/wiki/AdSense "AdSense"), [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook")), [virtual assistants](https://en.wikipedia.org/wiki/Virtual_assistant "Virtual assistant") (such as [Siri](https://en.wikipedia.org/wiki/Siri "Siri") or [Alexa](https://en.wikipedia.org/wiki/Amazon_Alexa "Amazon Alexa")), [autonomous vehicles](https://en.wikipedia.org/wiki/Autonomous_vehicles "Autonomous vehicles") (including [drones](https://en.wikipedia.org/wiki/Unmanned_aerial_vehicle "Unmanned aerial vehicle"), [ADAS](https://en.wikipedia.org/wiki/Advanced_driver-assistance_system "Advanced driver-assistance system") and [self-driving cars](https://en.wikipedia.org/wiki/Self-driving_cars "Self-driving cars")), [automatic language translation](https://en.wikipedia.org/wiki/Machine_translation "Machine translation") ([Microsoft Translator](https://en.wikipedia.org/wiki/Microsoft_Translator "Microsoft Translator"), [Google Translate](https://en.wikipedia.org/wiki/Google_Translate "Google Translate")), [facial recognition](https://en.wikipedia.org/wiki/Facial_recognition_system "Facial recognition system") ([Apple](https://en.wikipedia.org/wiki/Apple_Computer "Apple Computer")'s [Face ID](https://en.wikipedia.org/wiki/Face_ID "Face ID") or [Microsoft](https://en.wikipedia.org/wiki/Microsoft "Microsoft")'s [DeepFace](https://en.wikipedia.org/wiki/DeepFace "DeepFace") and [Google](https://en.wikipedia.org/wiki/Google "Google")'s [FaceNet](https://en.wikipedia.org/wiki/FaceNet "FaceNet")) and [image labeling](https://en.wikipedia.org/wiki/Automatic_image_annotation "Automatic image annotation") (used by [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook"), Apple's [iPhoto](https://en.wikipedia.org/wiki/IPhoto "IPhoto") and [TikTok](https://en.wikipedia.org/wiki/TikTok "TikTok")).

### Health and medicine

The application of AI in [medicine](https://en.wikipedia.org/wiki/Medicine "Medicine") and [medical research](https://en.wikipedia.org/wiki/Medical_research "Medical research") has the potential to increase patient care and quality of life.[\[129\]](#cite_note-139) Through the lens of the [Hippocratic Oath](https://en.wikipedia.org/wiki/Hippocratic_Oath "Hippocratic Oath"), medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.

For medical research, AI is an important tool for processing and integrating [big data](https://en.wikipedia.org/wiki/Big_data "Big data"). This is particularly important for [organoid](https://en.wikipedia.org/wiki/Organoid "Organoid") and [tissue engineering](https://en.wikipedia.org/wiki/Tissue_engineering "Tissue engineering") development which use [microscopy](https://en.wikipedia.org/wiki/Microscopy "Microscopy") imaging as a key technique in fabrication.[\[130\]](#cite_note-The_future_of_personalized_cardiova-140) It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.[\[130\]](#cite_note-The_future_of_personalized_cardiova-140) New AI tools can deepen the understanding of biomedically relevant pathways. For example, [AlphaFold 2](https://en.wikipedia.org/wiki/AlphaFold_2 "AlphaFold 2") (2021) demonstrated the ability to approximate, in hours rather than months, the 3D [structure of a protein](https://en.wikipedia.org/wiki/Protein_structure "Protein structure").[\[131\]](#cite_note-141) In 2023, it was reported that AI-guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria.[\[132\]](#cite_note-142) In 2024, researchers used machine learning to accelerate the search for [Parkinson's disease](https://en.wikipedia.org/wiki/Parkinson%27s_disease "Parkinson's disease") drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of [alpha-synuclein](https://en.wikipedia.org/wiki/Alpha-synuclein "Alpha-synuclein") (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold.[\[133\]](#cite_note-143)[\[134\]](#cite_note-144)

### Games

[Game playing](https://en.wikipedia.org/wiki/Game_AI "Game AI") programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.[\[135\]](#cite_note-145) [Deep Blue](https://en.wikipedia.org/wiki/IBM_Deep_Blue "IBM Deep Blue") became the first computer chess-playing system to beat a reigning world chess champion, [Garry Kasparov](https://en.wikipedia.org/wiki/Garry_Kasparov "Garry Kasparov"), on 11 May 1997.[\[136\]](#cite_note-146) In 2011, in a _[Jeopardy!](https://en.wikipedia.org/wiki/Jeopardy! "Jeopardy!")_ [quiz show](https://en.wikipedia.org/wiki/Quiz_show "Quiz show") exhibition match, [IBM](https://en.wikipedia.org/wiki/IBM "IBM")'s [question answering system](https://en.wikipedia.org/wiki/Question_answering_system "Question answering system"), [Watson](https://en.wikipedia.org/wiki/Watson_\(artificial_intelligence_software\) "Watson (artificial intelligence software)"), defeated the two greatest _Jeopardy!_ champions, [Brad Rutter](https://en.wikipedia.org/wiki/Brad_Rutter "Brad Rutter") and [Ken Jennings](https://en.wikipedia.org/wiki/Ken_Jennings "Ken Jennings"), by a significant margin.[\[137\]](#cite_note-147) In March 2016, [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo "AlphaGo") won 4 out of 5 games of [Go](https://en.wikipedia.org/wiki/Go_\(game\) "Go (game)") in a match with Go champion [Lee Sedol](https://en.wikipedia.org/wiki/Lee_Sedol "Lee Sedol"), becoming the first [computer Go](https://en.wikipedia.org/wiki/Computer_Go "Computer Go")\-playing system to beat a professional Go player without [handicaps](https://en.wikipedia.org/wiki/Go_handicaps "Go handicaps"). Then in 2017 it [defeated Ke Jie](https://en.wikipedia.org/wiki/AlphaGo_versus_Ke_Jie "AlphaGo versus Ke Jie"), who was the best Go player in the world.[\[138\]](#cite_note-148) Other programs handle [imperfect-information](https://en.wikipedia.org/wiki/Imperfect_information "Imperfect information") games, such as the [poker](https://en.wikipedia.org/wiki/Poker "Poker")\-playing program [Pluribus](https://en.wikipedia.org/wiki/Pluribus_\(poker_bot\) "Pluribus (poker bot)").[\[139\]](#cite_note-149) [DeepMind](https://en.wikipedia.org/wiki/DeepMind "DeepMind") developed increasingly generalistic [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning "Reinforcement learning") models, such as with [MuZero](https://en.wikipedia.org/wiki/MuZero "MuZero"), which could be trained to play chess, Go, or [Atari](https://en.wikipedia.org/wiki/Atari "Atari") games.[\[140\]](#cite_note-150) In 2019, DeepMind's AlphaStar achieved grandmaster level in [StarCraft II](https://en.wikipedia.org/wiki/StarCraft_II "StarCraft II"), a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map.[\[141\]](#cite_note-151) In 2021, an AI agent competed in a PlayStation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.[\[142\]](#cite_note-152)

### Military

Various countries are deploying AI military applications.[\[143\]](#cite_note-:22-153) The main applications enhance [command and control](https://en.wikipedia.org/wiki/Command_and_control "Command and control"), communications, sensors, integration and interoperability.[\[144\]](#cite_note-AI-154) Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and [autonomous vehicles](https://en.wikipedia.org/wiki/Vehicular_automation "Vehicular automation").[\[143\]](#cite_note-:22-153) AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, [target acquisition](https://en.wikipedia.org/wiki/Target_acquisition "Target acquisition"), coordination and deconfliction of distributed [Joint Fires](https://en.wikipedia.org/wiki/Forward_observers_in_the_U.S._military "Forward observers in the U.S. military") between networked combat vehicles involving manned and unmanned teams.[\[144\]](#cite_note-AI-154) AI was incorporated into military operations in Iraq and Syria.[\[143\]](#cite_note-:22-153)

In November 2023, US Vice President [Kamala Harris](https://en.wikipedia.org/wiki/Kamala_Harris "Kamala Harris") disclosed a declaration signed by 31 nations to set guardrails for the military use of AI. The commitments include using legal reviews to ensure the compliance of military AI with international laws, and being cautious and transparent in the development of this technology.[\[145\]](#cite_note-155)

### Generative AI

[![Image 8](https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Vincent_van_Gogh_in_watercolour.png/220px-Vincent_van_Gogh_in_watercolour.png)](https://en.wikipedia.org/wiki/File:Vincent_van_Gogh_in_watercolour.png)

Vincent van Gogh in watercolour created by generative AI software

In the early 2020s, [generative AI](https://en.wikipedia.org/wiki/Generative_AI "Generative AI") gained widespread prominence. In March 2023, 58% of U.S. adults had heard about [ChatGPT](https://en.wikipedia.org/wiki/ChatGPT "ChatGPT") and 14% had tried it.[\[146\]](#cite_note-156) The increasing realism and ease-of-use of AI-based [text-to-image](https://en.wikipedia.org/wiki/Text-to-image_model "Text-to-image model") generators such as [Midjourney](https://en.wikipedia.org/wiki/Midjourney "Midjourney"), [DALL-E](https://en.wikipedia.org/wiki/DALL-E "DALL-E"), and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion "Stable Diffusion") sparked a trend of [viral](https://en.wikipedia.org/wiki/Viral_phenomenon "Viral phenomenon") AI-generated photos. Widespread attention was gained by a fake photo of [Pope Francis](https://en.wikipedia.org/wiki/Pope_Francis "Pope Francis") wearing a white puffer coat, the fictional arrest of [Donald Trump](https://en.wikipedia.org/wiki/Donald_Trump "Donald Trump"), and a hoax of an attack on the [Pentagon](https://en.wikipedia.org/wiki/The_Pentagon "The Pentagon"), as well as the usage in professional creative arts.[\[147\]](#cite_note-157)[\[148\]](#cite_note-158)

### Industry-specific tasks

There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes.[\[149\]](#cite_note-159) A few examples are [energy storage](https://en.wikipedia.org/wiki/Energy_storage "Energy storage"), medical diagnosis, military logistics, applications that predict the result of judicial decisions, [foreign policy](https://en.wikipedia.org/wiki/Foreign_policy "Foreign policy"), or supply chain management.

In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.

Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.

Ethics
------

AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: [Demis Hassabis](https://en.wikipedia.org/wiki/Demis_Hassabis "Demis Hassabis") of [Deep Mind](https://en.wikipedia.org/wiki/DeepMind "DeepMind") hopes to "solve intelligence, and then use that to solve everything else".[\[150\]](#cite_note-FOOTNOTESimonite2016-160) However, as the use of AI has become widespread, several unintended consequences and risks have been identified.[\[151\]](#cite_note-FOOTNOTERussellNorvig2021987-161) In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning.[\[152\]](#cite_note-FOOTNOTELaskowski2023-162)

### Risks and harm

#### Privacy and copyright

Machine-learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about [privacy](https://en.wikipedia.org/wiki/Privacy "Privacy"), [surveillance](https://en.wikipedia.org/wiki/Surveillance "Surveillance") and [copyright](https://en.wikipedia.org/wiki/Copyright "Copyright").

Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio.[\[153\]](#cite_note-FOOTNOTEGAO2022-163) For example, in order to build [speech recognition](https://en.wikipedia.org/wiki/Speech_recognition "Speech recognition") algorithms, [Amazon](https://en.wikipedia.org/wiki/Amazon_\(company\) "Amazon (company)") has recorded millions of private conversations and allowed [temporary workers](https://en.wikipedia.org/wiki/Temporary_worker "Temporary worker") to listen to and transcribe some of them.[\[154\]](#cite_note-FOOTNOTEValinsky2019-164) Opinions about this widespread [surveillance](https://en.wikipedia.org/wiki/Surveillance "Surveillance") range from those who see it as a [necessary evil](https://en.wikipedia.org/wiki/Necessary_evil "Necessary evil") to those for whom it is clearly [unethical](https://en.wikipedia.org/wiki/Unethical "Unethical") and a violation of the [right to privacy](https://en.wikipedia.org/wiki/Right_to_privacy "Right to privacy").[\[155\]](#cite_note-FOOTNOTERussellNorvig2021991-165)

AI developers argue that this is the only way to deliver valuable applications. and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as [data aggregation](https://en.wikipedia.org/wiki/Data_aggregation "Data aggregation"), [de-identification](https://en.wikipedia.org/wiki/De-identification "De-identification") and [differential privacy](https://en.wikipedia.org/wiki/Differential_privacy "Differential privacy").[\[156\]](#cite_note-FOOTNOTERussellNorvig2021991–992-166) Since 2016, some privacy experts, such as [Cynthia Dwork](https://en.wikipedia.org/wiki/Cynthia_Dwork "Cynthia Dwork"), have begun to view privacy in terms of [fairness](https://en.wikipedia.org/wiki/Fairness_\(machine_learning\) "Fairness (machine learning)"). [Brian Christian](https://en.wikipedia.org/wiki/Brian_Christian "Brian Christian") wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."[\[157\]](#cite_note-FOOTNOTEChristian202063-167)

Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "[fair use](https://en.wikipedia.org/wiki/Fair_use "Fair use")". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work".[\[158\]](#cite_note-FOOTNOTEVincent2022-168)[\[159\]](#cite_note-169) Website owners who do not wish to have their content scraped can indicate it in a "[robots.txt](https://en.wikipedia.org/wiki/Robots.txt "Robots.txt")" file.[\[160\]](#cite_note-170) In 2023, leading authors (including [John Grisham](https://en.wikipedia.org/wiki/John_Grisham "John Grisham") and [Jonathan Franzen](https://en.wikipedia.org/wiki/Jonathan_Franzen "Jonathan Franzen")) sued AI companies for using their work to train generative AI.[\[161\]](#cite_note-FOOTNOTEReisner2023-171)[\[162\]](#cite_note-FOOTNOTEAlterHarris2023-172) Another discussed approach is to envision a separate [_sui generis_](https://en.wikipedia.org/wiki/Sui_generis "Sui generis") system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.[\[163\]](#cite_note-173)

#### Misinformation

[YouTube](https://en.wikipedia.org/wiki/YouTube "YouTube"), [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook") and others use [recommender systems](https://en.wikipedia.org/wiki/Recommender_system "Recommender system") to guide users to more content. These AI programs were given the goal of [maximizing](https://en.wikipedia.org/wiki/Mathematical_optimization "Mathematical optimization") user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose [misinformation](https://en.wikipedia.org/wiki/Misinformation "Misinformation"), [conspiracy theories](https://en.wikipedia.org/wiki/Conspiracy_theory "Conspiracy theory"), and extreme [partisan](https://en.wikipedia.org/wiki/Partisan_\(politics\) "Partisan (politics)") content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into [filter bubbles](https://en.wikipedia.org/wiki/Filter_bubbles "Filter bubbles") where they received multiple versions of the same misinformation.[\[164\]](#cite_note-FOOTNOTENicas2018-174) This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government.[\[165\]](#cite_note-175) The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem.

In 2022, [generative AI](https://en.wikipedia.org/wiki/Generative_AI "Generative AI") began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda.[\[166\]](#cite_note-FOOTNOTEWilliams2023-176) AI pioneer [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton "Geoffrey Hinton") expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.[\[167\]](#cite_note-FOOTNOTETaylorHern2023-177)

#### Algorithmic bias and fairness

Machine learning applications will be biased if they learn from biased data.[\[168\]](#cite_note-FOOTNOTERose2023-178) The developers may not be aware that the bias exists.[\[169\]](#cite_note-FOOTNOTECNA2019-179) Bias can be introduced by the way [training data](https://en.wikipedia.org/wiki/Training_data "Training data") is selected and by the way a model is deployed.[\[170\]](#cite_note-FOOTNOTEGoffrey200817-180)[\[168\]](#cite_note-FOOTNOTERose2023-178) If a biased algorithm is used to make decisions that can seriously [harm](https://en.wikipedia.org/wiki/Harm "Harm") people (as it can in [medicine](https://en.wikipedia.org/wiki/Health_equity "Health equity"), [finance](https://en.wikipedia.org/wiki/Credit_rating "Credit rating"), [recruitment](https://en.wikipedia.org/wiki/Recruitment "Recruitment"), [housing](https://en.wikipedia.org/wiki/Public_housing "Public housing") or [policing](https://en.wikipedia.org/wiki/Policing "Policing")) then the algorithm may cause [discrimination](https://en.wikipedia.org/wiki/Discrimination "Discrimination").[\[171\]](#cite_note-181) [Fairness](https://en.wikipedia.org/wiki/Fairness_\(machine_learning\) "Fairness (machine learning)") in machine learning is the study of how to prevent the harm caused by algorithmic bias. It has become serious area of academic study within AI. Researchers have discovered it is not always possible to define "fairness" in a way that satisfies all stakeholders.[\[172\]](#cite_note-182)

On June 28, 2015, [Google Photos](https://en.wikipedia.org/wiki/Google_Photos "Google Photos")'s new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people,[\[173\]](#cite_note-FOOTNOTEChristian202025-183) a problem called "sample size disparity".[\[174\]](#cite_note-FOOTNOTERussellNorvig2021995-184) Google "fixed" this problem by preventing the system from labelling _anything_ as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.[\[175\]](#cite_note-FOOTNOTEGrantHill2023-185)

[COMPAS](https://en.wikipedia.org/wiki/COMPAS_\(software\) "COMPAS (software)") is a commercial program widely used by [U.S. courts](https://en.wikipedia.org/wiki/U.S._court "U.S. court") to assess the likelihood of a [defendant](https://en.wikipedia.org/wiki/Defendant "Defendant") becoming a [recidivist](https://en.wikipedia.org/wiki/Recidivist "Recidivist"). In 2016, [Julia Angwin](https://en.wikipedia.org/wiki/Julia_Angwin "Julia Angwin") at [ProPublica](https://en.wikipedia.org/wiki/ProPublica "ProPublica") discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend.[\[176\]](#cite_note-FOOTNOTELarsonAngwin2016-186) In 2017, several researchers[\[k\]](#cite_note-188) showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.[\[178\]](#cite_note-189)

A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender".[\[179\]](#cite_note-190) Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."[\[180\]](#cite_note-191)

Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as _recommendations_, some of these "recommendations" will likely be racist.[\[181\]](#cite_note-192) Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be _better_ than the past. It is necessarily descriptive and not proscriptive.[\[l\]](#cite_note-194)

Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.[\[174\]](#cite_note-FOOTNOTERussellNorvig2021995-184)

At its 2022 [Conference on Fairness, Accountability, and Transparency](https://en.wikipedia.org/wiki/ACM_Conference_on_Fairness,_Accountability,_and_Transparency "ACM Conference on Fairness, Accountability, and Transparency") (ACM FAccT 2022), the [Association for Computing Machinery](https://en.wikipedia.org/wiki/Association_for_Computing_Machinery "Association for Computing Machinery"), in Seoul, South Korea, presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe, and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.[\[183\]](#cite_note-FOOTNOTEDockrill2022-195)

#### Lack of transparency

[![Image 9](https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/HiPhi_Z%2C_IAA_Summit_2023%2C_Munich_%28P1120237%29.jpg/260px-HiPhi_Z%2C_IAA_Summit_2023%2C_Munich_%28P1120237%29.jpg)](https://en.wikipedia.org/wiki/File:HiPhi_Z,_IAA_Summit_2023,_Munich_\(P1120237\).jpg)

[Lidar](https://en.wikipedia.org/wiki/Lidar "Lidar") testing vehicle for autonomous driving

Many AI systems are so complex that their designers cannot explain how they reach their decisions.[\[184\]](#cite_note-FOOTNOTESample2017-196) Particularly with [deep neural networks](https://en.wikipedia.org/wiki/Deep_neural_networks "Deep neural networks"), in which there are a large amount of non-[linear](https://en.wikipedia.org/wiki/Linear "Linear") relationships between inputs and outputs. But some popular explainability techniques exist.[\[185\]](#cite_note-197)

It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a [ruler](https://en.wikipedia.org/wiki/Ruler "Ruler") as "cancerous", because pictures of malignancies typically include a ruler to show the scale.[\[186\]](#cite_note-FOOTNOTEChristian2020110-198) Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.[\[187\]](#cite_note-FOOTNOTEChristian202088–91-199)

People who have been harmed by an algorithm's decision have a right to an explanation.[\[188\]](#cite_note-200) Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's [General Data Protection Regulation](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation "General Data Protection Regulation") in 2016 included an explicit statement that this right exists.[\[m\]](#cite_note-201) Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.[\[189\]](#cite_note-FOOTNOTEChristian202091-202)

[DARPA](https://en.wikipedia.org/wiki/DARPA "DARPA") established the [XAI](https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence "Explainable Artificial Intelligence") ("Explainable Artificial Intelligence") program in 2014 to try and solve these problems.[\[190\]](#cite_note-FOOTNOTEChristian202083-203)

There are several possible solutions to the transparency problem. SHAP tried to solve the transparency problems by visualising the contribution of each feature to the output.[\[191\]](#cite_note-FOOTNOTEVerma2021-204) LIME can locally approximate a model with a simpler, interpretable model.[\[192\]](#cite_note-FOOTNOTERothman2020-205) [Multitask learning](https://en.wikipedia.org/wiki/Multitask_learning "Multitask learning") provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned.[\[193\]](#cite_note-FOOTNOTEChristian2020105–108-206) [Deconvolution](https://en.wikipedia.org/wiki/Deconvolution "Deconvolution"), [DeepDream](https://en.wikipedia.org/wiki/DeepDream "DeepDream") and other [generative](https://en.wikipedia.org/wiki/Generative_AI "Generative AI") methods can allow developers to see what different layers of a deep network have learned and produce output that can suggest what the network is learning.[\[194\]](#cite_note-FOOTNOTEChristian2020108–112-207)

#### Bad actors and weaponized AI

Artificial intelligence provides a number of tools that are useful to [bad actors](https://en.wikipedia.org/wiki/Bad_actor "Bad actor"), such as [authoritarian governments](https://en.wikipedia.org/wiki/Authoritarian "Authoritarian"), [terrorists](https://en.wikipedia.org/wiki/Terrorist "Terrorist"), [criminals](https://en.wikipedia.org/wiki/Criminals "Criminals") or [rogue states](https://en.wikipedia.org/wiki/Rogue_states "Rogue states").

A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision.[\[n\]](#cite_note-209) Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentially [weapons of mass destruction](https://en.wikipedia.org/wiki/Weapons_of_mass_destruction "Weapons of mass destruction").[\[196\]](#cite_note-FOOTNOTERussellNorvig2021987–990-210) Even when used in conventional warfare, it is unlikely that they will be unable to reliably choose targets and could potentially [kill an innocent person](https://en.wikipedia.org/wiki/Murder "Murder").[\[196\]](#cite_note-FOOTNOTERussellNorvig2021987–990-210) In 2014, 30 nations (including China) supported a ban on autonomous weapons under the [United Nations](https://en.wikipedia.org/wiki/United_Nations "United Nations")' [Convention on Certain Conventional Weapons](https://en.wikipedia.org/wiki/Convention_on_Certain_Conventional_Weapons "Convention on Certain Conventional Weapons"), however the [United States](https://en.wikipedia.org/wiki/United_States "United States") and others disagreed.[\[197\]](#cite_note-FOOTNOTERussellNorvig2021988-211) By 2015, over fifty countries were reported to be researching battlefield robots.[\[198\]](#cite_note-212)

AI tools make it easier for [authoritarian governments](https://en.wikipedia.org/wiki/Authoritarian "Authoritarian") to efficiently control their citizens in several ways. [Face](https://en.wikipedia.org/wiki/Facial_recognition_system "Facial recognition system") and [voice recognition](https://en.wikipedia.org/wiki/Speaker_recognition "Speaker recognition") allow widespread [surveillance](https://en.wikipedia.org/wiki/Surveillance "Surveillance"). [Machine learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning"), operating this data, can [classify](https://en.wikipedia.org/wiki/Classifier_\(machine_learning\) "Classifier (machine learning)") potential enemies of the state and prevent them from hiding. [Recommendation systems](https://en.wikipedia.org/wiki/Recommender_system "Recommender system") can precisely target [propaganda](https://en.wikipedia.org/wiki/Propaganda "Propaganda") and [misinformation](https://en.wikipedia.org/wiki/Misinformation "Misinformation") for maximum effect. [Deepfakes](https://en.wikipedia.org/wiki/Deepfakes "Deepfakes") and [generative AI](https://en.wikipedia.org/wiki/Generative_AI "Generative AI") aid in producing misinformation. Advanced AI can make authoritarian [centralized decision making](https://en.wikipedia.org/wiki/Technocracy "Technocracy") more competitive than liberal and decentralized systems such as [markets](https://en.wikipedia.org/wiki/Market_\(economics\) "Market (economics)"). It lowers the cost and difficulty of [digital warfare](https://en.wikipedia.org/wiki/Digital_warfare "Digital warfare") and [advanced spyware](https://en.wikipedia.org/wiki/Spyware "Spyware").[\[199\]](#cite_note-FOOTNOTEHarari2018-213) All these technologies have been available since 2020 or earlier—AI [facial recognition systems](https://en.wikipedia.org/wiki/Facial_recognition_system "Facial recognition system") are already being used for [mass surveillance](https://en.wikipedia.org/wiki/Mass_surveillance "Mass surveillance") in China.[\[200\]](#cite_note-214)[\[201\]](#cite_note-215)

There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.[\[202\]](#cite_note-FOOTNOTEUrbinaLentzosInvernizziEkins2022-216)

#### Reliance on industry giants

Training AI systems requires an enormous amount of computing power. Usually only [Big Tech](https://en.wikipedia.org/wiki/Big_Tech "Big Tech") companies have the financial resources to make such investments. Smaller startups such as [Cohere](https://en.wikipedia.org/wiki/Cohere "Cohere") and [OpenAI](https://en.wikipedia.org/wiki/OpenAI "OpenAI") end up buying access to [data centers](https://en.wikipedia.org/wiki/Data_centers "Data centers") from [Google](https://en.wikipedia.org/wiki/Google "Google") and [Microsoft](https://en.wikipedia.org/wiki/Microsoft "Microsoft") respectively.[\[203\]](#cite_note-217)

#### Technological unemployment

Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[\[204\]](#cite_note-auto1-218)

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.[\[205\]](#cite_note-219) A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term [unemployment](https://en.wikipedia.org/wiki/Unemployment "Unemployment"), but they generally agree that it could be a net benefit if [productivity](https://en.wikipedia.org/wiki/Productivity "Productivity") gains are [redistributed](https://en.wikipedia.org/wiki/Redistribution_of_income_and_wealth "Redistribution of income and wealth").[\[206\]](#cite_note-FOOTNOTEIGM_Chicago2017-220) Risk estimates vary; for example, in the 2010s, Michael Osborne and [Carl Benedikt Frey](https://en.wikipedia.org/wiki/Carl_Benedikt_Frey "Carl Benedikt Frey") estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk".[\[o\]](#cite_note-222)[\[208\]](#cite_note-223) The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies.[\[204\]](#cite_note-auto1-218) In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.[\[209\]](#cite_note-224)[\[210\]](#cite_note-225)

Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; _[The Economist](https://en.wikipedia.org/wiki/The_Economist "The Economist")_ stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously".[\[211\]](#cite_note-FOOTNOTEMorgenstern2015-226) Jobs at extreme risk range from [paralegals](https://en.wikipedia.org/wiki/Paralegal "Paralegal") to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.[\[212\]](#cite_note-227)

From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by [Joseph Weizenbaum](https://en.wikipedia.org/wiki/Joseph_Weizenbaum "Joseph Weizenbaum"), about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.[\[213\]](#cite_note-228)

#### Existential risk

It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist [Stephen Hawking](https://en.wikipedia.org/wiki/Stephen_Hawking "Stephen Hawking") stated, "[spell the end of the human race](https://en.wikipedia.org/wiki/Global_catastrophic_risk "Global catastrophic risk")".[\[214\]](#cite_note-FOOTNOTECellan-Jones2014-229) This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character.[\[p\]](#cite_note-231) These sci-fi scenarios are misleading in several ways.

First, AI does not require human-like "[sentience](https://en.wikipedia.org/wiki/Sentience "Sentience")" to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher [Nick Bostrom](https://en.wikipedia.org/wiki/Nick_Bostrom "Nick Bostrom") argued that if one gives _almost any_ goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a [paperclip factory manager](https://en.wikipedia.org/wiki/Instrumental_convergence#Paperclip_maximizer "Instrumental convergence")).[\[216\]](#cite_note-FOOTNOTEBostrom2014-232) [Stuart Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead."[\[217\]](#cite_note-FOOTNOTERussell2019-233) In order to be safe for humanity, a [superintelligence](https://en.wikipedia.org/wiki/Superintelligence "Superintelligence") would have to be genuinely [aligned](https://en.wikipedia.org/wiki/AI_alignment "AI alignment") with humanity's morality and values so that it is "fundamentally on our side".[\[218\]](#cite_note-234)

Second, [Yuval Noah Harari](https://en.wikipedia.org/wiki/Yuval_Noah_Harari "Yuval Noah Harari") argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like [ideologies](https://en.wikipedia.org/wiki/Ideology "Ideology"), [law](https://en.wikipedia.org/wiki/Law "Law"), [government](https://en.wikipedia.org/wiki/Government "Government"), [money](https://en.wikipedia.org/wiki/Money "Money") and the [economy](https://en.wikipedia.org/wiki/Economy "Economy") are made of [language](https://en.wikipedia.org/wiki/Language "Language"); they exist because there are stories that billions of people believe. The current prevalence of [misinformation](https://en.wikipedia.org/wiki/Misinformation "Misinformation") suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.[\[219\]](#cite_note-FOOTNOTEHarari2023-235)

The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.[\[220\]](#cite_note-FOOTNOTEMüllerBostrom2014-236) Personalities such as [Stephen Hawking](https://en.wikipedia.org/wiki/Stephen_Hawking "Stephen Hawking"), [Bill Gates](https://en.wikipedia.org/wiki/Bill_Gates "Bill Gates"), and [Elon Musk](https://en.wikipedia.org/wiki/Elon_Musk "Elon Musk") have expressed concern about existential risk from AI.[\[221\]](#cite_note-237) AI pioneers including [Fei-Fei Li](https://en.wikipedia.org/wiki/Fei-Fei_Li "Fei-Fei Li"), [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton "Geoffrey Hinton"), [Yoshua Bengio](https://en.wikipedia.org/wiki/Yoshua_Bengio "Yoshua Bengio"), [Cynthia Breazeal](https://en.wikipedia.org/wiki/Cynthia_Breazeal "Cynthia Breazeal"), [Rana el Kaliouby](https://en.wikipedia.org/wiki/Rana_el_Kaliouby "Rana el Kaliouby"), [Demis Hassabis](https://en.wikipedia.org/wiki/Demis_Hassabis "Demis Hassabis"), [Joy Buolamwini](https://en.wikipedia.org/wiki/Joy_Buolamwini "Joy Buolamwini"), and [Sam Altman](https://en.wikipedia.org/wiki/Sam_Altman "Sam Altman") have expressed concerns about the risks of AI. In 2023, many leading AI experts issued the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".[\[222\]](#cite_note-FOOTNOTEValance2023-238)

Other researchers, however, spoke in favor of a less dystopian view. AI pioneer [Juergen Schmidhuber](https://en.wikipedia.org/wiki/Juergen_Schmidhuber "Juergen Schmidhuber") did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier."[\[223\]](#cite_note-guardian2023-239) While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors."[\[224\]](#cite_note-foxnews2023-240)[\[225\]](#cite_note-forbes2023-241) [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng "Andrew Ng") also argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests."[\[226\]](#cite_note-andrewng2023-242) [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun "Yann LeCun") "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."[\[227\]](#cite_note-lecun2023-243) In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine.[\[228\]](#cite_note-244) However, after 2016, the study of current and future risks and possible solutions became a serious area of research.[\[229\]](#cite_note-FOOTNOTEChristian202067,_73-245)

### Ethical machines and alignment

Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. [Eliezer Yudkowsky](https://en.wikipedia.org/wiki/Eliezer_Yudkowsky "Eliezer Yudkowsky"), who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk.[\[230\]](#cite_note-FOOTNOTEYudkowsky2008-246)

Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas.[\[231\]](#cite_note-FOOTNOTEAndersonAnderson2011-247) The field of machine ethics is also called computational morality,[\[231\]](#cite_note-FOOTNOTEAndersonAnderson2011-247) and was founded at an [AAAI](https://en.wikipedia.org/wiki/AAAI "AAAI") symposium in 2005.[\[232\]](#cite_note-FOOTNOTEAAAI2014-248)

Other approaches include [Wendell Wallach](https://en.wikipedia.org/wiki/Wendell_Wallach "Wendell Wallach")'s "artificial moral agents"[\[233\]](#cite_note-FOOTNOTEWallach2010-249) and [Stuart J. Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell")'s [three principles](https://en.wikipedia.org/wiki/Human_Compatible#Russell's_three_principles "Human Compatible") for developing provably beneficial machines.[\[234\]](#cite_note-FOOTNOTERussell2019173-250)

### Open source

Active organizations in the AI open-source community include [Hugging Face](https://en.wikipedia.org/wiki/Hugging_Face "Hugging Face"),[\[235\]](#cite_note-251) [Google](https://en.wikipedia.org/wiki/Google "Google"),[\[236\]](#cite_note-252) [EleutherAI](https://en.wikipedia.org/wiki/EleutherAI "EleutherAI") and [Meta](https://en.wikipedia.org/wiki/Meta_Platforms "Meta Platforms").[\[237\]](#cite_note-253) Various AI models, such as [Llama 2](https://en.wikipedia.org/wiki/LLaMA "LLaMA"), [Mistral](https://en.wikipedia.org/wiki/Mistral_AI "Mistral AI") or [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion "Stable Diffusion"), have been made open-weight,[\[238\]](#cite_note-254)[\[239\]](#cite_note-255) meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely [fine-tuned](https://en.wikipedia.org/wiki/Fine-tuning_\(deep_learning\) "Fine-tuning (deep learning)"), which allows companies to specialize them with their own data and for their own use-case.[\[240\]](#cite_note-256) Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate [bioterrorism](https://en.wikipedia.org/wiki/Bioterrorism "Bioterrorism")), and that once released on the Internet, they can't be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.[\[241\]](#cite_note-257)

### Frameworks

Artificial Intelligence projects can have their ethical permissibility tested while designing, developing, and implementing an AI system. An AI framework such as the Care and Act Framework containing the SUM values—developed by the [Alan Turing Institute](https://en.wikipedia.org/wiki/Alan_Turing_Institute "Alan Turing Institute") tests projects in four main areas:[\[242\]](#cite_note-258)[\[243\]](#cite_note-259)

*   RESPECT the dignity of individual people
*   CONNECT with other people sincerely, openly and inclusively
*   CARE for the wellbeing of everyone
*   PROTECT social values, justice and the public interest

Other developments in ethical frameworks include those decided upon during the [Asilomar Conference](https://en.wikipedia.org/wiki/Asilomar_Conference_on_Beneficial_AI "Asilomar Conference on Beneficial AI"), the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others;[\[244\]](#cite_note-260) however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks.[\[245\]](#cite_note-261)

Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers.[\[246\]](#cite_note-262)

The AI Safety Institute in the UK has released a testing toolset called ‘Inspect’ for AI safety evaluations available under a MIT open-source licence which is freely available on Github and can be improved with third-party packages. It can be used to evaluate AI models in a range of areas including core knowledge, ability to reason, and autonomous capabilities.[\[247\]](#cite_note-263)

### Regulation

[![Image 10: AI Safety Summit](https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/Vice_President_Harris_at_the_group_photo_of_the_2023_AI_Safety_Summit.jpg/260px-Vice_President_Harris_at_the_group_photo_of_the_2023_AI_Safety_Summit.jpg)](https://en.wikipedia.org/wiki/File:Vice_President_Harris_at_the_group_photo_of_the_2023_AI_Safety_Summit.jpg)

The first global [AI Safety Summit](https://en.wikipedia.org/wiki/AI_Safety_Summit "AI Safety Summit") was held in 2023 with a declaration calling for international co-operation.

The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms.[\[248\]](#cite_note-264) The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.[\[249\]](#cite_note-FOOTNOTELaw_Library_of_Congress_\(U.S.\)._Global_Legal_Research_Directorate2019-265) According to AI Index at [Stanford](https://en.wikipedia.org/wiki/Stanford "Stanford"), the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone.[\[250\]](#cite_note-FOOTNOTEVincent2023-266)[\[251\]](#cite_note-FOOTNOTEStanford_University2023-267) Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.[\[252\]](#cite_note-FOOTNOTEUNESCO2021-268) Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia.[\[252\]](#cite_note-FOOTNOTEUNESCO2021-268) The [Global Partnership on Artificial Intelligence](https://en.wikipedia.org/wiki/Global_Partnership_on_Artificial_Intelligence "Global Partnership on Artificial Intelligence") was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology.[\[252\]](#cite_note-FOOTNOTEUNESCO2021-268) [Henry Kissinger](https://en.wikipedia.org/wiki/Henry_Kissinger "Henry Kissinger"), [Eric Schmidt](https://en.wikipedia.org/wiki/Eric_Schmidt "Eric Schmidt"), and [Daniel Huttenlocher](https://en.wikipedia.org/wiki/Daniel_P._Huttenlocher "Daniel P. Huttenlocher") published a joint statement in November 2021 calling for a government commission to regulate AI.[\[253\]](#cite_note-FOOTNOTEKissinger2021-269) In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.[\[254\]](#cite_note-FOOTNOTEAltmanBrockmanSutskever2023-270) In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics.[\[255\]](#cite_note-271)

In a 2022 [Ipsos](https://en.wikipedia.org/wiki/Ipsos "Ipsos") survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks".[\[250\]](#cite_note-FOOTNOTEVincent2023-266) A 2023 [Reuters](https://en.wikipedia.org/wiki/Reuters "Reuters")/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.[\[256\]](#cite_note-FOOTNOTEEdwards2023-272) In a 2023 [Fox News](https://en.wikipedia.org/wiki/Fox_News "Fox News") poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".[\[257\]](#cite_note-FOOTNOTEKasperowicz2023-273)[\[258\]](#cite_note-FOOTNOTEFox_News2023-274)

In November 2023, the first global [AI Safety Summit](https://en.wikipedia.org/wiki/2023_AI_Safety_Summit "2023 AI Safety Summit") was held in [Bletchley Park](https://en.wikipedia.org/wiki/Bletchley_Park "Bletchley Park") in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks.[\[259\]](#cite_note-275) 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence.[\[260\]](#cite_note-2023-11-01-bletchley-declaration-full-276)[\[261\]](#cite_note-277)

History
-------

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing "Alan Turing")'s [theory of computation](https://en.wikipedia.org/wiki/Theory_of_computation "Theory of computation"), which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning.[\[262\]](#cite_note-FOOTNOTERussellNorvig20219-278)[\[5\]](#cite_note-turing-5) This, along with concurrent discoveries in [cybernetics](https://en.wikipedia.org/wiki/Cybernetics "Cybernetics"), [information theory](https://en.wikipedia.org/wiki/Information_theory "Information theory") and [neurobiology](https://en.wikipedia.org/wiki/Neurobiology "Neurobiology"), led researchers to consider the possibility of building an "electronic brain".[\[q\]](#cite_note-280) They developed several areas of research that would become part of AI,[\[264\]](#cite_note-281) such as [McCullouch](https://en.wikipedia.org/wiki/Warren_McCullouch "Warren McCullouch") and [Pitts](https://en.wikipedia.org/wiki/Walter_Pitts "Walter Pitts") design for "artificial neurons" in 1943,[\[265\]](#cite_note-FOOTNOTERussellNorvig202117-282) and Turing's influential 1950 paper '[Computing Machinery and Intelligence](https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence "Computing Machinery and Intelligence")', which introduced the [Turing test](https://en.wikipedia.org/wiki/Turing_test "Turing test") and showed that "machine intelligence" was plausible.[\[266\]](#cite_note-Turing_test-283)[\[5\]](#cite_note-turing-5)

The field of AI research was founded at [a workshop](https://en.wikipedia.org/wiki/Dartmouth_workshop "Dartmouth workshop") at [Dartmouth College](https://en.wikipedia.org/wiki/Dartmouth_College "Dartmouth College") in 1956.[\[r\]](#cite_note-285)[\[6\]](#cite_note-Dartmouth_workshop-6) The attendees became the leaders of AI research in the 1960s.[\[s\]](#cite_note-287) They and their students produced programs that the press described as "astonishing":[\[t\]](#cite_note-289) computers were learning [checkers](https://en.wikipedia.org/wiki/Draughts "Draughts") strategies, solving word problems in algebra, proving [logical theorems](https://en.wikipedia.org/wiki/Theorem "Theorem") and speaking English.[\[u\]](#cite_note-290)[\[7\]](#cite_note-AI_in_the_60s-7) Artificial intelligence laboratories were set up at a number of British and U.S. Universities in the latter 1950s and early 1960s.[\[5\]](#cite_note-turing-5)

Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with [general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence") and considered this the goal of their field.[\[270\]](#cite_note-FOOTNOTENewquist199486–86-291) [Herbert Simon](https://en.wikipedia.org/wiki/Herbert_A._Simon "Herbert A. Simon") predicted, "machines will be capable, within twenty years, of doing any work a man can do".[\[271\]](#cite_note-292) [Marvin Minsky](https://en.wikipedia.org/wiki/Marvin_Minsky "Marvin Minsky") agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".[\[272\]](#cite_note-293) They had, however, underestimated the difficulty of the problem.[\[v\]](#cite_note-295) In 1974, both the U.S. and British governments cut off exploratory research in response to the [criticism](https://en.wikipedia.org/wiki/Lighthill_report "Lighthill report") of [Sir James Lighthill](https://en.wikipedia.org/wiki/Sir_James_Lighthill "Sir James Lighthill")[\[274\]](#cite_note-FOOTNOTELighthill1973-296) and ongoing pressure from the U.S. Congress to [fund more productive projects](https://en.wikipedia.org/wiki/Mansfield_Amendment "Mansfield Amendment").[\[275\]](#cite_note-FOOTNOTENRC1999212–213-297) [Minsky](https://en.wikipedia.org/wiki/Marvin_Minsky "Marvin Minsky")'s and [Papert](https://en.wikipedia.org/wiki/Seymour_Papert "Seymour Papert")'s book _[Perceptrons](https://en.wikipedia.org/wiki/Perceptron "Perceptron")_ was understood as proving that [artificial neural networks](https://en.wikipedia.org/wiki/Artificial_neural_networks "Artificial neural networks") would never be useful for solving real-world tasks, thus discrediting the approach altogether.[\[276\]](#cite_note-FOOTNOTERussellNorvig202122-298) The "[AI winter](https://en.wikipedia.org/wiki/AI_winter "AI winter")", a period when obtaining funding for AI projects was difficult, followed.[\[9\]](#cite_note-First_AI_winter-9)

In the early 1980s, AI research was revived by the commercial success of [expert systems](https://en.wikipedia.org/wiki/Expert_system "Expert system"),[\[277\]](#cite_note-299) a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's [fifth generation computer](https://en.wikipedia.org/wiki/Fifth_generation_computer "Fifth generation computer") project inspired the U.S. and British governments to restore funding for [academic research](https://en.wikipedia.org/wiki/Academic_research "Academic research").[\[8\]](#cite_note-AI_in_the_80s-8) However, beginning with the collapse of the [Lisp Machine](https://en.wikipedia.org/wiki/Lisp_Machine "Lisp Machine") market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.[\[10\]](#cite_note-Second_AI_winter-10)

Up to this point, most of AI's funding had gone to projects that used high-level [symbols](https://en.wikipedia.org/wiki/Symbolic_AI "Symbolic AI") to represent [mental objects](https://en.wikipedia.org/wiki/Mental_objects "Mental objects") like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially [perception](https://en.wikipedia.org/wiki/Machine_perception "Machine perception"), [robotics](https://en.wikipedia.org/wiki/Robotics "Robotics"), [learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning") and [pattern recognition](https://en.wikipedia.org/wiki/Pattern_recognition "Pattern recognition"),[\[278\]](#cite_note-FOOTNOTERussellNorvig202124-300) and began to look into "sub-symbolic" approaches.[\[279\]](#cite_note-FOOTNOTENilsson19987-301) [Rodney Brooks](https://en.wikipedia.org/wiki/Rodney_Brooks "Rodney Brooks") rejected "representation" in general and focussed directly on engineering machines that move and survive.[\[w\]](#cite_note-306) [Judea Pearl](https://en.wikipedia.org/wiki/Judea_Pearl "Judea Pearl"), [Lofti Zadeh](https://en.wikipedia.org/wiki/Lofti_Zadeh "Lofti Zadeh") and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.[\[88\]](#cite_note-Uncertain_reasoning-94)[\[284\]](#cite_note-FOOTNOTERussellNorvig202125-307) But the most important development was the revival of "[connectionism](https://en.wikipedia.org/wiki/Connectionism "Connectionism")", including neural network research, by [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton "Geoffrey Hinton") and others.[\[285\]](#cite_note-308) In 1990, [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun "Yann LeCun") successfully showed that [convolutional neural networks](https://en.wikipedia.org/wiki/Convolutional_neural_networks "Convolutional neural networks") can recognize handwritten digits, the first of many successful applications of neural networks.[\[286\]](#cite_note-FOOTNOTERussellNorvig202126-309)

AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "[narrow](https://en.wikipedia.org/wiki/Narrow_AI "Narrow AI")" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as [statistics](https://en.wikipedia.org/wiki/Statistics "Statistics"), [economics](https://en.wikipedia.org/wiki/Economics "Economics") and [mathematics](https://en.wikipedia.org/wiki/Mathematical_optimization "Mathematical optimization")).[\[287\]](#cite_note-AI_1990s-310) By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[\[288\]](#cite_note-AI_widely_used_1990s-311) However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of [artificial general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence") (or "AGI"), which had several well-funded institutions by the 2010s.[\[14\]](#cite_note-AGI-15)

[Deep learning](https://en.wikipedia.org/wiki/Deep_learning "Deep learning") began to dominate industry benchmarks in 2012 and was adopted throughout the field.[\[11\]](#cite_note-Deep_learning_revolution-11) For many specific tasks, other methods were abandoned.[\[x\]](#cite_note-313) Deep learning's success was based on both hardware improvements ([faster computers](https://en.wikipedia.org/wiki/Moore%27s_law "Moore's law"),[\[290\]](#cite_note-Moore's_Law-314) [graphics processing units](https://en.wikipedia.org/wiki/Graphics_processing_unit "Graphics processing unit"), [cloud computing](https://en.wikipedia.org/wiki/Cloud_computing "Cloud computing")[\[291\]](#cite_note-FOOTNOTEClark2015b-315)) and access to [large amounts of data](https://en.wikipedia.org/wiki/Big_data "Big data")[\[292\]](#cite_note-Big_data-316) (including curated datasets,[\[291\]](#cite_note-FOOTNOTEClark2015b-315) such as [ImageNet](https://en.wikipedia.org/wiki/ImageNet "ImageNet")). Deep learning's success led to an enormous increase in interest and funding in AI.[\[y\]](#cite_note-317) The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.[\[252\]](#cite_note-FOOTNOTEUNESCO2021-268)

In 2016, issues of [fairness](https://en.wikipedia.org/wiki/Algorithmic_fairness "Algorithmic fairness") and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The [alignment problem](https://en.wikipedia.org/wiki/AI_alignment "AI alignment") became a serious field of academic study.[\[229\]](#cite_note-FOOTNOTEChristian202067,_73-245)

In the late teens and early 2020s, [AGI](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence") companies began to deliver programs that created enormous interest. In 2015, [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo "AlphaGo"), developed by [DeepMind](https://en.wikipedia.org/wiki/DeepMind "DeepMind"), beat the world champion [Go player](https://en.wikipedia.org/wiki/Go_player "Go player"). The program was taught only the rules of the game and developed strategy by itself. [GPT-3](https://en.wikipedia.org/wiki/GPT-3 "GPT-3") is a [large language model](https://en.wikipedia.org/wiki/Large_language_model "Large language model") that was released in 2020 by [OpenAI](https://en.wikipedia.org/wiki/OpenAI "OpenAI") and is capable of generating high-quality human-like text.[\[293\]](#cite_note-318) These programs, and others, inspired an aggressive [AI boom](https://en.wikipedia.org/wiki/AI_boom "AI boom"), where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI".[\[294\]](#cite_note-FOOTNOTEDiFeliciantonio2023-319) About 800,000 "AI"-related U.S. job openings existed in 2022.[\[295\]](#cite_note-FOOTNOTEGoswami2023-320)

Philosophy
----------

### Defining artificial intelligence

[Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing "Alan Turing") wrote in 1950 "I propose to consider the question 'can machines think'?"[\[296\]](#cite_note-FOOTNOTETuring19501-321) He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".[\[296\]](#cite_note-FOOTNOTETuring19501-321) He devised the Turing test, which measures the ability of a machine to simulate human conversation.[\[266\]](#cite_note-Turing_test-283) Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that [we can not determine these things about other people](https://en.wikipedia.org/wiki/Problem_of_other_minds "Problem of other minds") but "it is usual to have a polite convention that everyone thinks"[\[297\]](#cite_note-FOOTNOTETuring1950Under_"The_Argument_from_Consciousness"-322)

[Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") agree with Turing that intelligence must be defined in terms of external behavior, not internal structure.[\[1\]](#cite_note-FOOTNOTERussellNorvig20211–4-1) However, they are critical that the test requires the machine to imitate humans. "[Aeronautical engineering](https://en.wikipedia.org/wiki/Aeronautics "Aeronautics") texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like [pigeons](https://en.wikipedia.org/wiki/Pigeon "Pigeon") that they can fool other pigeons.'"[\[298\]](#cite_note-FOOTNOTERussellNorvig20213-323) AI founder [John McCarthy](https://en.wikipedia.org/wiki/John_McCarthy_\(computer_scientist\) "John McCarthy (computer scientist)") agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".[\[299\]](#cite_note-FOOTNOTEMaker2006-324)

McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world".[\[300\]](#cite_note-FOOTNOTEMcCarthy1999-325) Another AI founder, [Marvin Minsky](https://en.wikipedia.org/wiki/Marvin_Minsky "Marvin Minsky") similarly describes it as "the ability to solve hard problems".[\[301\]](#cite_note-FOOTNOTEMinsky1986-326) The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals.[\[1\]](#cite_note-FOOTNOTERussellNorvig20211–4-1) These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.

Another definition has been adopted by Google,[\[302\]](#cite_note-327) a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.

### Evaluating approaches to AI

No established unifying theory or [paradigm](https://en.wikipedia.org/wiki/Paradigm "Paradigm") has guided AI research for most of its history.[\[z\]](#cite_note-329) The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly [sub-symbolic](https://en.wikipedia.org/wiki/Sub-symbolic "Sub-symbolic"), [soft](https://en.wikipedia.org/wiki/Soft_computing "Soft computing") and [narrow](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence"). Critics argue that these questions may have to be revisited by future generations of AI researchers.

#### Symbolic AI and its limits

[Symbolic AI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence "Symbolic artificial intelligence") (or "[GOFAI](https://en.wikipedia.org/wiki/GOFAI "GOFAI")")[\[304\]](#cite_note-FOOTNOTEHaugeland1985112–117-330) simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the [physical symbol systems hypothesis](https://en.wikipedia.org/wiki/Physical_symbol_systems_hypothesis "Physical symbol systems hypothesis"): "A physical symbol system has the necessary and sufficient means of general intelligent action."[\[305\]](#cite_note-Physical_symbol_system_hypothesis-331)

However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. [Moravec's paradox](https://en.wikipedia.org/wiki/Moravec%27s_paradox "Moravec's paradox") is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.[\[306\]](#cite_note-332) Philosopher [Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus "Hubert Dreyfus") had [argued](https://en.wikipedia.org/wiki/Dreyfus%27_critique_of_AI "Dreyfus' critique of AI") since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.[\[307\]](#cite_note-Dreyfus'_critique-333) Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.[\[aa\]](#cite_note-335)[\[19\]](#cite_note-Psychological_evidence_of_sub-symbolic_reasoning-21)

The issue is not resolved: [sub-symbolic](https://en.wikipedia.org/wiki/Sub-symbolic "Sub-symbolic") reasoning can make many of the same inscrutable mistakes that human intuition does, such as [algorithmic bias](https://en.wikipedia.org/wiki/Algorithmic_bias "Algorithmic bias"). Critics such as [Noam Chomsky](https://en.wikipedia.org/wiki/Noam_Chomsky "Noam Chomsky") argue continuing research into symbolic AI will still be necessary to attain general intelligence,[\[309\]](#cite_note-FOOTNOTELangley2011-336)[\[310\]](#cite_note-FOOTNOTEKatz2012-337) in part because sub-symbolic AI is a move away from [explainable AI](https://en.wikipedia.org/wiki/Explainable_AI "Explainable AI"): it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of [neuro-symbolic artificial intelligence](https://en.wikipedia.org/wiki/Neuro-symbolic_AI "Neuro-symbolic AI") attempts to bridge the two approaches.

#### Neat vs. scruffy

"Neats" hope that intelligent behavior is described using simple, elegant principles (such as [logic](https://en.wikipedia.org/wiki/Logic "Logic"), [optimization](https://en.wikipedia.org/wiki/Optimization_\(mathematics\) "Optimization (mathematics)"), or [neural networks](https://en.wikipedia.org/wiki/Artificial_neural_network "Artificial neural network")). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s,[\[311\]](#cite_note-Neats_vs._scruffies-338) but eventually was seen as irrelevant. Modern AI has elements of both.

#### Soft vs. hard computing

Finding a provably correct or optimal solution is [intractable](https://en.wikipedia.org/wiki/Intractability_\(complexity\) "Intractability (complexity)") for many important problems.[\[18\]](#cite_note-Intractability-20) Soft computing is a set of techniques, including [genetic algorithms](https://en.wikipedia.org/wiki/Genetic_algorithms "Genetic algorithms"), [fuzzy logic](https://en.wikipedia.org/wiki/Fuzzy_logic "Fuzzy logic") and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks.

#### Narrow vs. general AI

AI researchers are divided as to whether to pursue the goals of artificial general intelligence and [superintelligence](https://en.wikipedia.org/wiki/Superintelligence "Superintelligence") directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals.[\[312\]](#cite_note-FOOTNOTEPennachinGoertzel2007-339)[\[313\]](#cite_note-FOOTNOTERoberts2016-340) General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The experimental sub-field of artificial general intelligence studies this area exclusively.

### Machine consciousness, sentience, and mind

The [philosophy of mind](https://en.wikipedia.org/wiki/Philosophy_of_mind "Philosophy of mind") does not know whether a machine can have a [mind](https://en.wikipedia.org/wiki/Mind "Mind"), [consciousness](https://en.wikipedia.org/wiki/Consciousness "Consciousness") and [mental states](https://en.wikipedia.org/wiki/Philosophy_of_mind "Philosophy of mind"), in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. [Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") add that "\[t\]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on."[\[314\]](#cite_note-FOOTNOTERussellNorvig2021986-341) However, the question has become central to the philosophy of mind. It is also typically the central question at issue in [artificial intelligence in fiction](https://en.wikipedia.org/wiki/Artificial_intelligence_in_fiction "Artificial intelligence in fiction").

#### Consciousness

[David Chalmers](https://en.wikipedia.org/wiki/David_Chalmers "David Chalmers") identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.[\[315\]](#cite_note-FOOTNOTEChalmers1995-342) The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this _feels_ or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). While human [information processing](https://en.wikipedia.org/wiki/Information_processing_\(psychology\) "Information processing (psychology)") is easy to explain, human [subjective experience](https://en.wikipedia.org/wiki/Subjective_experience "Subjective experience") is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to _know what red looks like_.[\[316\]](#cite_note-FOOTNOTEDennett1991-343)

#### Computationalism and functionalism

Computationalism is the position in the [philosophy of mind](https://en.wikipedia.org/wiki/Philosophy_of_mind "Philosophy of mind") that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the [mind–body problem](https://en.wikipedia.org/wiki/Mind%E2%80%93body_problem "Mind–body problem"). This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers [Jerry Fodor](https://en.wikipedia.org/wiki/Jerry_Fodor "Jerry Fodor") and [Hilary Putnam](https://en.wikipedia.org/wiki/Hilary_Putnam "Hilary Putnam").[\[317\]](#cite_note-FOOTNOTEHorst2005-344)

Philosopher [John Searle](https://en.wikipedia.org/wiki/John_Searle "John Searle") characterized this position as "[strong AI](https://en.wikipedia.org/wiki/Strong_AI_hypothesis "Strong AI hypothesis")": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[\[ab\]](#cite_note-Searle's_strong_AI-348) Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind.[\[321\]](#cite_note-Chinese_room-349)

#### AI welfare and rights

It is difficult or impossible to reliably evaluate whether an advanced [AI is sentient](https://en.wikipedia.org/wiki/Sentient_AI "Sentient AI") (has the ability to feel), and if so, to what degree.[\[322\]](#cite_note-350) But if there is a significant chance that a given machine can feel and suffer, then it may be entitled to certain rights or welfare protection measures, similarly to animals.[\[323\]](#cite_note-:02-351)[\[324\]](#cite_note-:12-352) [Sapience](https://en.wikipedia.org/wiki/Sapience "Sapience") (a set of capacities related to high intelligence, such as discernment or [self-awareness](https://en.wikipedia.org/wiki/Self-awareness "Self-awareness")) may provide another moral basis for AI rights.[\[323\]](#cite_note-:02-351) [Robot rights](https://en.wikipedia.org/wiki/Robot_rights "Robot rights") are also sometimes proposed as a practical way to integrate autonomous agents into society.[\[325\]](#cite_note-353)

In 2017, the European Union considered granting "electronic personhood" to some of the most capable AI systems. Similarly to the legal status of companies, it would have conferred rights but also responsibilities.[\[326\]](#cite_note-354) Critics argued in 2018 that granting rights to AI systems would downplay the importance of [human rights](https://en.wikipedia.org/wiki/Human_rights "Human rights"), and that legislation should focus on user needs rather than speculative futuristic scenarios. They also noted that robots lacked the autonomy to take part to society on their own.[\[327\]](#cite_note-355)[\[328\]](#cite_note-356)

Progress in AI increased interest in the topic. Proponents of AI welfare and rights often argue that AI sentience, if it emerges, would be particularly easy to deny. They warn that this may be a [moral blind spot](https://en.wikipedia.org/wiki/Moral_blindness "Moral blindness") analogous to [slavery](https://en.wikipedia.org/wiki/Slavery "Slavery") or [factory farming](https://en.wikipedia.org/wiki/Factory_farming "Factory farming"), which could lead to [large-scale suffering](https://en.wikipedia.org/wiki/Suffering_risks "Suffering risks") if sentient AI is created and carelessly exploited.[\[324\]](#cite_note-:12-352)[\[323\]](#cite_note-:02-351)

Future
------

### Superintelligence and the singularity

A [superintelligence](https://en.wikipedia.org/wiki/Superintelligence "Superintelligence") is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.[\[313\]](#cite_note-FOOTNOTERoberts2016-340)

If research into [artificial general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence") produced sufficiently intelligent software, it might be able to [reprogram and improve itself](https://en.wikipedia.org/wiki/Recursive_self-improvement "Recursive self-improvement"). The improved software would be even better at improving itself, leading to what [I. J. Good](https://en.wikipedia.org/wiki/I._J._Good "I. J. Good") called an "[intelligence explosion](https://en.wikipedia.org/wiki/Intelligence_explosion "Intelligence explosion")" and [Vernor Vinge](https://en.wikipedia.org/wiki/Vernor_Vinge "Vernor Vinge") called a "[singularity](https://en.wikipedia.org/wiki/Technological_singularity "Technological singularity")".[\[329\]](#cite_note-Singularity-357)

However, technologies cannot improve exponentially indefinitely, and typically follow an [S-shaped curve](https://en.wikipedia.org/wiki/S-shaped_curve "S-shaped curve"), slowing when they reach the physical limits of what the technology can do.[\[330\]](#cite_note-FOOTNOTERussellNorvig20211005-358)

### Transhumanism

Robot designer [Hans Moravec](https://en.wikipedia.org/wiki/Hans_Moravec "Hans Moravec"), cyberneticist [Kevin Warwick](https://en.wikipedia.org/wiki/Kevin_Warwick "Kevin Warwick"), and inventor [Ray Kurzweil](https://en.wikipedia.org/wiki/Ray_Kurzweil "Ray Kurzweil") have predicted that humans and machines will merge in the future into [cyborgs](https://en.wikipedia.org/wiki/Cyborg "Cyborg") that are more capable and powerful than either. This idea, called transhumanism, has roots in [Aldous Huxley](https://en.wikipedia.org/wiki/Aldous_Huxley "Aldous Huxley") and [Robert Ettinger](https://en.wikipedia.org/wiki/Robert_Ettinger "Robert Ettinger").[\[331\]](#cite_note-359)

[Edward Fredkin](https://en.wikipedia.org/wiki/Edward_Fredkin "Edward Fredkin") argues that "artificial intelligence is the next stage in evolution", an idea first proposed by [Samuel Butler](https://en.wikipedia.org/wiki/Samuel_Butler_\(novelist\) "Samuel Butler (novelist)")'s "[Darwin among the Machines](https://en.wikipedia.org/wiki/Darwin_among_the_Machines "Darwin among the Machines")" as far back as 1863, and expanded upon by [George Dyson](https://en.wikipedia.org/wiki/George_Dyson_\(science_historian\) "George Dyson (science historian)") in his book of the same name in 1998.[\[332\]](#cite_note-360)

In fiction
----------

[![Image 11](https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Capek_play.jpg/260px-Capek_play.jpg)](https://en.wikipedia.org/wiki/File:Capek_play.jpg)

The word "robot" itself was coined by [Karel Čapek](https://en.wikipedia.org/wiki/Karel_%C4%8Capek "Karel Čapek") in his 1921 play _[R.U.R.](https://en.wikipedia.org/wiki/R.U.R. "R.U.R.")_, the title standing for "Rossum's Universal Robots".

Thought-capable artificial beings have appeared as storytelling devices since antiquity,[\[333\]](#cite_note-AI_in_myth-361) and have been a persistent theme in [science fiction](https://en.wikipedia.org/wiki/Science_fiction "Science fiction").[\[334\]](#cite_note-FOOTNOTEMcCorduck2004340–400-362)

A common [trope](https://en.wikipedia.org/wiki/Trope_\(literature\) "Trope (literature)") in these works began with [Mary Shelley](https://en.wikipedia.org/wiki/Mary_Shelley "Mary Shelley")'s _[Frankenstein](https://en.wikipedia.org/wiki/Frankenstein "Frankenstein")_, where a human creation becomes a threat to its masters. This includes such works as [Arthur C. Clarke's](https://en.wikipedia.org/wiki/2001:_A_Space_Odyssey_\(novel\) "2001: A Space Odyssey (novel)") and [Stanley Kubrick's](https://en.wikipedia.org/wiki/2001:_A_Space_Odyssey_\(film\) "2001: A Space Odyssey (film)") _2001: A Space Odyssey_ (both 1968), with [HAL 9000](https://en.wikipedia.org/wiki/HAL_9000 "HAL 9000"), the murderous computer in charge of the _[Discovery One](https://en.wikipedia.org/wiki/Discovery_One "Discovery One")_ spaceship, as well as _[The Terminator](https://en.wikipedia.org/wiki/The_Terminator "The Terminator")_ (1984) and _[The Matrix](https://en.wikipedia.org/wiki/The_Matrix "The Matrix")_ (1999). In contrast, the rare loyal robots such as Gort from _[The Day the Earth Stood Still](https://en.wikipedia.org/wiki/The_Day_the_Earth_Stood_Still "The Day the Earth Stood Still")_ (1951) and Bishop from _[Aliens](https://en.wikipedia.org/wiki/Aliens_\(film\) "Aliens (film)")_ (1986) are less prominent in popular culture.[\[335\]](#cite_note-FOOTNOTEButtazzo2001-363)

[Isaac Asimov](https://en.wikipedia.org/wiki/Isaac_Asimov "Isaac Asimov") introduced the [Three Laws of Robotics](https://en.wikipedia.org/wiki/Three_Laws_of_Robotics "Three Laws of Robotics") in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;[\[336\]](#cite_note-FOOTNOTEAnderson2008-364) while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.[\[337\]](#cite_note-FOOTNOTEMcCauley2007-365)

Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have [the ability to feel](https://en.wikipedia.org/wiki/Sentience "Sentience"), and thus to suffer. This appears in [Karel Čapek](https://en.wikipedia.org/wiki/Karel_%C4%8Capek "Karel Čapek")'s _[R.U.R.](https://en.wikipedia.org/wiki/R.U.R. "R.U.R.")_, the films _[A.I. Artificial Intelligence](https://en.wikipedia.org/wiki/A.I._Artificial_Intelligence "A.I. Artificial Intelligence")_ and _[Ex Machina](https://en.wikipedia.org/wiki/Ex_Machina_\(film\) "Ex Machina (film)")_, as well as the novel _[Do Androids Dream of Electric Sheep?](https://en.wikipedia.org/wiki/Do_Androids_Dream_of_Electric_Sheep%3F "Do Androids Dream of Electric Sheep?")_, by [Philip K. Dick](https://en.wikipedia.org/wiki/Philip_K._Dick "Philip K. Dick"). Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.[\[338\]](#cite_note-FOOTNOTEGalvan1997-366)

See also
--------

*   [Artificial intelligence detection software](https://en.wikipedia.org/wiki/Artificial_intelligence_detection_software "Artificial intelligence detection software") – Software to detect AI-generated content
*   [Behavior selection algorithm](https://en.wikipedia.org/wiki/Behavior_selection_algorithm "Behavior selection algorithm") – Algorithm that selects actions for intelligent agents
*   [Business process automation](https://en.wikipedia.org/wiki/Business_process_automation "Business process automation") – Technology-enabled automation of complex business processes
*   [Case-based reasoning](https://en.wikipedia.org/wiki/Case-based_reasoning "Case-based reasoning") – Process of solving new problems based on the solutions of similar past problems
*   [Computational intelligence](https://en.wikipedia.org/wiki/Computational_intelligence "Computational intelligence") – Ability of a computer to learn a specific task from data or experimental observation
*   [Digital immortality](https://en.wikipedia.org/wiki/Digital_immortality "Digital immortality") – Hypothetical concept of storing a personality in digital form
*   [Emergent algorithm](https://en.wikipedia.org/wiki/Emergent_algorithm "Emergent algorithm") – Algorithm exhibiting emergent behavior
*   [Female gendering of AI technologies](https://en.wikipedia.org/wiki/Female_gendering_of_AI_technologies "Female gendering of AI technologies") – Gender biases in digital technology
*   [Glossary of artificial intelligence](https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence "Glossary of artificial intelligence") – List of definitions of terms and concepts commonly used in the study of artificial intelligence
*   [Intelligence amplification](https://en.wikipedia.org/wiki/Intelligence_amplification "Intelligence amplification") – Use of information technology to augment human intelligence
*   [Mind uploading](https://en.wikipedia.org/wiki/Mind_uploading "Mind uploading") – Hypothetical process of digitally emulating a brain
*   [Robotic process automation](https://en.wikipedia.org/wiki/Robotic_process_automation "Robotic process automation") – Form of business process automation technology
*   [Weak artificial intelligence](https://en.wikipedia.org/wiki/Weak_artificial_intelligence "Weak artificial intelligence") – Form of artificial intelligence
*   [Wetware computer](https://en.wikipedia.org/wiki/Wetware_computer "Wetware computer") – Computer composed of organic material

Explanatory notes
-----------------

1.  ^ [Jump up to: _**a**_](#cite_ref-Problems_of_AI_14-0) [_**b**_](#cite_ref-Problems_of_AI_14-1) This list of intelligent traits is based on the topics covered by the major AI textbooks, including: [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), [Luger & Stubblefield (2004)](#CITEREFLugerStubblefield2004), [Poole, Mackworth & Goebel (1998)](#CITEREFPooleMackworthGoebel1998) and [Nilsson (1998)](#CITEREFNilsson1998)
2.  ^ [Jump up to: _**a**_](#cite_ref-Tools_of_AI_16-0) [_**b**_](#cite_ref-Tools_of_AI_16-1) This list of tools is based on the topics covered by the major AI textbooks, including: [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), [Luger & Stubblefield (2004)](#CITEREFLugerStubblefield2004), [Poole, Mackworth & Goebel (1998)](#CITEREFPooleMackworthGoebel1998) and [Nilsson (1998)](#CITEREFNilsson1998)
3.  **[^](#cite_ref-37 "Jump up")** It is among the reasons that [expert systems](https://en.wikipedia.org/wiki/Expert_system "Expert system") proved to be inefficient for capturing knowledge.[\[33\]](#cite_note-FOOTNOTENewquist1994296-35)[\[34\]](#cite_note-FOOTNOTECrevier1993204–208-36)
4.  **[^](#cite_ref-38 "Jump up")** "Rational agent" is general term used in [economics](https://en.wikipedia.org/wiki/Economics "Economics"), [philosophy](https://en.wikipedia.org/wiki/Philosophy "Philosophy") and theoretical artificial intelligence. It can refer to anything that directs its behavior to accomplish goals, such as a person, an animal, a corporation, a nation, or in the case of AI, a computer program.
5.  **[^](#cite_ref-51 "Jump up")** [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing "Alan Turing") discussed the centrality of learning as early as 1950, in his classic paper "[Computing Machinery and Intelligence](https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence "Computing Machinery and Intelligence")".[\[45\]](#cite_note-FOOTNOTETuring1950-49) In 1956, at the original Dartmouth AI summer conference, [Ray Solomonoff](https://en.wikipedia.org/wiki/Ray_Solomonoff "Ray Solomonoff") wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[\[46\]](#cite_note-FOOTNOTESolomonoff1956-50)
6.  **[^](#cite_ref-60 "Jump up")** See [AI winter § Machine translation and the ALPAC report of 1966](https://en.wikipedia.org/wiki/AI_winter#Machine_translation_and_the_ALPAC_report_of_1966 "AI winter")
7.  **[^](#cite_ref-102 "Jump up")** Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be [conditionally independent](https://en.wikipedia.org/wiki/Conditionally_independent "Conditionally independent") of one another. [AdSense](https://en.wikipedia.org/wiki/Google_AdSense "Google AdSense") uses a Bayesian network with over 300 million edges to learn which ads to serve.[\[95\]](#cite_note-FOOTNOTEDomingos2015chapter_6-101)
8.  **[^](#cite_ref-105 "Jump up")** Expectation-maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown [latent variables](https://en.wikipedia.org/wiki/Latent_variables "Latent variables").[\[97\]](#cite_note-FOOTNOTEDomingos2015210-104)
9.  **[^](#cite_ref-131 "Jump up")** Some form of deep neural networks (without a specific learning algorithm) were described by: [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing "Alan Turing") (1948);[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) [Frank Rosenblatt](https://en.wikipedia.org/wiki/Frank_Rosenblatt "Frank Rosenblatt")(1957);[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) [Karl Steinbuch](https://en.wikipedia.org/wiki/Karl_Steinbuch "Karl Steinbuch") and [Roger David Joseph](https://en.wikipedia.org/w/index.php?title=Roger_David_Joseph&action=edit&redlink=1 "Roger David Joseph (page does not exist)") (1961).[\[117\]](#cite_note-FOOTNOTESchmidhuber2022§5-125) Deep or recurrent networks that learned (or used gradient descent) were developed by: [Ernst Ising](https://en.wikipedia.org/wiki/Ernst_Ising "Ernst Ising") and [Wilhelm Lenz](https://en.wikipedia.org/wiki/Wilhelm_Lenz "Wilhelm Lenz") (1925);[\[118\]](#cite_note-FOOTNOTESchmidhuber2022§6-126) [Oliver Selfridge](https://en.wikipedia.org/wiki/Oliver_Selfridge "Oliver Selfridge") (1959);[\[117\]](#cite_note-FOOTNOTESchmidhuber2022§5-125) [Alexey Ivakhnenko](https://en.wikipedia.org/wiki/Alexey_Ivakhnenko "Alexey Ivakhnenko") and [Valentin Lapa](https://en.wikipedia.org/w/index.php?title=Valentin_Lapa&action=edit&redlink=1 "Valentin Lapa (page does not exist)") (1965);[\[118\]](#cite_note-FOOTNOTESchmidhuber2022§6-126) [Kaoru Nakano](https://en.wikipedia.org/w/index.php?title=Kaoru_Nakano&action=edit&redlink=1 "Kaoru Nakano (page does not exist)") (1977);[\[119\]](#cite_note-FOOTNOTESchmidhuber2022§7-127) [Shun-Ichi Amari](https://en.wikipedia.org/wiki/Shun-Ichi_Amari "Shun-Ichi Amari") (1972);[\[119\]](#cite_note-FOOTNOTESchmidhuber2022§7-127) [John Joseph Hopfield](https://en.wikipedia.org/wiki/John_Joseph_Hopfield "John Joseph Hopfield") (1982).[\[119\]](#cite_note-FOOTNOTESchmidhuber2022§7-127) Backpropagation was independently discovered by: [Henry J. Kelley](https://en.wikipedia.org/wiki/Henry_J._Kelley "Henry J. Kelley") (1960);[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) [Arthur E. Bryson](https://en.wikipedia.org/wiki/Arthur_E._Bryson "Arthur E. Bryson") (1962);[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) [Stuart Dreyfus](https://en.wikipedia.org/wiki/Stuart_Dreyfus "Stuart Dreyfus") (1962);[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) [Arthur E. Bryson](https://en.wikipedia.org/wiki/Arthur_E._Bryson "Arthur E. Bryson") and [Yu-Chi Ho](https://en.wikipedia.org/wiki/Yu-Chi_Ho "Yu-Chi Ho") (1969);[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) [Seppo Linnainmaa](https://en.wikipedia.org/wiki/Seppo_Linnainmaa "Seppo Linnainmaa") (1970);[\[120\]](#cite_note-FOOTNOTESchmidhuber2022§8-128) [Paul Werbos](https://en.wikipedia.org/wiki/Paul_Werbos "Paul Werbos") (1974).[\[116\]](#cite_note-FOOTNOTERussellNorvig2021785-124) In fact, backpropagation and gradient descent are straight forward applications of [Gottfried Leibniz](https://en.wikipedia.org/wiki/Gottfried_Leibniz "Gottfried Leibniz")' [chain rule](https://en.wikipedia.org/wiki/Chain_rule "Chain rule") in calculus (1676),[\[121\]](#cite_note-FOOTNOTESchmidhuber2022§2-129) and is essentially identical (for one layer) to the [method of least squares](https://en.wikipedia.org/wiki/Method_of_least_squares "Method of least squares"), developed independently by [Johann Carl Friedrich Gauss](https://en.wikipedia.org/wiki/Johann_Carl_Friedrich_Gauss "Johann Carl Friedrich Gauss") (1795) and [Adrien-Marie Legendre](https://en.wikipedia.org/wiki/Adrien-Marie_Legendre "Adrien-Marie Legendre") (1805).[\[122\]](#cite_note-FOOTNOTESchmidhuber2022§3-130) There are probably many others, yet to be discovered by historians of science.
10.  **[^](#cite_ref-133 "Jump up")** [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton "Geoffrey Hinton") said, of his work on neural networks in the 1990s, "our labeled datasets were thousands of times too small. \[And\] our computers were millions of times too slow"[\[123\]](#cite_note-132)
11.  **[^](#cite_ref-188 "Jump up")** Including [Jon Kleinberg](https://en.wikipedia.org/wiki/Jon_Kleinberg "Jon Kleinberg") ([Cornell](https://en.wikipedia.org/wiki/Cornell "Cornell")), Sendhil Mullainathan ([University of Chicago](https://en.wikipedia.org/wiki/University_of_Chicago "University of Chicago")), Cynthia Chouldechova ([Carnegie Mellon](https://en.wikipedia.org/wiki/Carnegie_Mellon "Carnegie Mellon")) and Sam Corbett-Davis ([Stanford](https://en.wikipedia.org/wiki/Stanford "Stanford"))[\[177\]](#cite_note-FOOTNOTEChristian202067–70-187)
12.  **[^](#cite_ref-194 "Jump up")** Moritz Hardt (a director at the [Max Planck Institute for Intelligent Systems](https://en.wikipedia.org/wiki/Max_Planck_Institute_for_Intelligent_Systems "Max Planck Institute for Intelligent Systems")) argues that machine learning "is fundamentally the wrong tool for a lot of domains, where you're trying to design interventions and mechanisms that change the world."[\[182\]](#cite_note-193)
13.  **[^](#cite_ref-201 "Jump up")** When the law was passed in 2018, it still contained a form of this provision.
14.  **[^](#cite_ref-209 "Jump up")** This is the [United Nations](https://en.wikipedia.org/wiki/United_Nations "United Nations")' definition, and includes things like [land mines](https://en.wikipedia.org/wiki/Land_mines "Land mines") as well.[\[195\]](#cite_note-FOOTNOTERussellNorvig2021989-208)
15.  **[^](#cite_ref-222 "Jump up")** See table 4; 9% is both the OECD average and the U.S. average.[\[207\]](#cite_note-FOOTNOTEArntzGregoryZierahn201633-221)
16.  **[^](#cite_ref-231 "Jump up")** Sometimes called a "[robopocalypse](https://en.wikipedia.org/wiki/Robopocalypse "Robopocalypse")".[\[215\]](#cite_note-FOOTNOTERussellNorvig20211001-230)
17.  **[^](#cite_ref-280 "Jump up")** "Electronic brain" was the term used by the press around this time.[\[262\]](#cite_note-FOOTNOTERussellNorvig20219-278)[\[263\]](#cite_note-279)
18.  **[^](#cite_ref-285 "Jump up")** Daniel Crevier wrote, "the conference is generally recognized as the official birthdate of the new science."[\[267\]](#cite_note-FOOTNOTECrevier199347–49-284) [Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") called the conference "the inception of artificial intelligence."[\[265\]](#cite_note-FOOTNOTERussellNorvig202117-282)
19.  **[^](#cite_ref-287 "Jump up")** [Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") wrote "for the next 20 years the field would be dominated by these people and their students."[\[268\]](#cite_note-FOOTNOTERussellNorvig200317-286)
20.  **[^](#cite_ref-289 "Jump up")** [Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") wrote "it was astonishing whenever a computer did anything kind of smartish".[\[269\]](#cite_note-FOOTNOTERussellNorvig200318-288)
21.  **[^](#cite_ref-290 "Jump up")** The programs described are [Arthur Samuel](https://en.wikipedia.org/wiki/Arthur_Samuel_\(computer_scientist\) "Arthur Samuel (computer scientist)")'s checkers program for the [IBM 701](https://en.wikipedia.org/wiki/IBM_701 "IBM 701"), [Daniel Bobrow](https://en.wikipedia.org/wiki/Daniel_Bobrow "Daniel Bobrow")'s [STUDENT](https://en.wikipedia.org/wiki/STUDENT_\(computer_program\) "STUDENT (computer program)"), [Newell](https://en.wikipedia.org/wiki/Allen_Newell "Allen Newell") and [Simon](https://en.wikipedia.org/wiki/Herbert_A._Simon "Herbert A. Simon")'s [Logic Theorist](https://en.wikipedia.org/wiki/Logic_Theorist "Logic Theorist") and [Terry Winograd](https://en.wikipedia.org/wiki/Terry_Winograd "Terry Winograd")'s [SHRDLU](https://en.wikipedia.org/wiki/SHRDLU "SHRDLU").
22.  **[^](#cite_ref-295 "Jump up")** [Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") write: "in almost all cases, these early systems failed on more difficult problems"[\[273\]](#cite_note-FOOTNOTERussellNorvig202121-294)
23.  **[^](#cite_ref-306 "Jump up")** [Embodied](https://en.wikipedia.org/wiki/Embodied_mind "Embodied mind") approaches to AI[\[280\]](#cite_note-FOOTNOTEMcCorduck2004454–462-302) were championed by [Hans Moravec](https://en.wikipedia.org/wiki/Hans_Moravec "Hans Moravec")[\[281\]](#cite_note-FOOTNOTEMoravec1988-303) and [Rodney Brooks](https://en.wikipedia.org/wiki/Rodney_Brooks "Rodney Brooks")[\[282\]](#cite_note-FOOTNOTEBrooks1990-304) and went by many names: [Nouvelle AI](https://en.wikipedia.org/wiki/Nouvelle_AI "Nouvelle AI").[\[282\]](#cite_note-FOOTNOTEBrooks1990-304) [Developmental robotics](https://en.wikipedia.org/wiki/Developmental_robotics "Developmental robotics"),[\[283\]](#cite_note-Developmental_robotics-305)
24.  **[^](#cite_ref-313 "Jump up")** Matteo Wong wrote in [The Atlantic](https://en.wikipedia.org/wiki/The_Atlantic "The Atlantic"): "Whereas for decades, computer-science fields such as natural-language processing, computer vision, and robotics used extremely different methods, now they all use a programming method called "deep learning." As a result, their code and approaches have become more similar, and their models are easier to integrate into one another."[\[289\]](#cite_note-FOOTNOTEWong2023-312)
25.  **[^](#cite_ref-317 "Jump up")** Jack Clark wrote in [Bloomberg](https://en.wikipedia.org/wiki/Bloomberg_News "Bloomberg News"): "After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever", and noted that the number of software projects that use machine learning at [Google](https://en.wikipedia.org/wiki/Google "Google") increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.[\[291\]](#cite_note-FOOTNOTEClark2015b-315)
26.  **[^](#cite_ref-329 "Jump up")** [Nils Nilsson](https://en.wikipedia.org/wiki/Nils_Nilsson_\(researcher\) "Nils Nilsson (researcher)") wrote in 1983: "Simply put, there is wide disagreement in the field about what AI is all about."[\[303\]](#cite_note-FOOTNOTENilsson198310-328)
27.  **[^](#cite_ref-335 "Jump up")** Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."[\[308\]](#cite_note-FOOTNOTECrevier1993125-334)
28.  **[^](#cite_ref-Searle's_strong_AI_348-0 "Jump up")** Searle presented this definition of "Strong AI" in 1999.[\[318\]](#cite_note-FOOTNOTESearle1999-345) Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states."[\[319\]](#cite_note-FOOTNOTESearle19801-346) Strong AI is defined similarly by [Russell](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") and [Norvig](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig"): "Stong AI – the assertion that machines that do so are _actually_ thinking (as opposed to _simulating_ thinking)."[\[320\]](#cite_note-FOOTNOTERussellNorvig20219817-347)

References
----------

1.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERussellNorvig20211–4_1-0) [_**b**_](#cite_ref-FOOTNOTERussellNorvig20211–4_1-1) [_**c**_](#cite_ref-FOOTNOTERussellNorvig20211–4_1-2) [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 1–4.
2.  **[^](#cite_ref-FOOTNOTEGoogle2016_2-0 "Jump up")** [Google (2016)](#CITEREFGoogle2016).
3.  **[^](#cite_ref-3 "Jump up")** [AI set to exceed human brain power](http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/) [Archived](https://web.archive.org/web/20080219001624/http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/) 2008-02-19 at the [Wayback Machine](https://en.wikipedia.org/wiki/Wayback_Machine "Wayback Machine") CNN.com (July 26, 2006)
4.  **[^](#cite_ref-andreas_4-0 "Jump up")** Kaplan, Andreas; Haenlein, Michael (2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". _Business Horizons_. **62**: 15–25. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/j.bushor.2018.08.004](https://doi.org/10.1016%2Fj.bushor.2018.08.004). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [158433736](https://api.semanticscholar.org/CorpusID:158433736).
5.  ^ [Jump up to: _**a**_](#cite_ref-turing_5-0) [_**b**_](#cite_ref-turing_5-1) [_**c**_](#cite_ref-turing_5-2) [_**d**_](#cite_ref-turing_5-3) Copeland, J., ed. (2004). _The Essential Turing: the ideas that gave birth to the computer age_. Oxford, England: Clarendon Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [0-19-825079-7](https://en.wikipedia.org/wiki/Special:BookSources/0-19-825079-7 "Special:BookSources/0-19-825079-7").
6.  ^ [Jump up to: _**a**_](#cite_ref-Dartmouth_workshop_6-0) [_**b**_](#cite_ref-Dartmouth_workshop_6-1) [Dartmouth workshop](https://en.wikipedia.org/wiki/Dartmouth_workshop "Dartmouth workshop"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 18)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 111–136)
    *   [NRC (1999](#CITEREFNRC1999), pp. 200–201)
    
    The proposal:
    
    *   [McCarthy et al. (1955)](#CITEREFMcCarthyMinskyRochesterShannon1955)
    
7.  ^ [Jump up to: _**a**_](#cite_ref-AI_in_the_60s_7-0) [_**b**_](#cite_ref-AI_in_the_60s_7-1) Successful programs the 1960s:
    
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 243–252)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 52–107)
    *   [Moravec (1988](#CITEREFMoravec1988), p. 9)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 19–21)
    
8.  ^ [Jump up to: _**a**_](#cite_ref-AI_in_the_80s_8-0) [_**b**_](#cite_ref-AI_in_the_80s_8-1) Funding initiatives in the early 1980s: [Fifth Generation Project](https://en.wikipedia.org/wiki/Fifth_Generation_Project "Fifth Generation Project") (Japan), [Alvey](https://en.wikipedia.org/wiki/Alvey "Alvey") (UK), [Microelectronics and Computer Technology Corporation](https://en.wikipedia.org/wiki/Microelectronics_and_Computer_Technology_Corporation "Microelectronics and Computer Technology Corporation") (US), [Strategic Computing Initiative](https://en.wikipedia.org/wiki/Strategic_Computing_Initiative "Strategic Computing Initiative") (US):
    
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 426–441)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 161–162, 197–203, 211, 240)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 23)
    *   [NRC (1999](#CITEREFNRC1999), pp. 210–211)
    *   [Newquist (1994](#CITEREFNewquist1994), pp. 235–248)
    
9.  ^ [Jump up to: _**a**_](#cite_ref-First_AI_winter_9-0) [_**b**_](#cite_ref-First_AI_winter_9-1) First [AI Winter](https://en.wikipedia.org/wiki/AI_Winter "AI Winter"), [Lighthill report](https://en.wikipedia.org/wiki/Lighthill_report "Lighthill report"), [Mansfield Amendment](https://en.wikipedia.org/wiki/Mansfield_Amendment "Mansfield Amendment")
    
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 115–117)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 21–22)
    *   [NRC (1999](#CITEREFNRC1999), pp. 212–213)
    *   [Howe (1994)](#CITEREFHowe1994)
    *   [Newquist (1994](#CITEREFNewquist1994), pp. 189–201)
    
10.  ^ [Jump up to: _**a**_](#cite_ref-Second_AI_winter_10-0) [_**b**_](#cite_ref-Second_AI_winter_10-1) Second [AI Winter](https://en.wikipedia.org/wiki/AI_Winter "AI Winter"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 24)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 430–435)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 209–210)
    *   [NRC (1999](#CITEREFNRC1999), pp. 214–216)
    *   [Newquist (1994](#CITEREFNewquist1994), pp. 301–318)
    
11.  ^ [Jump up to: _**a**_](#cite_ref-Deep_learning_revolution_11-0) [_**b**_](#cite_ref-Deep_learning_revolution_11-1) [Deep learning](https://en.wikipedia.org/wiki/Deep_learning "Deep learning") revolution, [AlexNet](https://en.wikipedia.org/wiki/AlexNet "AlexNet"):
    
    *   [Goldman (2022)](#CITEREFGoldman2022)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 26)
    *   [McKinsey (2018)](#CITEREFMcKinsey2018)
    
12.  **[^](#cite_ref-FOOTNOTEToews2023_12-0 "Jump up")** [Toews (2023)](#CITEREFToews2023).
13.  **[^](#cite_ref-FOOTNOTEFrank2023_13-0 "Jump up")** [Frank (2023)](#CITEREFFrank2023).
14.  ^ [Jump up to: _**a**_](#cite_ref-AGI_15-0) [_**b**_](#cite_ref-AGI_15-1) [_**c**_](#cite_ref-AGI_15-2) [Artificial general intelligence](https://en.wikipedia.org/wiki/Artificial_general_intelligence "Artificial general intelligence"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 32–33, 1020–1021)
    
    Proposal for the modern version:
    
    *   [Pennachin & Goertzel (2007)](#CITEREFPennachinGoertzel2007)
    
    Warnings of overspecialization in AI from leading researchers:
    
    *   [Nilsson (1995)](#CITEREFNilsson1995)
    *   [McCarthy (2007)](#CITEREFMcCarthy2007)
    *   [Beal & Winston (2009)](#CITEREFBealWinston2009)
    
15.  **[^](#cite_ref-AI_influences_17-0 "Jump up")** [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §1.2).
16.  **[^](#cite_ref-18 "Jump up")** Problem-solving, puzzle solving, game playing, and deduction:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 3–5)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 6) ([constraint satisfaction](https://en.wikipedia.org/wiki/Constraint_satisfaction "Constraint satisfaction"))
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), chpt. 2, 3, 7, 9)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), chpt. 3, 4, 6, 8)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 7–12)
    
17.  **[^](#cite_ref-19 "Jump up")** Uncertain reasoning:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 12–18)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 345–395)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 333–381)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 7–12)
    
18.  ^ [Jump up to: _**a**_](#cite_ref-Intractability_20-0) [_**b**_](#cite_ref-Intractability_20-1) [_**c**_](#cite_ref-Intractability_20-2) [Intractability and efficiency](https://en.wikipedia.org/wiki/Intractably "Intractably") and the [combinatorial explosion](https://en.wikipedia.org/wiki/Combinatorial_explosion "Combinatorial explosion"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 21)
    
19.  ^ [Jump up to: _**a**_](#cite_ref-Psychological_evidence_of_sub-symbolic_reasoning_21-0) [_**b**_](#cite_ref-Psychological_evidence_of_sub-symbolic_reasoning_21-1) [_**c**_](#cite_ref-Psychological_evidence_of_sub-symbolic_reasoning_21-2) Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge:
    
    *   [Kahneman (2011)](#CITEREFKahneman2011)
    *   [Dreyfus & Dreyfus (1986)](#CITEREFDreyfusDreyfus1986)
    *   [Wason & Shapiro (1966)](#CITEREFWasonShapiro1966)
    *   [Kahneman, Slovic & Tversky (1982)](#CITEREFKahnemanSlovicTversky1982)
    
20.  **[^](#cite_ref-22 "Jump up")** [Knowledge representation](https://en.wikipedia.org/wiki/Knowledge_representation "Knowledge representation") and [knowledge engineering](https://en.wikipedia.org/wiki/Knowledge_engineering "Knowledge engineering"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 10)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 23–46, 69–81, 169–233, 235–277, 281–298, 319–345)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 227–243),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 17.1–17.4, 18)
    
21.  **[^](#cite_ref-FOOTNOTESmoliarZhang1994_23-0 "Jump up")** [Smoliar & Zhang (1994)](#CITEREFSmoliarZhang1994).
22.  **[^](#cite_ref-FOOTNOTENeumannMöller2008_24-0 "Jump up")** [Neumann & Möller (2008)](#CITEREFNeumannMöller2008).
23.  **[^](#cite_ref-FOOTNOTEKupermanReichleyBailey2006_25-0 "Jump up")** [Kuperman, Reichley & Bailey (2006)](#CITEREFKupermanReichleyBailey2006).
24.  **[^](#cite_ref-FOOTNOTEMcGarry2005_26-0 "Jump up")** [McGarry (2005)](#CITEREFMcGarry2005).
25.  **[^](#cite_ref-FOOTNOTEBertiniDel_BimboTorniai2006_27-0 "Jump up")** [Bertini, Del Bimbo & Torniai (2006)](#CITEREFBertiniDel_BimboTorniai2006).
26.  **[^](#cite_ref-FOOTNOTERussellNorvig2021272_28-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 272.
27.  **[^](#cite_ref-Representing_categories_and_relations_29-0 "Jump up")** Representing categories and relations: [Semantic networks](https://en.wikipedia.org/wiki/Semantic_network "Semantic network"), [description logics](https://en.wikipedia.org/wiki/Description_logic "Description logic"), [inheritance](https://en.wikipedia.org/wiki/Inheritance_\(object-oriented_programming\) "Inheritance (object-oriented programming)") (including [frames](https://en.wikipedia.org/wiki/Frame_\(artificial_intelligence\) "Frame (artificial intelligence)"), and [scripts](https://en.wikipedia.org/wiki/Scripts_\(artificial_intelligence\) "Scripts (artificial intelligence)")):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §10.2 & 10.5),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 174–177),
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 248–258),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 18.3)
    
28.  **[^](#cite_ref-Representing_time_30-0 "Jump up")** Representing events and time:[Situation calculus](https://en.wikipedia.org/wiki/Situation_calculus "Situation calculus"), [event calculus](https://en.wikipedia.org/wiki/Event_calculus "Event calculus"), [fluent calculus](https://en.wikipedia.org/wiki/Fluent_calculus "Fluent calculus") (including solving the [frame problem](https://en.wikipedia.org/wiki/Frame_problem "Frame problem")):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §10.3),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 281–298),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 18.2)
    
29.  **[^](#cite_ref-Representing_causation_31-0 "Jump up")** [Causal calculus](https://en.wikipedia.org/wiki/Causality#Causal_calculus "Causality"):
    
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 335–337)
    
30.  **[^](#cite_ref-Representing_knowledge_about_knowledge_32-0 "Jump up")** Representing knowledge about knowledge: Belief calculus, [modal logics](https://en.wikipedia.org/wiki/Modal_logic "Modal logic"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §10.4),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 275–277)
    
31.  ^ [Jump up to: _**a**_](#cite_ref-Default_reasoning_and_non-monotonic_logic_33-0) [_**b**_](#cite_ref-Default_reasoning_and_non-monotonic_logic_33-1) [Default reasoning](https://en.wikipedia.org/wiki/Default_reasoning "Default reasoning"), [Frame problem](https://en.wikipedia.org/wiki/Frame_problem "Frame problem"), [default logic](https://en.wikipedia.org/wiki/Default_logic "Default logic"), [non-monotonic logics](https://en.wikipedia.org/wiki/Non-monotonic_logic "Non-monotonic logic"), [circumscription](https://en.wikipedia.org/wiki/Circumscription_\(logic\) "Circumscription (logic)"), [closed world assumption](https://en.wikipedia.org/wiki/Closed_world_assumption "Closed world assumption"), [abduction](https://en.wikipedia.org/wiki/Abductive_reasoning "Abductive reasoning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §10.6)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 248–256, 323–335)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 335–363)
    *   [Nilsson (1998](#CITEREFNilsson1998), ~18.3.3)
    
    (Poole _et al._ places abduction under "default reasoning". Luger _et al._ places this under "uncertain reasoning").
32.  ^ [Jump up to: _**a**_](#cite_ref-Breadth_of_commonsense_knowledge_34-0) [_**b**_](#cite_ref-Breadth_of_commonsense_knowledge_34-1) Breadth of commonsense knowledge:
    
    *   [Lenat & Guha (1989](#CITEREFLenatGuha1989), Introduction)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 113–114),
    *   [Moravec (1988](#CITEREFMoravec1988), p. 13),
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 241, 385, 982) ([qualification problem](https://en.wikipedia.org/wiki/Qualification_problem "Qualification problem"))
    
33.  **[^](#cite_ref-FOOTNOTENewquist1994296_35-0 "Jump up")** [Newquist (1994)](#CITEREFNewquist1994), p. 296.
34.  **[^](#cite_ref-FOOTNOTECrevier1993204–208_36-0 "Jump up")** [Crevier (1993)](#CITEREFCrevier1993), pp. 204–208.
35.  **[^](#cite_ref-FOOTNOTERussellNorvig2021528_39-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 528.
36.  **[^](#cite_ref-40 "Jump up")** [Automated planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling "Automated planning and scheduling"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 11).
    
37.  **[^](#cite_ref-41 "Jump up")** [Automated decision making](https://en.wikipedia.org/wiki/Automated_decision_making "Automated decision making"), [Decision theory](https://en.wikipedia.org/wiki/Decision_theory "Decision theory"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 16–18).
    
38.  **[^](#cite_ref-42 "Jump up")** [Classical planning](https://en.wikipedia.org/wiki/Automated_planning_and_scheduling#classical_planning "Automated planning and scheduling"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Section 11.2).
    
39.  **[^](#cite_ref-43 "Jump up")** Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Section 11.5).
    
40.  **[^](#cite_ref-44 "Jump up")** Uncertain preferences:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Section 16.7)
    
    [Inverse reinforcement learning](https://en.wikipedia.org/wiki/Inverse_reinforcement_learning "Inverse reinforcement learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Section 22.6)
    
41.  **[^](#cite_ref-45 "Jump up")** [Information value theory](https://en.wikipedia.org/wiki/Information_value_theory "Information value theory"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Section 16.6).
    
42.  **[^](#cite_ref-46 "Jump up")** [Markov decision process](https://en.wikipedia.org/wiki/Markov_decision_process "Markov decision process"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 17).
    
43.  **[^](#cite_ref-47 "Jump up")** [Game theory](https://en.wikipedia.org/wiki/Game_theory "Game theory") and multi-agent decision theory:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 18).
    
44.  **[^](#cite_ref-machine_learning_48-0 "Jump up")** [Learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 19–22)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 397–438)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 385–542)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 3.3, 10.3, 17.5, 20)
    
45.  **[^](#cite_ref-FOOTNOTETuring1950_49-0 "Jump up")** [Turing (1950)](#CITEREFTuring1950).
46.  **[^](#cite_ref-FOOTNOTESolomonoff1956_50-0 "Jump up")** [Solomonoff (1956)](#CITEREFSolomonoff1956).
47.  **[^](#cite_ref-52 "Jump up")** [Unsupervised learning](https://en.wikipedia.org/wiki/Unsupervised_learning "Unsupervised learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 653) (definition)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 738–740) ([cluster analysis](https://en.wikipedia.org/wiki/Cluster_analysis "Cluster analysis"))
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 846–860) ([word embedding](https://en.wikipedia.org/wiki/Word_embedding "Word embedding"))
    
48.  ^ [Jump up to: _**a**_](#cite_ref-Supervised_learning_53-0) [_**b**_](#cite_ref-Supervised_learning_53-1) [Supervised learning](https://en.wikipedia.org/wiki/Supervised_learning "Supervised learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §19.2) (Definition)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 19–20) (Techniques)
    
49.  **[^](#cite_ref-54 "Jump up")** [Reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning "Reinforcement learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 22)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 442–449)
    
50.  **[^](#cite_ref-55 "Jump up")** [Transfer learning](https://en.wikipedia.org/wiki/Transfer_learning "Transfer learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 281)
    *   [The Economist (2016)](#CITEREFThe_Economist2016)
    
51.  **[^](#cite_ref-56 "Jump up")** ["Artificial Intelligence (AI): What Is AI and How Does It Work? | Built In"](https://builtin.com/artificial-intelligence). _builtin.com_. Retrieved 30 October 2023.
52.  **[^](#cite_ref-57 "Jump up")** [Computational learning theory](https://en.wikipedia.org/wiki/Computational_learning_theory "Computational learning theory"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 672–674)
    *   [Jordan & Mitchell (2015)](#CITEREFJordanMitchell2015)
    
53.  **[^](#cite_ref-58 "Jump up")** [Natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing") (NLP):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 23–24)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 91–104)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 591–632)
    
54.  **[^](#cite_ref-59 "Jump up")** Subproblems of [NLP](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 849–850)
    
55.  **[^](#cite_ref-FOOTNOTERussellNorvig2021856–858_61-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 856–858.
56.  **[^](#cite_ref-FOOTNOTEDickson2022_62-0 "Jump up")** [Dickson (2022)](#CITEREFDickson2022).
57.  **[^](#cite_ref-63 "Jump up")** Modern statistical and deep learning approaches to [NLP](https://en.wikipedia.org/wiki/Natural_language_processing "Natural language processing"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 24)
    *   [Cambria & White (2014)](#CITEREFCambriaWhite2014)
    
58.  **[^](#cite_ref-FOOTNOTEVincent2019_64-0 "Jump up")** [Vincent (2019)](#CITEREFVincent2019).
59.  **[^](#cite_ref-FOOTNOTERussellNorvig2021875–878_65-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 875–878.
60.  **[^](#cite_ref-FOOTNOTEBushwick2023_66-0 "Jump up")** [Bushwick (2023)](#CITEREFBushwick2023).
61.  **[^](#cite_ref-67 "Jump up")** [Computer vision](https://en.wikipedia.org/wiki/Computer_vision "Computer vision"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 25)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 6)
    
62.  **[^](#cite_ref-FOOTNOTERussellNorvig2021849–850_68-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 849–850.
63.  **[^](#cite_ref-FOOTNOTERussellNorvig2021895–899_69-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 895–899.
64.  **[^](#cite_ref-FOOTNOTERussellNorvig2021899–901_70-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 899–901.
65.  **[^](#cite_ref-FOOTNOTERussellNorvig2021931–938_71-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 931–938.
66.  **[^](#cite_ref-FOOTNOTEMIT_AIL2014_72-0 "Jump up")** [MIT AIL (2014)](#CITEREFMIT_AIL2014).
67.  **[^](#cite_ref-73 "Jump up")** [Affective computing](https://en.wikipedia.org/wiki/Affective_computing "Affective computing"):
    
    *   [Thro (1993)](#CITEREFThro1993)
    *   [Edelson (1991)](#CITEREFEdelson1991)
    *   [Tao & Tan (2005)](#CITEREFTaoTan2005)
    *   [Scassellati (2002)](#CITEREFScassellati2002)
    
68.  **[^](#cite_ref-FOOTNOTEWaddell2018_74-0 "Jump up")** [Waddell (2018)](#CITEREFWaddell2018).
69.  **[^](#cite_ref-FOOTNOTEPoriaCambriaBajpaiHussain2017_75-0 "Jump up")** [Poria et al. (2017)](#CITEREFPoriaCambriaBajpaiHussain2017).
70.  **[^](#cite_ref-76 "Jump up")** [Search algorithms](https://en.wikipedia.org/wiki/Search_algorithm "Search algorithm"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 3–5)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 113–163)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 79–164, 193–219)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 7–12)
    
71.  **[^](#cite_ref-State_space_search_77-0 "Jump up")** [State space search](https://en.wikipedia.org/wiki/State_space_search "State space search"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 3)
    
72.  **[^](#cite_ref-FOOTNOTERussellNorvig2021§11.2_78-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), §11.2.
73.  **[^](#cite_ref-Uninformed_search_79-0 "Jump up")** [Uninformed searches](https://en.wikipedia.org/wiki/Uninformed_search "Uninformed search") ([breadth first search](https://en.wikipedia.org/wiki/Breadth_first_search "Breadth first search"), [depth-first search](https://en.wikipedia.org/wiki/Depth-first_search "Depth-first search") and general [state space search](https://en.wikipedia.org/wiki/State_space_search "State space search")):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §3.4)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 113–132)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 79–121)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 8)
    
74.  **[^](#cite_ref-Informed_search_80-0 "Jump up")** [Heuristic](https://en.wikipedia.org/wiki/Heuristic "Heuristic") or informed searches (e.g., greedy [best first](https://en.wikipedia.org/wiki/Best-first_search "Best-first search") and [A\*](https://en.wikipedia.org/wiki/A*_search_algorithm "A* search algorithm")):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), s§3.5)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 132–147)
    *   [Poole & Mackworth (2017](#CITEREFPooleMackworth2017), §3.6)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 133–150)
    
75.  **[^](#cite_ref-81 "Jump up")** [Adversarial search](https://en.wikipedia.org/wiki/Adversarial_search "Adversarial search"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 5)
    
76.  **[^](#cite_ref-Local_search2_82-0 "Jump up")** [Local](https://en.wikipedia.org/wiki/Local_search_\(optimization\) "Local search (optimization)") or "[optimization](https://en.wikipedia.org/wiki/Mathematical_optimization "Mathematical optimization")" search:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 4)
    
77.  **[^](#cite_ref-83 "Jump up")** Singh Chauhan, Nagesh (18 December 2020). ["Optimization Algorithms in Neural Networks"](https://www.kdnuggets.com/optimization-algorithms-in-neural-networks). _KDnuggets_. Retrieved 13 January 2024.
78.  **[^](#cite_ref-84 "Jump up")** [Evolutionary computation](https://en.wikipedia.org/wiki/Evolutionary_computation "Evolutionary computation"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §4.1.2)
    
79.  **[^](#cite_ref-FOOTNOTEMerkleMiddendorf2013_85-0 "Jump up")** [Merkle & Middendorf (2013)](#CITEREFMerkleMiddendorf2013).
80.  **[^](#cite_ref-Logic_86-0 "Jump up")** [Logic](https://en.wikipedia.org/wiki/Logic "Logic"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 6–9)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 35–77)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 13–16)
    
81.  **[^](#cite_ref-Propositional_logic_87-0 "Jump up")** [Propositional logic](https://en.wikipedia.org/wiki/Propositional_logic "Propositional logic"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 6)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 45–50)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 13)
    
82.  **[^](#cite_ref-Predicate_logic_88-0 "Jump up")** [First-order logic](https://en.wikipedia.org/wiki/First-order_logic "First-order logic") and features such as [equality](https://en.wikipedia.org/wiki/Equality_\(mathematics\) "Equality (mathematics)"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 7)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 268–275),
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 50–62),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 15)
    
83.  **[^](#cite_ref-Inference_89-0 "Jump up")** [Logical inference](https://en.wikipedia.org/wiki/Logical_inference "Logical inference"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 10)
    
84.  **[^](#cite_ref-Logic_as_search_90-0 "Jump up")** logical deduction as search:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §9.3, §9.4)
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. ~46–52)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 62–73)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 4.2, 7.2)
    
85.  **[^](#cite_ref-Resolution_91-0 "Jump up")** [Resolution](https://en.wikipedia.org/wiki/Resolution_\(logic\) "Resolution (logic)") and [unification](https://en.wikipedia.org/wiki/Unification_\(computer_science\) "Unification (computer science)"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §7.5.2, §9.2, §9.5)
    
86.  **[^](#cite_ref-92 "Jump up")** Warren, D.H.; Pereira, L.M.; Pereira, F. (1977). "Prolog-the language and its implementation compared with Lisp". _[ACM SIGPLAN Notices](https://en.wikipedia.org/wiki/ACM_SIGPLAN_Notices "ACM SIGPLAN Notices")_. **12** (8): 109–115. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1145/872734.806939](https://doi.org/10.1145%2F872734.806939).
87.  **[^](#cite_ref-Fuzzy_logic_93-0 "Jump up")** Fuzzy logic:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 214, 255, 459)
    *   [Scientific American (1999)](#CITEREFScientific_American1999)
    
88.  ^ [Jump up to: _**a**_](#cite_ref-Uncertain_reasoning_94-0) [_**b**_](#cite_ref-Uncertain_reasoning_94-1) Stochastic methods for uncertain reasoning:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 12–18 and 20),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 345–395),
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 165–191, 333–381),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 19)
    
89.  **[^](#cite_ref-Decisions_theory_and_analysis_95-0 "Jump up")** [decision theory](https://en.wikipedia.org/wiki/Decision_theory "Decision theory") and [decision analysis](https://en.wikipedia.org/wiki/Decision_analysis "Decision analysis"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 16–18),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 381–394)
    
90.  **[^](#cite_ref-Information_value_theory_96-0 "Jump up")** [Information value theory](https://en.wikipedia.org/wiki/Information_value_theory "Information value theory"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §16.6)
    
91.  **[^](#cite_ref-Markov_decision_process_97-0 "Jump up")** [Markov decision processes](https://en.wikipedia.org/wiki/Markov_decision_process "Markov decision process") and dynamic [decision networks](https://en.wikipedia.org/wiki/Decision_network "Decision network"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 17)
    
92.  ^ [Jump up to: _**a**_](#cite_ref-Stochastic_temporal_models_98-0) [_**b**_](#cite_ref-Stochastic_temporal_models_98-1) [_**c**_](#cite_ref-Stochastic_temporal_models_98-2) Stochastic temporal models:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 14)
    
    [Hidden Markov model](https://en.wikipedia.org/wiki/Hidden_Markov_model "Hidden Markov model"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §14.3)
    
    [Kalman filters](https://en.wikipedia.org/wiki/Kalman_filter "Kalman filter"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §14.4)
    
    [Dynamic Bayesian networks](https://en.wikipedia.org/wiki/Dynamic_Bayesian_network "Dynamic Bayesian network"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §14.5)
    
93.  **[^](#cite_ref-Game_theory_and_mechanism_design_99-0 "Jump up")** [Game theory](https://en.wikipedia.org/wiki/Game_theory "Game theory") and [mechanism design](https://en.wikipedia.org/wiki/Mechanism_design "Mechanism design"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 18)
    
94.  **[^](#cite_ref-Bayesian_networks_100-0 "Jump up")** [Bayesian networks](https://en.wikipedia.org/wiki/Bayesian_network "Bayesian network"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §12.5–12.6, §13.4–13.5, §14.3–14.5, §16.5, §20.2 -20.3),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 361–381),
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. ~182–190, ≈363–379),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 19.3–4)
    
95.  **[^](#cite_ref-FOOTNOTEDomingos2015chapter_6_101-0 "Jump up")** [Domingos (2015)](#CITEREFDomingos2015), chapter 6.
96.  **[^](#cite_ref-Bayesian_inference_103-0 "Jump up")** [Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_inference "Bayesian inference") algorithm:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §13.3–13.5),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 361–381),
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. ~363–379),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 19.4 & 7)
    
97.  **[^](#cite_ref-FOOTNOTEDomingos2015210_104-0 "Jump up")** [Domingos (2015)](#CITEREFDomingos2015), p. 210.
98.  **[^](#cite_ref-Bayesian_learning_106-0 "Jump up")** [Bayesian learning](https://en.wikipedia.org/wiki/Bayesian_learning "Bayesian learning") and the [expectation-maximization algorithm](https://en.wikipedia.org/wiki/Expectation-maximization_algorithm "Expectation-maximization algorithm"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 20),
    *   [Poole, Mackworth & Goebel (1998](#CITEREFPooleMackworthGoebel1998), pp. 424–433),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 20)
    *   [Domingos (2015](#CITEREFDomingos2015), p. 210)
    
99.  **[^](#cite_ref-Bayesian_decision_networks_107-0 "Jump up")** [Bayesian decision theory](https://en.wikipedia.org/wiki/Bayesian_decision_theory "Bayesian decision theory") and Bayesian [decision networks](https://en.wikipedia.org/wiki/Decision_network "Decision network"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §16.5)
    
100.  **[^](#cite_ref-Statistical_classifiers_108-0 "Jump up")** Statistical learning methods and [classifiers](https://en.wikipedia.org/wiki/Classifier_\(mathematics\) "Classifier (mathematics)"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), chpt. 20),
    
101.  **[^](#cite_ref-109 "Jump up")** [Decision trees](https://en.wikipedia.org/wiki/Alternating_decision_tree "Alternating decision tree"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §19.3)
    *   [Domingos (2015](#CITEREFDomingos2015), p. 88)
    
102.  **[^](#cite_ref-110 "Jump up")** [Non-parameteric](https://en.wikipedia.org/wiki/Nonparametric_statistics "Nonparametric statistics") learning models such as [K-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbor "K-nearest neighbor") and [support vector machines](https://en.wikipedia.org/wiki/Support_vector_machines "Support vector machines"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §19.7)
    *   [Domingos (2015](#CITEREFDomingos2015), p. 187) (k-nearest neighbor)
    *   [Domingos (2015](#CITEREFDomingos2015), p. 88) (kernel methods)
    
103.  **[^](#cite_ref-FOOTNOTEDomingos2015152_111-0 "Jump up")** [Domingos (2015)](#CITEREFDomingos2015), p. 152.
104.  **[^](#cite_ref-112 "Jump up")** [Naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier "Naive Bayes classifier"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §12.6)
    *   [Domingos (2015](#CITEREFDomingos2015), p. 152)
    
105.  ^ [Jump up to: _**a**_](#cite_ref-Neural_networks_113-0) [_**b**_](#cite_ref-Neural_networks_113-1) Neural networks:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 21),
    *   [Domingos (2015](#CITEREFDomingos2015), Chapter 4)
    
106.  **[^](#cite_ref-Backpropagation_114-0 "Jump up")** Gradient calculation in computational graphs, [backpropagation](https://en.wikipedia.org/wiki/Backpropagation "Backpropagation"), [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation "Automatic differentiation"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §21.2),
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 467–474),
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 3.3)
    
107.  **[^](#cite_ref-115 "Jump up")** [Universal approximation theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem "Universal approximation theorem"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 752)
    
    The theorem:
    
    *   [Cybenko (1988)](#CITEREFCybenko1988)
    *   [Hornik, Stinchcombe & White (1989)](#CITEREFHornikStinchcombeWhite1989)
    
108.  **[^](#cite_ref-116 "Jump up")** [Feedforward neural networks](https://en.wikipedia.org/wiki/Feedforward_neural_network "Feedforward neural network"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §21.1)
    
109.  **[^](#cite_ref-117 "Jump up")** [Recurrent neural networks](https://en.wikipedia.org/wiki/Recurrent_neural_network "Recurrent neural network"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §21.6)
    
110.  **[^](#cite_ref-118 "Jump up")** [Perceptrons](https://en.wikipedia.org/wiki/Perceptron "Perceptron"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 21, 22, 683, 22)
    
111.  ^ [Jump up to: _**a**_](#cite_ref-Deep_learning_119-0) [_**b**_](#cite_ref-Deep_learning_119-1) [Deep learning](https://en.wikipedia.org/wiki/Deep_learning "Deep learning"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), Chpt. 21)
    *   [Goodfellow, Bengio & Courville (2016)](#CITEREFGoodfellowBengioCourville2016)
    *   [Hinton _et al._ (2016)](#CITEREFHinton_et_al.2016)
    *   [Schmidhuber (2015)](#CITEREFSchmidhuber2015)
    
112.  **[^](#cite_ref-120 "Jump up")** [Convolutional neural networks](https://en.wikipedia.org/wiki/Convolutional_neural_networks "Convolutional neural networks"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), §21.3)
    
113.  **[^](#cite_ref-FOOTNOTEDengYu2014199–200_121-0 "Jump up")** [Deng & Yu (2014)](#CITEREFDengYu2014), pp. 199–200.
114.  **[^](#cite_ref-FOOTNOTECiresanMeierSchmidhuber2012_122-0 "Jump up")** [Ciresan, Meier & Schmidhuber (2012)](#CITEREFCiresanMeierSchmidhuber2012).
115.  **[^](#cite_ref-FOOTNOTERussellNorvig2021751_123-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 751.
116.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-0) [_**b**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-1) [_**c**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-2) [_**d**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-3) [_**e**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-4) [_**f**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-5) [_**g**_](#cite_ref-FOOTNOTERussellNorvig2021785_124-6) [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 785.
117.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTESchmidhuber2022§5_125-0) [_**b**_](#cite_ref-FOOTNOTESchmidhuber2022§5_125-1) [Schmidhuber (2022)](#CITEREFSchmidhuber2022), §5.
118.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTESchmidhuber2022§6_126-0) [_**b**_](#cite_ref-FOOTNOTESchmidhuber2022§6_126-1) [Schmidhuber (2022)](#CITEREFSchmidhuber2022), §6.
119.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTESchmidhuber2022§7_127-0) [_**b**_](#cite_ref-FOOTNOTESchmidhuber2022§7_127-1) [_**c**_](#cite_ref-FOOTNOTESchmidhuber2022§7_127-2) [Schmidhuber (2022)](#CITEREFSchmidhuber2022), §7.
120.  **[^](#cite_ref-FOOTNOTESchmidhuber2022§8_128-0 "Jump up")** [Schmidhuber (2022)](#CITEREFSchmidhuber2022), §8.
121.  **[^](#cite_ref-FOOTNOTESchmidhuber2022§2_129-0 "Jump up")** [Schmidhuber (2022)](#CITEREFSchmidhuber2022), §2.
122.  **[^](#cite_ref-FOOTNOTESchmidhuber2022§3_130-0 "Jump up")** [Schmidhuber (2022)](#CITEREFSchmidhuber2022), §3.
123.  **[^](#cite_ref-132 "Jump up")** Quoted in [Christian (2020](#CITEREFChristian2020), p. 22)
124.  **[^](#cite_ref-FOOTNOTESmith2023_134-0 "Jump up")** [Smith (2023)](#CITEREFSmith2023).
125.  **[^](#cite_ref-135 "Jump up")** ["Explained: Generative AI"](https://news.mit.edu/2023/explained-generative-ai-1109). 9 November 2023.
126.  **[^](#cite_ref-136 "Jump up")** ["AI Writing and Content Creation Tools"](https://mitsloanedtech.mit.edu/ai/tools/writing/). MIT Sloan Teaching & Learning Technologies. Retrieved 25 December 2023.
127.  **[^](#cite_ref-FOOTNOTEMarmouyet2023_137-0 "Jump up")** [Marmouyet (2023)](#CITEREFMarmouyet2023).
128.  **[^](#cite_ref-FOOTNOTEKobielus2019_138-0 "Jump up")** [Kobielus (2019)](#CITEREFKobielus2019).
129.  **[^](#cite_ref-139 "Jump up")** Davenport, T; Kalakota, R (June 2019). ["The potential for artificial intelligence in healthcare"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181). _Future Healthc J_. **6** (2): 94–98. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.7861/futurehosp.6-2-94](https://doi.org/10.7861%2Ffuturehosp.6-2-94). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [6616181](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [31363513](https://pubmed.ncbi.nlm.nih.gov/31363513).
130.  ^ [Jump up to: _**a**_](#cite_ref-The_future_of_personalized_cardiova_140-0) [_**b**_](#cite_ref-The_future_of_personalized_cardiova_140-1) Bax, Monique; Thorpe, Jordan; Romanov, Valentin (December 2023). ["The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence"](https://doi.org/10.3389%2Ffsens.2023.1294721). _Frontiers in Sensors_. **4**. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.3389/fsens.2023.1294721](https://doi.org/10.3389%2Ffsens.2023.1294721). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [2673-5067](https://www.worldcat.org/issn/2673-5067).
131.  **[^](#cite_ref-141 "Jump up")** Jumper, J; Evans, R; Pritzel, A (2021). ["Highly accurate protein structure prediction with AlphaFold"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605). _Nature_. **596** (7873): 583–589. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2021Natur.596..583J](https://ui.adsabs.harvard.edu/abs/2021Natur.596..583J). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/s41586-021-03819-2](https://doi.org/10.1038%2Fs41586-021-03819-2). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [8371605](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [34265844](https://pubmed.ncbi.nlm.nih.gov/34265844).
132.  **[^](#cite_ref-142 "Jump up")** ["AI discovers new class of antibiotics to kill drug-resistant bacteria"](https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/). 20 December 2023.
133.  **[^](#cite_ref-143 "Jump up")** ["AI speeds up drug design for Parkinson's ten-fold"](https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold). Cambridge University. 17 April 2024.
134.  **[^](#cite_ref-144 "Jump up")** Horne, Robert I.; Andrzejewska, Ewa A.; Alam, Parvez; Brotzakis, Z. Faidon; Srivastava, Ankit; Aubert, Alice; Nowinska, Magdalena; Gregory, Rebecca C.; Staats, Roxine; Possenti, Andrea; Chia, Sean; Sormanni, Pietro; Ghetti, Bernardino; Caughey, Byron; Knowles, Tuomas P. J.; Vendruscolo, Michele (17 April 2024). ["Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning"](https://www.nature.com/articles/s41589-024-01580-x). _Nature Chemical Biology_. **20** (5). Nature: 634–645. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/s41589-024-01580-x](https://doi.org/10.1038%2Fs41589-024-01580-x). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [11062903](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11062903). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [38632492](https://pubmed.ncbi.nlm.nih.gov/38632492).
135.  **[^](#cite_ref-145 "Jump up")** Grant, Eugene F.; Lardner, Rex (25 July 1952). ["The Talk of the Town – It"](https://www.newyorker.com/magazine/1952/08/02/it). _The New Yorker_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0028-792X](https://www.worldcat.org/issn/0028-792X). Retrieved 28 January 2024.
136.  **[^](#cite_ref-146 "Jump up")** Anderson, Mark Robert (11 May 2017). ["Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution"](https://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882). _The Conversation_. Retrieved 28 January 2024.
137.  **[^](#cite_ref-147 "Jump up")** Markoff, John (16 February 2011). ["Computer Wins on 'Jeopardy!': Trivial, It's Not"](https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html). _The New York Times_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0362-4331](https://www.worldcat.org/issn/0362-4331). Retrieved 28 January 2024.
138.  **[^](#cite_ref-148 "Jump up")** Byford, Sam (27 May 2017). ["AlphaGo retires from competitive Go after defeating world number one 3-0"](https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future). _The Verge_. Retrieved 28 January 2024.
139.  **[^](#cite_ref-149 "Jump up")** Brown, Noam; Sandholm, Tuomas (30 August 2019). ["Superhuman AI for multiplayer poker"](https://www.science.org/doi/10.1126/science.aay2400). _Science_. **365** (6456): 885–890. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2019Sci...365..885B](https://ui.adsabs.harvard.edu/abs/2019Sci...365..885B). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1126/science.aay2400](https://doi.org/10.1126%2Fscience.aay2400). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0036-8075](https://www.worldcat.org/issn/0036-8075). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [31296650](https://pubmed.ncbi.nlm.nih.gov/31296650).
140.  **[^](#cite_ref-150 "Jump up")** ["MuZero: Mastering Go, chess, shogi and Atari without rules"](https://deepmind.google/discover/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules/). _Google DeepMind_. 23 December 2020. Retrieved 28 January 2024.
141.  **[^](#cite_ref-151 "Jump up")** Sample, Ian (30 October 2019). ["AI becomes grandmaster in 'fiendishly complex' StarCraft II"](https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii). _The Guardian_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0261-3077](https://www.worldcat.org/issn/0261-3077). Retrieved 28 January 2024.
142.  **[^](#cite_ref-152 "Jump up")** Wurman, P.R.; Barrett, S.; Kawamoto, K. (2022). "Outracing champion Gran Turismo drivers with deep reinforcement learning". _Nature_. **602** (7896): 223–228. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2022Natur.602..223W](https://ui.adsabs.harvard.edu/abs/2022Natur.602..223W). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/s41586-021-04357-7](https://doi.org/10.1038%2Fs41586-021-04357-7). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [35140384](https://pubmed.ncbi.nlm.nih.gov/35140384).
143.  ^ [Jump up to: _**a**_](#cite_ref-:22_153-0) [_**b**_](#cite_ref-:22_153-1) [_**c**_](#cite_ref-:22_153-2) Congressional Research Service (2019). [_Artificial Intelligence and National Security_](https://fas.org/sgp/crs/natsec/R45178.pdf) (PDF). Washington, DC: Congressional Research Service.[PD-notice](https://en.wikipedia.org/wiki/Template:PD-notice "Template:PD-notice")
144.  ^ [Jump up to: _**a**_](#cite_ref-AI_154-0) [_**b**_](#cite_ref-AI_154-1) Slyusar, Vadym (2019). ["Artificial intelligence as the basis of future control networks"](https://www.researchgate.net/publication/334573170). _ResearchGate_. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.13140/RG.2.2.30247.50087](https://doi.org/10.13140%2FRG.2.2.30247.50087).
145.  **[^](#cite_ref-155 "Jump up")** Knight, Will. ["The US and 30 Other Nations Agree to Set Guardrails for Military AI"](https://www.wired.com/story/the-us-and-30-other-nations-agree-to-set-guardrails-for-military-ai/). _Wired_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1059-1028](https://www.worldcat.org/issn/1059-1028). Retrieved 24 January 2024.
146.  **[^](#cite_ref-156 "Jump up")** Marcelline, Marco (27 May 2023). ["ChatGPT: Most Americans Know About It, But Few Actually Use the AI Chatbot"](https://www.pcmag.com/news/few-americans-have-actually-tried-chatgpt-despite-most-knowing-about-it). _PCMag_. Retrieved 28 January 2024.
147.  **[^](#cite_ref-157 "Jump up")** Lu, Donna (31 March 2023). ["Misinformation, mistakes and the Pope in a puffer: what rapidly evolving AI can – and can't – do"](https://www.theguardian.com/technology/2023/apr/01/misinformation-mistakes-and-the-pope-in-a-puffer-what-rapidly-evolving-ai-can-and-cant-do). _The Guardian_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0261-3077](https://www.worldcat.org/issn/0261-3077). Retrieved 28 January 2024.
148.  **[^](#cite_ref-158 "Jump up")** Hurst, Luke (23 May 2023). ["How a fake image of a Pentagon explosion shared on Twitter caused a real dip on Wall Street"](https://www.euronews.com/next/2023/05/23/fake-news-about-an-explosion-at-the-pentagon-spreads-on-verified-accounts-on-twitter). _euronews_. Retrieved 28 January 2024.
149.  **[^](#cite_ref-159 "Jump up")** Ransbotham, Sam; Kiron, David; Gerbert, Philipp; Reeves, Martin (6 September 2017). ["Reshaping Business With Artificial Intelligence"](https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/). _MIT Sloan Management Review_. [Archived](https://web.archive.org/web/20240213070751/https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/) from the original on 13 February 2024.
150.  **[^](#cite_ref-FOOTNOTESimonite2016_160-0 "Jump up")** [Simonite (2016)](#CITEREFSimonite2016).
151.  **[^](#cite_ref-FOOTNOTERussellNorvig2021987_161-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 987.
152.  **[^](#cite_ref-FOOTNOTELaskowski2023_162-0 "Jump up")** [Laskowski (2023)](#CITEREFLaskowski2023).
153.  **[^](#cite_ref-FOOTNOTEGAO2022_163-0 "Jump up")** [GAO (2022)](#CITEREFGAO2022).
154.  **[^](#cite_ref-FOOTNOTEValinsky2019_164-0 "Jump up")** [Valinsky (2019)](#CITEREFValinsky2019).
155.  **[^](#cite_ref-FOOTNOTERussellNorvig2021991_165-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 991.
156.  **[^](#cite_ref-FOOTNOTERussellNorvig2021991–992_166-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 991–992.
157.  **[^](#cite_ref-FOOTNOTEChristian202063_167-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), p. 63.
158.  **[^](#cite_ref-FOOTNOTEVincent2022_168-0 "Jump up")** [Vincent (2022)](#CITEREFVincent2022).
159.  **[^](#cite_ref-169 "Jump up")** Kopel, Matthew. ["Copyright Services: Fair Use"](https://guides.library.cornell.edu/copyright/fair-use). _Cornell University Library_. Retrieved 26 April 2024.
160.  **[^](#cite_ref-170 "Jump up")** Burgess, Matt. ["How to Stop Your Data From Being Used to Train AI"](https://www.wired.com/story/how-to-stop-your-data-from-being-used-to-train-ai/). _Wired_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1059-1028](https://www.worldcat.org/issn/1059-1028). Retrieved 26 April 2024.
161.  **[^](#cite_ref-FOOTNOTEReisner2023_171-0 "Jump up")** [Reisner (2023)](#CITEREFReisner2023).
162.  **[^](#cite_ref-FOOTNOTEAlterHarris2023_172-0 "Jump up")** [Alter & Harris (2023)](#CITEREFAlterHarris2023).
163.  **[^](#cite_ref-173 "Jump up")** ["Getting the Innovation Ecosystem Ready for AI. An IP policy toolkit"](https://www.wipo.int/edocs/pubdocs/en/wipo-pub-2003-en-getting-the-innovation-ecosystem-ready-for-ai.pdf) (PDF). _[WIPO](https://en.wikipedia.org/wiki/WIPO "WIPO")_.
164.  **[^](#cite_ref-FOOTNOTENicas2018_174-0 "Jump up")** [Nicas (2018)](#CITEREFNicas2018).
165.  **[^](#cite_ref-175 "Jump up")** Rainie, Lee; Keeter, Scott; Perrin, Andrew (22 July 2019). ["Trust and Distrust in America"](https://www.pewresearch.org/politics/2019/07/22/trust-and-distrust-in-america/). _Pew Research Center_. [Archived](https://web.archive.org/web/20240222000601/https://www.pewresearch.org/politics/2019/07/22/trust-and-distrust-in-america/) from the original on 22 February 2024.
166.  **[^](#cite_ref-FOOTNOTEWilliams2023_176-0 "Jump up")** [Williams (2023)](#CITEREFWilliams2023).
167.  **[^](#cite_ref-FOOTNOTETaylorHern2023_177-0 "Jump up")** [Taylor & Hern (2023)](#CITEREFTaylorHern2023).
168.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERose2023_178-0) [_**b**_](#cite_ref-FOOTNOTERose2023_178-1) [Rose (2023)](#CITEREFRose2023).
169.  **[^](#cite_ref-FOOTNOTECNA2019_179-0 "Jump up")** [CNA (2019)](#CITEREFCNA2019).
170.  **[^](#cite_ref-FOOTNOTEGoffrey200817_180-0 "Jump up")** [Goffrey (2008)](#CITEREFGoffrey2008), p. 17.
171.  **[^](#cite_ref-181 "Jump up")** [Berdahl et al. (2023)](#CITEREFBerdahlBakerMannOsoba2023); [Goffrey (2008](#CITEREFGoffrey2008), p. 17); [Rose (2023)](#CITEREFRose2023); [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 995)
172.  **[^](#cite_ref-182 "Jump up")** [Algorithmic bias](https://en.wikipedia.org/wiki/Algorithmic_bias "Algorithmic bias") and [Fairness (machine learning)](https://en.wikipedia.org/wiki/Fairness_\(machine_learning\) "Fairness (machine learning)"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), section 27.3.3)
    *   [Christian (2020](#CITEREFChristian2020), Fairness)
    
173.  **[^](#cite_ref-FOOTNOTEChristian202025_183-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), p. 25.
174.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERussellNorvig2021995_184-0) [_**b**_](#cite_ref-FOOTNOTERussellNorvig2021995_184-1) [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 995.
175.  **[^](#cite_ref-FOOTNOTEGrantHill2023_185-0 "Jump up")** [Grant & Hill (2023)](#CITEREFGrantHill2023).
176.  **[^](#cite_ref-FOOTNOTELarsonAngwin2016_186-0 "Jump up")** [Larson & Angwin (2016)](#CITEREFLarsonAngwin2016).
177.  **[^](#cite_ref-FOOTNOTEChristian202067–70_187-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), p. 67–70.
178.  **[^](#cite_ref-189 "Jump up")** [Christian (2020](#CITEREFChristian2020), pp. 67–70); [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 993–994)
179.  **[^](#cite_ref-190 "Jump up")** [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 995); [Lipartito (2011](#CITEREFLipartito2011), p. 36); [Goodman & Flaxman (2017](#CITEREFGoodmanFlaxman2017), p. 6); [Christian (2020](#CITEREFChristian2020), pp. 39–40, 65)
180.  **[^](#cite_ref-191 "Jump up")** Quoted in [Christian (2020](#CITEREFChristian2020), p. 65).
181.  **[^](#cite_ref-192 "Jump up")** [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 994); [Christian (2020](#CITEREFChristian2020), pp. 40, 80–81)
182.  **[^](#cite_ref-193 "Jump up")** Quoted in [Christian (2020](#CITEREFChristian2020), p. 80)
183.  **[^](#cite_ref-FOOTNOTEDockrill2022_195-0 "Jump up")** [Dockrill (2022)](#CITEREFDockrill2022).
184.  **[^](#cite_ref-FOOTNOTESample2017_196-0 "Jump up")** [Sample (2017)](#CITEREFSample2017).
185.  **[^](#cite_ref-197 "Jump up")** ["Black Box AI"](https://www.techopedia.com/definition/34940/black-box-ai). 16 June 2023.
186.  **[^](#cite_ref-FOOTNOTEChristian2020110_198-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), p. 110.
187.  **[^](#cite_ref-FOOTNOTEChristian202088–91_199-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), pp. 88–91.
188.  **[^](#cite_ref-200 "Jump up")** [Christian (2020](#CITEREFChristian2020), p. 83); [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 997)
189.  **[^](#cite_ref-FOOTNOTEChristian202091_202-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), p. 91.
190.  **[^](#cite_ref-FOOTNOTEChristian202083_203-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), p. 83.
191.  **[^](#cite_ref-FOOTNOTEVerma2021_204-0 "Jump up")** [Verma (2021)](#CITEREFVerma2021).
192.  **[^](#cite_ref-FOOTNOTERothman2020_205-0 "Jump up")** [Rothman (2020)](#CITEREFRothman2020).
193.  **[^](#cite_ref-FOOTNOTEChristian2020105–108_206-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), pp. 105–108.
194.  **[^](#cite_ref-FOOTNOTEChristian2020108–112_207-0 "Jump up")** [Christian (2020)](#CITEREFChristian2020), pp. 108–112.
195.  **[^](#cite_ref-FOOTNOTERussellNorvig2021989_208-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 989.
196.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERussellNorvig2021987–990_210-0) [_**b**_](#cite_ref-FOOTNOTERussellNorvig2021987–990_210-1) [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), pp. 987–990.
197.  **[^](#cite_ref-FOOTNOTERussellNorvig2021988_211-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 988.
198.  **[^](#cite_ref-212 "Jump up")** [Robitzski (2018)](#CITEREFRobitzski2018); [Sainato (2015)](#CITEREFSainato2015)
199.  **[^](#cite_ref-FOOTNOTEHarari2018_213-0 "Jump up")** [Harari (2018)](#CITEREFHarari2018).
200.  **[^](#cite_ref-214 "Jump up")** Buckley, Chris; Mozur, Paul (22 May 2019). ["How China Uses High-Tech Surveillance to Subdue Minorities"](https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html). _The New York Times_.
201.  **[^](#cite_ref-215 "Jump up")** ["Security lapse exposed a Chinese smart city surveillance system"](https://web.archive.org/web/20210307203740/https://consent.yahoo.com/v2/collectConsent?sessionId=3_cc-session_c8562b93-9863-4915-8523-6c7b930a3efc). 3 May 2019. Archived from [the original](https://social.techcrunch.com/2019/05/03/china-smart-city-exposed/) on 7 March 2021. Retrieved 14 September 2020.
202.  **[^](#cite_ref-FOOTNOTEUrbinaLentzosInvernizziEkins2022_216-0 "Jump up")** [Urbina et al. (2022)](#CITEREFUrbinaLentzosInvernizziEkins2022).
203.  **[^](#cite_ref-217 "Jump up")** Metz, Cade (10 July 2023). ["In the Age of A.I., Tech's Little Guys Need Big Friends"](https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html). _New York Times_.
204.  ^ [Jump up to: _**a**_](#cite_ref-auto1_218-0) [_**b**_](#cite_ref-auto1_218-1) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022) [51(3) Industrial Law Journal 511–559](https://academic.oup.com/ilj/article/51/3/511/6321008) [Archived](https://web.archive.org/web/20230527163045/https://academic.oup.com/ilj/article/51/3/511/6321008) 27 May 2023 at the [Wayback Machine](https://en.wikipedia.org/wiki/Wayback_Machine "Wayback Machine")
205.  **[^](#cite_ref-219 "Jump up")** [Ford & Colvin (2015)](#CITEREFFordColvin2015);[McGaughey (2022)](#CITEREFMcGaughey2022)
206.  **[^](#cite_ref-FOOTNOTEIGM_Chicago2017_220-0 "Jump up")** [IGM Chicago (2017)](#CITEREFIGM_Chicago2017).
207.  **[^](#cite_ref-FOOTNOTEArntzGregoryZierahn201633_221-0 "Jump up")** [Arntz, Gregory & Zierahn (2016)](#CITEREFArntzGregoryZierahn2016), p. 33.
208.  **[^](#cite_ref-223 "Jump up")** [Lohr (2017)](#CITEREFLohr2017); [Frey & Osborne (2017)](#CITEREFFreyOsborne2017); [Arntz, Gregory & Zierahn (2016](#CITEREFArntzGregoryZierahn2016), p. 33)
209.  **[^](#cite_ref-224 "Jump up")** Zhou, Viola (11 April 2023). ["AI is already taking video game illustrators' jobs in China"](https://restofworld.org/2023/ai-image-china-video-game-layoffs/). _Rest of World_. Retrieved 17 August 2023.
210.  **[^](#cite_ref-225 "Jump up")** Carter, Justin (11 April 2023). ["China's game art industry reportedly decimated by growing AI use"](https://www.gamedeveloper.com/art/china-s-game-art-industry-reportedly-decimated-ai-art-use). _Game Developer_. Retrieved 17 August 2023.
211.  **[^](#cite_ref-FOOTNOTEMorgenstern2015_226-0 "Jump up")** [Morgenstern (2015)](#CITEREFMorgenstern2015).
212.  **[^](#cite_ref-227 "Jump up")** [Mahdawi (2017)](#CITEREFMahdawi2017); [Thompson (2014)](#CITEREFThompson2014)
213.  **[^](#cite_ref-228 "Jump up")** Tarnoff, Ben (4 August 2023). "Lessons from Eliza". _[The Guardian Weekly](https://en.wikipedia.org/wiki/The_Guardian_Weekly "The Guardian Weekly")_. pp. 34–39.
214.  **[^](#cite_ref-FOOTNOTECellan-Jones2014_229-0 "Jump up")** [Cellan-Jones (2014)](#CITEREFCellan-Jones2014).
215.  **[^](#cite_ref-FOOTNOTERussellNorvig20211001_230-0 "Jump up")** [Russell & Norvig 2021](#CITEREFRussellNorvig2021), p. 1001.
216.  **[^](#cite_ref-FOOTNOTEBostrom2014_232-0 "Jump up")** [Bostrom (2014)](#CITEREFBostrom2014).
217.  **[^](#cite_ref-FOOTNOTERussell2019_233-0 "Jump up")** [Russell (2019)](#CITEREFRussell2019).
218.  **[^](#cite_ref-234 "Jump up")** [Bostrom (2014)](#CITEREFBostrom2014); [Müller & Bostrom (2014)](#CITEREFMüllerBostrom2014); [Bostrom (2015)](#CITEREFBostrom2015).
219.  **[^](#cite_ref-FOOTNOTEHarari2023_235-0 "Jump up")** [Harari (2023)](#CITEREFHarari2023).
220.  **[^](#cite_ref-FOOTNOTEMüllerBostrom2014_236-0 "Jump up")** [Müller & Bostrom (2014)](#CITEREFMüllerBostrom2014).
221.  **[^](#cite_ref-237 "Jump up")** Leaders' concerns about the existential risks of AI around 2015:
    
    *   [Rawlinson (2015)](#CITEREFRawlinson2015)
    *   [Holley (2015)](#CITEREFHolley2015)
    *   [Gibbs (2014)](#CITEREFGibbs2014)
    *   [Sainato (2015)](#CITEREFSainato2015)
    
222.  **[^](#cite_ref-FOOTNOTEValance2023_238-0 "Jump up")** [Valance (2023)](#CITEREFValance2023).
223.  **[^](#cite_ref-guardian2023_239-0 "Jump up")** Taylor, Josh (7 May 2023). ["Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says"](https://www.theguardian.com/technology/2023/may/07/rise-of-artificial-intelligence-is-inevitable-but-should-not-be-feared-father-of-ai-says). _The Guardian_. Retrieved 26 May 2023.
224.  **[^](#cite_ref-foxnews2023_240-0 "Jump up")** Colton, Emma (7 May 2023). ["'Father of AI' says tech fears misplaced: 'You cannot stop it'"](https://www.foxnews.com/tech/father-ai-jurgen-schmidhuber-says-tech-fears-misplaced-cannot-stop). _Fox News_. Retrieved 26 May 2023.
225.  **[^](#cite_ref-forbes2023_241-0 "Jump up")** Jones, Hessie (23 May 2023). ["Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life's Work Won't Lead To Dystopia"](https://www.forbes.com/sites/hessiejones/2023/05/23/juergen-schmidhuber-renowned-father-of-modern-ai-says-his-lifes-work-wont-lead-to-dystopia/). _Forbes_. Retrieved 26 May 2023.
226.  **[^](#cite_ref-andrewng2023_242-0 "Jump up")** McMorrow, Ryan (19 December 2023). ["Andrew Ng: 'Do we think the world is better off with more or less intelligence?'"](https://www.ft.com/content/2dc07f9e-d2a9-4d98-b746-b051f9352be3). _Financial Times_. Retrieved 30 December 2023.
227.  **[^](#cite_ref-lecun2023_243-0 "Jump up")** Levy, Steven (22 December 2023). ["How Not to Be Stupid About AI, With Yann LeCun"](https://www.wired.com/story/artificial-intelligence-meta-yann-lecun-interview/). _Wired_. Retrieved 30 December 2023.
228.  **[^](#cite_ref-244 "Jump up")** Arguments that AI is not an imminent risk:
    
    *   [Brooks (2014)](#CITEREFBrooks2014)
    *   [Geist (2015)](#CITEREFGeist2015)
    *   [Madrigal (2015)](#CITEREFMadrigal2015)
    *   [Lee (2014)](#CITEREFLee2014)
    
229.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTEChristian202067,_73_245-0) [_**b**_](#cite_ref-FOOTNOTEChristian202067,_73_245-1) [Christian (2020)](#CITEREFChristian2020), pp. 67, 73.
230.  **[^](#cite_ref-FOOTNOTEYudkowsky2008_246-0 "Jump up")** [Yudkowsky (2008)](#CITEREFYudkowsky2008).
231.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTEAndersonAnderson2011_247-0) [_**b**_](#cite_ref-FOOTNOTEAndersonAnderson2011_247-1) [Anderson & Anderson (2011)](#CITEREFAndersonAnderson2011).
232.  **[^](#cite_ref-FOOTNOTEAAAI2014_248-0 "Jump up")** [AAAI (2014)](#CITEREFAAAI2014).
233.  **[^](#cite_ref-FOOTNOTEWallach2010_249-0 "Jump up")** [Wallach (2010)](#CITEREFWallach2010).
234.  **[^](#cite_ref-FOOTNOTERussell2019173_250-0 "Jump up")** [Russell (2019)](#CITEREFRussell2019), p. 173.
235.  **[^](#cite_ref-251 "Jump up")** Melton, Ashley Stewart, Monica. ["Hugging Face CEO says he's focused on building a 'sustainable model' for the $4.5 billion open-source-AI startup"](https://www.businessinsider.com/hugging-face-open-source-ai-approach-2023-12). _Business Insider_. Retrieved 14 April 2024.`{{[cite web](https://en.wikipedia.org/wiki/Template:Cite_web "Template:Cite web")}}`: CS1 maint: multiple names: authors list ([link](https://en.wikipedia.org/wiki/Category:CS1_maint:_multiple_names:_authors_list "Category:CS1 maint: multiple names: authors list"))
236.  **[^](#cite_ref-252 "Jump up")** Wiggers, Kyle (9 April 2024). ["Google open sources tools to support AI model development"](https://techcrunch.com/2024/04/09/google-open-sources-tools-to-support-ai-model-development/). _TechCrunch_. Retrieved 14 April 2024.
237.  **[^](#cite_ref-253 "Jump up")** Heaven, Will Douglas (12 May 2023). ["The open-source AI boom is built on Big Tech's handouts. How long will it last?"](https://www.technologyreview.com/2023/05/12/1072950/open-source-ai-google-openai-eleuther-meta/). _MIT Technology Review_. Retrieved 14 April 2024.
238.  **[^](#cite_ref-254 "Jump up")** Brodsky, Sascha (19 December 2023). ["Mistral AI's New Language Model Aims for Open Source Supremacy"](https://aibusiness.com/nlp/mistral-ai-s-new-language-model-aims-for-open-source-supremacy). _AI Business_.
239.  **[^](#cite_ref-255 "Jump up")** Edwards, Benj (22 February 2024). ["Stability announces Stable Diffusion 3, a next-gen AI image generator"](https://arstechnica.com/information-technology/2024/02/stability-announces-stable-diffusion-3-a-next-gen-ai-image-generator/). _Ars Technica_. Retrieved 14 April 2024.
240.  **[^](#cite_ref-256 "Jump up")** Marshall, Matt (29 January 2024). ["How enterprises are using open source LLMs: 16 examples"](https://venturebeat.com/ai/how-enterprises-are-using-open-source-llms-16-examples/). _VentureBeat_.
241.  **[^](#cite_ref-257 "Jump up")** Piper, Kelsey (2 February 2024). ["Should we make our most powerful AI models open source to all?"](https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake). _Vox_. Retrieved 14 April 2024.
242.  **[^](#cite_ref-258 "Jump up")** Alan Turing Institute (2019). ["Understanding artificial intelligence ethics and safety"](https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf) (PDF).
243.  **[^](#cite_ref-259 "Jump up")** Alan Turing Institute (2023). ["AI Ethics and Governance in Practice"](https://www.turing.ac.uk/sites/default/files/2023-12/aieg-ati-ai-ethics-an-intro_1.pdf) (PDF).
244.  **[^](#cite_ref-260 "Jump up")** Floridi, Luciano; Cowls, Josh (23 June 2019). ["A Unified Framework of Five Principles for AI in Society"](https://hdsr.mitpress.mit.edu/pub/l0jsh9d1). _Harvard Data Science Review_. **1** (1). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1162/99608f92.8cd550d1](https://doi.org/10.1162%2F99608f92.8cd550d1). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [198775713](https://api.semanticscholar.org/CorpusID:198775713).
245.  **[^](#cite_ref-261 "Jump up")** Buruk, Banu; Ekmekci, Perihan Elif; Arda, Berna (1 September 2020). ["A critical perspective on guidelines for responsible and trustworthy artificial intelligence"](https://doi.org/10.1007/s11019-020-09948-1). _Medicine, Health Care and Philosophy_. **23** (3): 387–399. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/s11019-020-09948-1](https://doi.org/10.1007%2Fs11019-020-09948-1). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1572-8633](https://www.worldcat.org/issn/1572-8633). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [32236794](https://pubmed.ncbi.nlm.nih.gov/32236794). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [214766800](https://api.semanticscholar.org/CorpusID:214766800).
246.  **[^](#cite_ref-262 "Jump up")** Kamila, Manoj Kumar; Jasrotia, Sahil Singh (1 January 2023). ["Ethical issues in the development of artificial intelligence: recognizing the risks"](https://doi.org/10.1108/IJOES-05-2023-0107). _International Journal of Ethics and Systems_. ahead-of-print (ahead-of-print). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1108/IJOES-05-2023-0107](https://doi.org/10.1108%2FIJOES-05-2023-0107). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [2514-9369](https://www.worldcat.org/issn/2514-9369). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [259614124](https://api.semanticscholar.org/CorpusID:259614124).
247.  **[^](#cite_ref-263 "Jump up")** ["AI Safety Institute releases new AI safety evaluations platform"](https://www.gov.uk/government/news/ai-safety-institute-releases-new-ai-safety-evaluations-platform). UK Government. 10 May 2024. Retrieved 14 May 2024.
248.  **[^](#cite_ref-264 "Jump up")** Regulation of AI to mitigate risks:
    
    *   [Berryhill et al. (2019)](#CITEREFBerryhillHeangClogherMcBride2019)
    *   [Barfield & Pagallo (2018)](#CITEREFBarfieldPagallo2018)
    *   [Iphofen & Kritikos (2019)](#CITEREFIphofenKritikos2019)
    *   [Wirtz, Weyerer & Geyer (2018)](#CITEREFWirtzWeyererGeyer2018)
    *   [Buiten (2019)](#CITEREFBuiten2019)
    
249.  **[^](#cite_ref-FOOTNOTELaw_Library_of_Congress_\(U.S.\)._Global_Legal_Research_Directorate2019_265-0 "Jump up")** [Law Library of Congress (U.S.). Global Legal Research Directorate (2019)](#CITEREFLaw_Library_of_Congress_\(U.S.\)._Global_Legal_Research_Directorate2019).
250.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTEVincent2023_266-0) [_**b**_](#cite_ref-FOOTNOTEVincent2023_266-1) [Vincent (2023)](#CITEREFVincent2023).
251.  **[^](#cite_ref-FOOTNOTEStanford_University2023_267-0 "Jump up")** [Stanford University (2023)](#CITEREFStanford_University2023).
252.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTEUNESCO2021_268-0) [_**b**_](#cite_ref-FOOTNOTEUNESCO2021_268-1) [_**c**_](#cite_ref-FOOTNOTEUNESCO2021_268-2) [_**d**_](#cite_ref-FOOTNOTEUNESCO2021_268-3) [UNESCO (2021)](#CITEREFUNESCO2021).
253.  **[^](#cite_ref-FOOTNOTEKissinger2021_269-0 "Jump up")** [Kissinger (2021)](#CITEREFKissinger2021).
254.  **[^](#cite_ref-FOOTNOTEAltmanBrockmanSutskever2023_270-0 "Jump up")** [Altman, Brockman & Sutskever (2023)](#CITEREFAltmanBrockmanSutskever2023).
255.  **[^](#cite_ref-271 "Jump up")** VOA News (25 October 2023). ["UN Announces Advisory Body on Artificial Intelligence"](https://www.voanews.com/a/un-announces-advisory-body-on-artificial-intelligence-/7328732.html).
256.  **[^](#cite_ref-FOOTNOTEEdwards2023_272-0 "Jump up")** [Edwards (2023)](#CITEREFEdwards2023).
257.  **[^](#cite_ref-FOOTNOTEKasperowicz2023_273-0 "Jump up")** [Kasperowicz (2023)](#CITEREFKasperowicz2023).
258.  **[^](#cite_ref-FOOTNOTEFox_News2023_274-0 "Jump up")** [Fox News (2023)](#CITEREFFox_News2023).
259.  **[^](#cite_ref-275 "Jump up")** Milmo, Dan (3 November 2023). "Hope or Horror? The great AI debate dividing its pioneers". _[The Guardian Weekly](https://en.wikipedia.org/wiki/The_Guardian_Weekly "The Guardian Weekly")_. pp. 10–12.
260.  **[^](#cite_ref-2023-11-01-bletchley-declaration-full_276-0 "Jump up")** ["The Bletchley Declaration by Countries Attending the AI Safety Summit, 1–2 November 2023"](https://web.archive.org/web/20231101123904/https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration/the-bletchley-declaration-by-countries-attending-the-ai-safety-summit-1-2-november-2023). _GOV.UK_. 1 November 2023. Archived from [the original](https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration/the-bletchley-declaration-by-countries-attending-the-ai-safety-summit-1-2-november-2023) on 1 November 2023. Retrieved 2 November 2023.
261.  **[^](#cite_ref-277 "Jump up")** ["Countries agree to safe and responsible development of frontier AI in landmark Bletchley Declaration"](https://www.gov.uk/government/news/countries-agree-to-safe-and-responsible-development-of-frontier-ai-in-landmark-bletchley-declaration). _GOV.UK_ (Press release). [Archived](https://web.archive.org/web/20231101115016/https://www.gov.uk/government/news/countries-agree-to-safe-and-responsible-development-of-frontier-ai-in-landmark-bletchley-declaration) from the original on 1 November 2023. Retrieved 1 November 2023.
262.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERussellNorvig20219_278-0) [_**b**_](#cite_ref-FOOTNOTERussellNorvig20219_278-1) [Russell & Norvig 2021](#CITEREFRussellNorvig2021), p. 9.
263.  **[^](#cite_ref-279 "Jump up")** ["Google books ngram"](https://books.google.com/ngrams/graph?content=electronic+brain&year_start=1930&year_end=2019&corpus=en-2019&smoothing=3).
264.  **[^](#cite_ref-281 "Jump up")** AI's immediate precursors:
    
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 51–107)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 27–32)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 8–17)
    *   [Moravec (1988](#CITEREFMoravec1988), p. 3)
    
265.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERussellNorvig202117_282-0) [_**b**_](#cite_ref-FOOTNOTERussellNorvig202117_282-1) [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 17.
266.  ^ [Jump up to: _**a**_](#cite_ref-Turing_test_283-0) [_**b**_](#cite_ref-Turing_test_283-1) Turing's original publication of the [Turing test](https://en.wikipedia.org/wiki/Turing_test "Turing test") in "[Computing machinery and intelligence](https://en.wikipedia.org/wiki/Computing_machinery_and_intelligence "Computing machinery and intelligence")":
    
    *   [Turing (1950)](#CITEREFTuring1950)
    
    Historical influence and philosophical implications:
    
    *   [Haugeland (1985](#CITEREFHaugeland1985), pp. 6–9)
    *   [Crevier (1993](#CITEREFCrevier1993), p. 24)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 70–71)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 2, 984)
    
267.  **[^](#cite_ref-FOOTNOTECrevier199347–49_284-0 "Jump up")** [Crevier (1993)](#CITEREFCrevier1993), pp. 47–49.
268.  **[^](#cite_ref-FOOTNOTERussellNorvig200317_286-0 "Jump up")** [Russell & Norvig (2003)](#CITEREFRussellNorvig2003), p. 17.
269.  **[^](#cite_ref-FOOTNOTERussellNorvig200318_288-0 "Jump up")** [Russell & Norvig (2003)](#CITEREFRussellNorvig2003), p. 18.
270.  **[^](#cite_ref-FOOTNOTENewquist199486–86_291-0 "Jump up")** [Newquist (1994)](#CITEREFNewquist1994), pp. 86–86.
271.  **[^](#cite_ref-292 "Jump up")** [Simon (1965](#CITEREFSimon1965), p. 96) quoted in [Crevier (1993](#CITEREFCrevier1993), p. 109)
272.  **[^](#cite_ref-293 "Jump up")** [Minsky (1967](#CITEREFMinsky1967), p. 2) quoted in [Crevier (1993](#CITEREFCrevier1993), p. 109)
273.  **[^](#cite_ref-FOOTNOTERussellNorvig202121_294-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 21.
274.  **[^](#cite_ref-FOOTNOTELighthill1973_296-0 "Jump up")** [Lighthill (1973)](#CITEREFLighthill1973).
275.  **[^](#cite_ref-FOOTNOTENRC1999212–213_297-0 "Jump up")** [NRC 1999](#CITEREFNRC1999), pp. 212–213.
276.  **[^](#cite_ref-FOOTNOTERussellNorvig202122_298-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 22.
277.  **[^](#cite_ref-299 "Jump up")** [Expert systems](https://en.wikipedia.org/wiki/Expert_systems "Expert systems"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 23, 292)
    *   [Luger & Stubblefield (2004](#CITEREFLugerStubblefield2004), pp. 227–331)
    *   [Nilsson (1998](#CITEREFNilsson1998), chpt. 17.4)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 327–335, 434–435)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 145–162, 197–203)
    *   [Newquist (1994](#CITEREFNewquist1994), pp. 155–183)
    
278.  **[^](#cite_ref-FOOTNOTERussellNorvig202124_300-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 24.
279.  **[^](#cite_ref-FOOTNOTENilsson19987_301-0 "Jump up")** [Nilsson (1998)](#CITEREFNilsson1998), p. 7.
280.  **[^](#cite_ref-FOOTNOTEMcCorduck2004454–462_302-0 "Jump up")** [McCorduck (2004)](#CITEREFMcCorduck2004), pp. 454–462.
281.  **[^](#cite_ref-FOOTNOTEMoravec1988_303-0 "Jump up")** [Moravec (1988)](#CITEREFMoravec1988).
282.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTEBrooks1990_304-0) [_**b**_](#cite_ref-FOOTNOTEBrooks1990_304-1) [Brooks (1990)](#CITEREFBrooks1990).
283.  **[^](#cite_ref-Developmental_robotics_305-0 "Jump up")** [Developmental robotics](https://en.wikipedia.org/wiki/Developmental_robotics "Developmental robotics"):
    
    *   [Weng et al. (2001)](#CITEREFWengMcClellandPentlandSporns2001)
    *   [Lungarella et al. (2003)](#CITEREFLungarellaMettaPfeiferSandini2003)
    *   [Asada et al. (2009)](#CITEREFAsadaHosodaKuniyoshiIshiguro2009)
    *   [Oudeyer (2010)](#CITEREFOudeyer2010)
    
284.  **[^](#cite_ref-FOOTNOTERussellNorvig202125_307-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 25.
285.  **[^](#cite_ref-308 "Jump up")**
    
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 214–215)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 24, 26)
    
286.  **[^](#cite_ref-FOOTNOTERussellNorvig202126_309-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 26.
287.  **[^](#cite_ref-AI_1990s_310-0 "Jump up")** [Formal](#Neat_vs._scruffy) and [narrow](#Narrow_vs._general_AI) methods adopted in the 1990s:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 24–26)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 486–487)
    
288.  **[^](#cite_ref-AI_widely_used_1990s_311-0 "Jump up")** AI widely used in the late 1990s:
    
    *   [Kurzweil (2005](#CITEREFKurzweil2005), p. 265)
    *   [NRC (1999](#CITEREFNRC1999), pp. 216–222)
    *   [Newquist (1994](#CITEREFNewquist1994), pp. 189–201)
    
289.  **[^](#cite_ref-FOOTNOTEWong2023_312-0 "Jump up")** [Wong (2023)](#CITEREFWong2023).
290.  **[^](#cite_ref-Moore's_Law_314-0 "Jump up")** [Moore's Law](https://en.wikipedia.org/wiki/Moore%27s_Law "Moore's Law") and AI:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 14, 27)
    
291.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTEClark2015b_315-0) [_**b**_](#cite_ref-FOOTNOTEClark2015b_315-1) [_**c**_](#cite_ref-FOOTNOTEClark2015b_315-2) [Clark (2015b)](#CITEREFClark2015b).
292.  **[^](#cite_ref-Big_data_316-0 "Jump up")** [Big data](https://en.wikipedia.org/wiki/Big_data "Big data"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 26)
    
293.  **[^](#cite_ref-318 "Jump up")** Sagar, Ram (3 June 2020). ["OpenAI Releases GPT-3, The Largest Model So Far"](https://analyticsindiamag.com/open-ai-gpt-3-language-model/). _Analytics India Magazine_. [Archived](https://web.archive.org/web/20200804173452/https://analyticsindiamag.com/open-ai-gpt-3-language-model/) from the original on 4 August 2020. Retrieved 15 March 2023.
294.  **[^](#cite_ref-FOOTNOTEDiFeliciantonio2023_319-0 "Jump up")** [DiFeliciantonio (2023)](#CITEREFDiFeliciantonio2023).
295.  **[^](#cite_ref-FOOTNOTEGoswami2023_320-0 "Jump up")** [Goswami (2023)](#CITEREFGoswami2023).
296.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTETuring19501_321-0) [_**b**_](#cite_ref-FOOTNOTETuring19501_321-1) [Turing (1950)](#CITEREFTuring1950), p. 1.
297.  **[^](#cite_ref-FOOTNOTETuring1950Under_"The_Argument_from_Consciousness"_322-0 "Jump up")** [Turing (1950)](#CITEREFTuring1950), Under "The Argument from Consciousness".
298.  **[^](#cite_ref-FOOTNOTERussellNorvig20213_323-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 3.
299.  **[^](#cite_ref-FOOTNOTEMaker2006_324-0 "Jump up")** [Maker (2006)](#CITEREFMaker2006).
300.  **[^](#cite_ref-FOOTNOTEMcCarthy1999_325-0 "Jump up")** [McCarthy (1999)](#CITEREFMcCarthy1999).
301.  **[^](#cite_ref-FOOTNOTEMinsky1986_326-0 "Jump up")** [Minsky (1986)](#CITEREFMinsky1986).
302.  **[^](#cite_ref-327 "Jump up")** ["What Is Artificial Intelligence (AI)?"](https://cloud.google.com/learn/what-is-artificial-intelligence). _[Google Cloud Platform](https://en.wikipedia.org/wiki/Google_Cloud_Platform "Google Cloud Platform")_. [Archived](https://web.archive.org/web/20230731114802/https://cloud.google.com/learn/what-is-artificial-intelligence) from the original on 31 July 2023. Retrieved 16 October 2023.
303.  **[^](#cite_ref-FOOTNOTENilsson198310_328-0 "Jump up")** [Nilsson (1983)](#CITEREFNilsson1983), p. 10.
304.  **[^](#cite_ref-FOOTNOTEHaugeland1985112–117_330-0 "Jump up")** [Haugeland (1985)](#CITEREFHaugeland1985), pp. 112–117.
305.  **[^](#cite_ref-Physical_symbol_system_hypothesis_331-0 "Jump up")** Physical symbol system hypothesis:
    
    *   [Newell & Simon (1976](#CITEREFNewellSimon1976), p. 116)
    
    Historical significance:
    
    *   [McCorduck (2004](#CITEREFMcCorduck2004), p. 153)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 19)
    
306.  **[^](#cite_ref-332 "Jump up")** [Moravec's paradox](https://en.wikipedia.org/wiki/Moravec%27s_paradox "Moravec's paradox"):
    
    *   [Moravec (1988](#CITEREFMoravec1988), pp. 15–16)
    *   [Minsky (1986](#CITEREFMinsky1986), p. 29)
    *   [Pinker (2007](#CITEREFPinker2007), pp. 190–191)
    
307.  **[^](#cite_ref-Dreyfus'_critique_333-0 "Jump up")** [Dreyfus' critique of AI](https://en.wikipedia.org/wiki/Dreyfus%27_critique_of_AI "Dreyfus' critique of AI"):
    
    *   [Dreyfus (1972)](#CITEREFDreyfus1972)
    *   [Dreyfus & Dreyfus (1986)](#CITEREFDreyfusDreyfus1986)
    
    Historical significance and philosophical implications:
    
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 120–132)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 211–239)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 981–982)
    *   [Fearn (2007](#CITEREFFearn2007), Chpt. 3)
    
308.  **[^](#cite_ref-FOOTNOTECrevier1993125_334-0 "Jump up")** [Crevier (1993)](#CITEREFCrevier1993), p. 125.
309.  **[^](#cite_ref-FOOTNOTELangley2011_336-0 "Jump up")** [Langley (2011)](#CITEREFLangley2011).
310.  **[^](#cite_ref-FOOTNOTEKatz2012_337-0 "Jump up")** [Katz (2012)](#CITEREFKatz2012).
311.  **[^](#cite_ref-Neats_vs._scruffies_338-0 "Jump up")** [Neats vs. scruffies](https://en.wikipedia.org/wiki/Neats_vs._scruffies "Neats vs. scruffies"), the historic debate:
    
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 421–424, 486–489)
    *   [Crevier (1993](#CITEREFCrevier1993), p. 168)
    *   [Nilsson (1983](#CITEREFNilsson1983), pp. 10–11)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 24)
    
    A classic example of the "scruffy" approach to intelligence:
    
    *   [Minsky (1986)](#CITEREFMinsky1986)
    
    A modern example of neat AI and its aspirations in the 21st century:
    
    *   [Domingos (2015)](#CITEREFDomingos2015)
    
312.  **[^](#cite_ref-FOOTNOTEPennachinGoertzel2007_339-0 "Jump up")** [Pennachin & Goertzel (2007)](#CITEREFPennachinGoertzel2007).
313.  ^ [Jump up to: _**a**_](#cite_ref-FOOTNOTERoberts2016_340-0) [_**b**_](#cite_ref-FOOTNOTERoberts2016_340-1) [Roberts (2016)](#CITEREFRoberts2016).
314.  **[^](#cite_ref-FOOTNOTERussellNorvig2021986_341-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 986.
315.  **[^](#cite_ref-FOOTNOTEChalmers1995_342-0 "Jump up")** [Chalmers (1995)](#CITEREFChalmers1995).
316.  **[^](#cite_ref-FOOTNOTEDennett1991_343-0 "Jump up")** [Dennett (1991)](#CITEREFDennett1991).
317.  **[^](#cite_ref-FOOTNOTEHorst2005_344-0 "Jump up")** [Horst (2005)](#CITEREFHorst2005).
318.  **[^](#cite_ref-FOOTNOTESearle1999_345-0 "Jump up")** [Searle (1999)](#CITEREFSearle1999).
319.  **[^](#cite_ref-FOOTNOTESearle19801_346-0 "Jump up")** [Searle (1980)](#CITEREFSearle1980), p. 1.
320.  **[^](#cite_ref-FOOTNOTERussellNorvig20219817_347-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 9817.
321.  **[^](#cite_ref-Chinese_room_349-0 "Jump up")** Searle's [Chinese room](https://en.wikipedia.org/wiki/Chinese_room "Chinese room") argument:
    
    *   [Searle (1980)](#CITEREFSearle1980). Searle's original presentation of the thought experiment.
    *   [Searle (1999)](#CITEREFSearle1999).
    
    Discussion:
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 985)
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 443–445)
    *   [Crevier (1993](#CITEREFCrevier1993), pp. 269–271)
    
322.  **[^](#cite_ref-350 "Jump up")** Leith, Sam (7 July 2022). ["Nick Bostrom: How can we be certain a machine isn't conscious?"](https://www.spectator.co.uk/article/nick-bostrom-how-can-we-be-certain-a-machine-isnt-conscious/). _The Spectator_. Retrieved 23 February 2024.
323.  ^ [Jump up to: _**a**_](#cite_ref-:02_351-0) [_**b**_](#cite_ref-:02_351-1) [_**c**_](#cite_ref-:02_351-2) Thomson, Jonny (31 October 2022). ["Why don't robots have rights?"](https://bigthink.com/thinking/why-dont-robots-have-rights/). _Big Think_. Retrieved 23 February 2024.
324.  ^ [Jump up to: _**a**_](#cite_ref-:12_352-0) [_**b**_](#cite_ref-:12_352-1) Kateman, Brian (24 July 2023). ["AI Should Be Terrified of Humans"](https://time.com/6296234/ai-should-be-terrified-of-humans/). _Time_. Retrieved 23 February 2024.
325.  **[^](#cite_ref-353 "Jump up")** Wong, Jeff (10 July 2023). ["What leaders need to know about robot rights"](https://www.fastcompany.com/90920769/what-leaders-need-to-know-about-robot-rights). _Fast Company_.
326.  **[^](#cite_ref-354 "Jump up")** Hern, Alex (12 January 2017). ["Give robots 'personhood' status, EU committee argues"](https://www.theguardian.com/technology/2017/jan/12/give-robots-personhood-status-eu-committee-argues). _The Guardian_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0261-3077](https://www.worldcat.org/issn/0261-3077). Retrieved 23 February 2024.
327.  **[^](#cite_ref-355 "Jump up")** Dovey, Dana (14 April 2018). ["Experts Don't Think Robots Should Have Rights"](https://www.newsweek.com/robots-human-rights-electronic-persons-humans-versus-machines-886075). _Newsweek_. Retrieved 23 February 2024.
328.  **[^](#cite_ref-356 "Jump up")** Cuddy, Alice (13 April 2018). ["Robot rights violate human rights, experts warn EU"](https://www.euronews.com/2018/04/13/robot-rights-violate-human-rights-experts-warn-eu). _euronews_. Retrieved 23 February 2024.
329.  **[^](#cite_ref-Singularity_357-0 "Jump up")** The [Intelligence explosion](https://en.wikipedia.org/wiki/Intelligence_explosion "Intelligence explosion") and [technological singularity](https://en.wikipedia.org/wiki/Technological_singularity "Technological singularity"):
    
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), pp. 1004–1005)
    *   [Omohundro (2008)](#CITEREFOmohundro2008)
    *   [Kurzweil (2005)](#CITEREFKurzweil2005)
    
    [I. J. Good](https://en.wikipedia.org/wiki/I._J._Good "I. J. Good")'s "intelligence explosion"
    
    *   [Good (1965)](#CITEREFGood1965)
    
    [Vernor Vinge](https://en.wikipedia.org/wiki/Vernor_Vinge "Vernor Vinge")'s "singularity"
    
    *   [Vinge (1993)](#CITEREFVinge1993)
    
330.  **[^](#cite_ref-FOOTNOTERussellNorvig20211005_358-0 "Jump up")** [Russell & Norvig (2021)](#CITEREFRussellNorvig2021), p. 1005.
331.  **[^](#cite_ref-359 "Jump up")** [Transhumanism](https://en.wikipedia.org/wiki/Transhumanism "Transhumanism"):
    
    *   [Moravec (1988)](#CITEREFMoravec1988)
    *   [Kurzweil (2005)](#CITEREFKurzweil2005)
    *   [Russell & Norvig (2021](#CITEREFRussellNorvig2021), p. 1005)
    
332.  **[^](#cite_ref-360 "Jump up")** AI as evolution:
    
    *   [Edward Fredkin](https://en.wikipedia.org/wiki/Edward_Fredkin "Edward Fredkin") is quoted in [McCorduck (2004](#CITEREFMcCorduck2004), p. 401)
    *   [Butler (1863)](#CITEREFButler1863)
    *   [Dyson (1998)](#CITEREFDyson1998)
    
333.  **[^](#cite_ref-AI_in_myth_361-0 "Jump up")** AI in myth:
    
    *   [McCorduck (2004](#CITEREFMcCorduck2004), pp. 4–5)
    
334.  **[^](#cite_ref-FOOTNOTEMcCorduck2004340–400_362-0 "Jump up")** [McCorduck (2004)](#CITEREFMcCorduck2004), pp. 340–400.
335.  **[^](#cite_ref-FOOTNOTEButtazzo2001_363-0 "Jump up")** [Buttazzo (2001)](#CITEREFButtazzo2001).
336.  **[^](#cite_ref-FOOTNOTEAnderson2008_364-0 "Jump up")** [Anderson (2008)](#CITEREFAnderson2008).
337.  **[^](#cite_ref-FOOTNOTEMcCauley2007_365-0 "Jump up")** [McCauley (2007)](#CITEREFMcCauley2007).
338.  **[^](#cite_ref-FOOTNOTEGalvan1997_366-0 "Jump up")** [Galvan (1997)](#CITEREFGalvan1997).

### AI textbooks

The two most widely used textbooks in 2023. (See the [Open Syllabus](https://explorer.opensyllabus.org/result/field?id=Computer+Science)).

*   [Russell, Stuart J.](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell"); [Norvig, Peter.](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") (2021). _[Artificial Intelligence: A Modern Approach](https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Modern_Approach "Artificial Intelligence: A Modern Approach")_ (4th ed.). Hoboken: Pearson. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0134610993](https://en.wikipedia.org/wiki/Special:BookSources/978-0134610993 "Special:BookSources/978-0134610993"). [LCCN](https://en.wikipedia.org/wiki/LCCN_\(identifier\) "LCCN (identifier)") [20190474](https://lccn.loc.gov/20190474).
*   [Rich, Elaine](https://en.wikipedia.org/wiki/Elaine_Rich "Elaine Rich"); Knight, Kevin; Nair, Shivashankar B (2010). _Artificial Intelligence_ (3rd ed.). New Delhi: Tata McGraw Hill India. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0070087705](https://en.wikipedia.org/wiki/Special:BookSources/978-0070087705 "Special:BookSources/978-0070087705").

These were the four of the most widely used AI textbooks in 2008:

*   [Luger, George](https://en.wikipedia.org/w/index.php?title=George_Luger&action=edit&redlink=1 "George Luger (page does not exist)"); [Stubblefield, William](https://en.wikipedia.org/wiki/William_Stubblefield "William Stubblefield") (2004). [_Artificial Intelligence: Structures and Strategies for Complex Problem Solving_](https://archive.org/details/artificialintell0000luge) (5th ed.). Benjamin/Cummings. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-8053-4780-7](https://en.wikipedia.org/wiki/Special:BookSources/978-0-8053-4780-7 "Special:BookSources/978-0-8053-4780-7"). [Archived](https://web.archive.org/web/20200726220613/https://archive.org/details/artificialintell0000luge) from the original on 26 July 2020. Retrieved 17 December 2019.
*   [Nilsson, Nils](https://en.wikipedia.org/wiki/Nils_Nilsson_\(researcher\) "Nils Nilsson (researcher)") (1998). [_Artificial Intelligence: A New Synthesis_](https://archive.org/details/artificialintell0000nils). Morgan Kaufmann. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-55860-467-4](https://en.wikipedia.org/wiki/Special:BookSources/978-1-55860-467-4 "Special:BookSources/978-1-55860-467-4"). [Archived](https://web.archive.org/web/20200726131654/https://archive.org/details/artificialintell0000nils) from the original on 26 July 2020. Retrieved 18 November 2019.
*   [Russell, Stuart J.](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell"); [Norvig, Peter](https://en.wikipedia.org/wiki/Peter_Norvig "Peter Norvig") (2003), [_Artificial Intelligence: A Modern Approach_](http://aima.cs.berkeley.edu/) (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [0-13-790395-2](https://en.wikipedia.org/wiki/Special:BookSources/0-13-790395-2 "Special:BookSources/0-13-790395-2").
*   [Poole, David](https://en.wikipedia.org/w/index.php?title=David_Poole_\(researcher\)&action=edit&redlink=1 "David Poole (researcher) (page does not exist)"); [Mackworth, Alan](https://en.wikipedia.org/wiki/Alan_Mackworth "Alan Mackworth"); [Goebel, Randy](https://en.wikipedia.org/w/index.php?title=Randy_Goebel&action=edit&redlink=1 "Randy Goebel (page does not exist)") (1998). [_Computational Intelligence: A Logical Approach_](https://archive.org/details/computationalint00pool). New York: Oxford University Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-19-510270-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-19-510270-3 "Special:BookSources/978-0-19-510270-3"). [Archived](https://web.archive.org/web/20200726131436/https://archive.org/details/computationalint00pool) from the original on 26 July 2020. Retrieved 22 August 2020.

Later editions.

*   Poole, David; [Mackworth, Alan](https://en.wikipedia.org/wiki/Alan_Mackworth "Alan Mackworth") (2017). [_Artificial Intelligence: Foundations of Computational Agents_](http://artint.info/index.html) (2nd ed.). Cambridge University Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-107-19539-4](https://en.wikipedia.org/wiki/Special:BookSources/978-1-107-19539-4 "Special:BookSources/978-1-107-19539-4"). [Archived](https://web.archive.org/web/20171207013855/http://artint.info/index.html) from the original on 7 December 2017. Retrieved 6 December 2017.

### History of AI

*   [Crevier, Daniel](https://en.wikipedia.org/wiki/Daniel_Crevier "Daniel Crevier") (1993). _AI: The Tumultuous Search for Artificial Intelligence_. New York, NY: BasicBooks. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [0-465-02997-3](https://en.wikipedia.org/wiki/Special:BookSources/0-465-02997-3 "Special:BookSources/0-465-02997-3")..
*   [McCorduck, Pamela](https://en.wikipedia.org/wiki/Pamela_McCorduck "Pamela McCorduck") (2004), _Machines Who Think_ (2nd ed.), Natick, MA: A. K. Peters, Ltd., [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [1-56881-205-1](https://en.wikipedia.org/wiki/Special:BookSources/1-56881-205-1 "Special:BookSources/1-56881-205-1").
*   [Newquist, H. P.](https://en.wikipedia.org/wiki/HP_Newquist "HP Newquist") (1994). _The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think_. New York: Macmillan/SAMS. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-672-30412-5](https://en.wikipedia.org/wiki/Special:BookSources/978-0-672-30412-5 "Special:BookSources/978-0-672-30412-5").

### Other sources

*   [AI & ML in Fusion](https://suli.pppl.gov/2023/course/Rea-PPPL-SULI2023.pdf)
*   [AI & ML in Fusion, video lecture](https://drive.google.com/file/d/1npCTrJ8XJn20ZGDA_DfMpANuQZFMzKPh/view?usp=drive_link) [Archived](https://web.archive.org/web/20230702164332/https://drive.google.com/file/d/1npCTrJ8XJn20ZGDA_DfMpANuQZFMzKPh/view?usp=drive_link) 2 July 2023 at the [Wayback Machine](https://en.wikipedia.org/wiki/Wayback_Machine "Wayback Machine")
*   ["AlphaGo – Google DeepMind"](https://wildoftech.com/alphago-google-deepmind). [Archived](https://web.archive.org/web/20160310191926/https://wildoftech.com/alphago-google-deepmind/) from the original on 10 March 2016.
*   Alter, Alexandra; Harris, Elizabeth A. (20 September 2023), ["Franzen, Grisham and Other Prominent Authors Sue OpenAI"](https://www.nytimes.com/2023/09/20/books/authors-openai-lawsuit-chatgpt-copyright.html?campaign_id=2&emc=edit_th_20230921&instance_id=103259&nl=todaysheadlines&regi_id=62816440&segment_id=145288&user_id=ad24f3545dae0ec44284a38bb4a88f1d), _The New York Times_
*   [Altman, Sam](https://en.wikipedia.org/wiki/Sam_Altman "Sam Altman"); [Brockman, Greg](https://en.wikipedia.org/wiki/Greg_Brockman "Greg Brockman"); [Sutskever, Ilya](https://en.wikipedia.org/wiki/Ilya_Sutskever "Ilya Sutskever") (22 May 2023). ["Governance of Superintelligence"](https://openai.com/blog/governance-of-superintelligence). _openai.com_. [Archived](https://web.archive.org/web/20230527061619/https://openai.com/blog/governance-of-superintelligence) from the original on 27 May 2023. Retrieved 27 May 2023.
*   Anderson, Susan Leigh (2008). "Asimov's "three laws of robotics" and machine metaethics". _AI & Society_. **22** (4): 477–493. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/s00146-007-0094-5](https://doi.org/10.1007%2Fs00146-007-0094-5). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [1809459](https://api.semanticscholar.org/CorpusID:1809459).
*   Anderson, Michael; Anderson, Susan Leigh (2011). _Machine Ethics_. Cambridge University Press.
*   Arntz, Melanie; Gregory, Terry; Zierahn, Ulrich (2016), "The risk of automation for jobs in OECD countries: A comparative analysis", _OECD Social, Employment, and Migration Working Papers 189_
*   Asada, M.; Hosoda, K.; Kuniyoshi, Y.; Ishiguro, H.; Inui, T.; Yoshikawa, Y.; Ogino, M.; Yoshida, C. (2009). "Cognitive developmental robotics: a survey". _IEEE Transactions on Autonomous Mental Development_. **1** (1): 12–34. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/tamd.2009.2021702](https://doi.org/10.1109%2Ftamd.2009.2021702). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [10168773](https://api.semanticscholar.org/CorpusID:10168773).
*   ["Ask the AI experts: What's driving today's progress in AI?"](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai). _McKinsey & Company_. [Archived](https://web.archive.org/web/20180413190018/https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai) from the original on 13 April 2018. Retrieved 13 April 2018.
*   Barfield, Woodrow; Pagallo, Ugo (2018). _Research handbook on the law of artificial intelligence_. Cheltenham, UK: Edward Elgar Publishing. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-78643-904-8](https://en.wikipedia.org/wiki/Special:BookSources/978-1-78643-904-8 "Special:BookSources/978-1-78643-904-8"). [OCLC](https://en.wikipedia.org/wiki/OCLC_\(identifier\) "OCLC (identifier)") [1039480085](https://www.worldcat.org/oclc/1039480085).
*   Beal, J.; [Winston, Patrick](https://en.wikipedia.org/wiki/Patrick_Winston "Patrick Winston") (2009), "The New Frontier of Human-Level Artificial Intelligence", _IEEE Intelligent Systems_, **24**: 21–24, [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/MIS.2009.75](https://doi.org/10.1109%2FMIS.2009.75), [hdl](https://en.wikipedia.org/wiki/Hdl_\(identifier\) "Hdl (identifier)"):[1721.1/52357](https://hdl.handle.net/1721.1%2F52357), [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [32437713](https://api.semanticscholar.org/CorpusID:32437713)
*   Berdahl, Carl Thomas; Baker, Lawrence; Mann, Sean; Osoba, Osonde; Girosi, Federico (7 February 2023). ["Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041459). _JMIR AI_. **2**: e42936. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.2196/42936](https://doi.org/10.2196%2F42936). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [2817-1705](https://www.worldcat.org/issn/2817-1705). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [11041459](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041459). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [256681439](https://api.semanticscholar.org/CorpusID:256681439).
*   [Berlinski, David](https://en.wikipedia.org/wiki/David_Berlinski "David Berlinski") (2000). [_The Advent of the Algorithm_](https://archive.org/details/adventofalgorith0000berl). Harcourt Books. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-15-601391-8](https://en.wikipedia.org/wiki/Special:BookSources/978-0-15-601391-8 "Special:BookSources/978-0-15-601391-8"). [OCLC](https://en.wikipedia.org/wiki/OCLC_\(identifier\) "OCLC (identifier)") [46890682](https://www.worldcat.org/oclc/46890682). [Archived](https://web.archive.org/web/20200726215744/https://archive.org/details/adventofalgorith0000berl) from the original on 26 July 2020. Retrieved 22 August 2020.
*   Berryhill, Jamie; Heang, Kévin Kok; Clogher, Rob; McBride, Keegan (2019). [_Hello, World: Artificial Intelligence and its Use in the Public Sector_](https://oecd-opsi.org/wp-content/uploads/2019/11/AI-Report-Online.pdf) (PDF). Paris: OECD Observatory of Public Sector Innovation. [Archived](https://web.archive.org/web/20191220021331/https://oecd-opsi.org/wp-content/uploads/2019/11/AI-Report-Online.pdf) (PDF) from the original on 20 December 2019. Retrieved 9 August 2020.
*   Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". _MM '06 Proceedings of the 14th ACM international conference on Multimedia_. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682.
*   [Bostrom, Nick](https://en.wikipedia.org/wiki/Nick_Bostrom "Nick Bostrom") (2014). [_Superintelligence: Paths, Dangers, Strategies_](https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies "Superintelligence: Paths, Dangers, Strategies"). Oxford University Press.
*   Bostrom, Nick (2015). ["What happens when our computers get smarter than we are?"](https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are/transcript). [TED (conference)](https://en.wikipedia.org/wiki/TED_\(conference\) "TED (conference)"). [Archived](https://web.archive.org/web/20200725005719/https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are/transcript) from the original on 25 July 2020. Retrieved 30 January 2020.
*   Brooks, Rodney (10 November 2014). ["artificial intelligence is a tool, not a threat"](https://web.archive.org/web/20141112130954/http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/). Archived from [the original](http://www.rethinkrobotics.com/artificial-intelligence-tool-threat/) on 12 November 2014.
*   [Brooks, Rodney](https://en.wikipedia.org/wiki/Rodney_Brooks "Rodney Brooks") (1990). ["Elephants Don't Play Chess"](http://people.csail.mit.edu/brooks/papers/elephants.pdf) (PDF). _Robotics and Autonomous Systems_. **6** (1–2): 3–15. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10.1.1.588.7539](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.588.7539). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/S0921-8890(05)80025-9](https://doi.org/10.1016%2FS0921-8890%2805%2980025-9). [Archived](https://web.archive.org/web/20070809020912/http://people.csail.mit.edu/brooks/papers/elephants.pdf) (PDF) from the original on 9 August 2007.
*   Buiten, Miriam C (2019). ["Towards Intelligent Regulation of Artificial Intelligence"](https://doi.org/10.1017%2Ferr.2019.8). _European Journal of Risk Regulation_. **10** (1): 41–59. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1017/err.2019.8](https://doi.org/10.1017%2Ferr.2019.8). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1867-299X](https://www.worldcat.org/issn/1867-299X).
*   Bushwick, Sophie (16 March 2023), ["What the New GPT-4 AI Can Do"](https://www.scientificamerican.com/article/what-the-new-gpt-4-ai-can-do/), _Scientific American_
*   [Butler, Samuel](https://en.wikipedia.org/wiki/Samuel_Butler_\(novelist\) "Samuel Butler (novelist)") (13 June 1863). ["Darwin among the Machines"](https://nzetc.victoria.ac.nz/tm/scholarly/tei-ButFir-t1-g1-t1-g1-t4-body.html). Letters to the Editor. _[The Press](https://en.wikipedia.org/wiki/The_Press "The Press")_. Christchurch, New Zealand. [Archived](https://web.archive.org/web/20080919172551/http://www.nzetc.org/tm/scholarly/tei-ButFir-t1-g1-t1-g1-t4-body.html) from the original on 19 September 2008. Retrieved 16 October 2014 – via Victoria University of Wellington.
*   Buttazzo, G. (July 2001). "Artificial consciousness: Utopia or real possibility?". _[Computer](https://en.wikipedia.org/wiki/Computer_\(magazine\) "Computer (magazine)")_. **34** (7): 24–30. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/2.933500](https://doi.org/10.1109%2F2.933500).
*   Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research \[Review Article\]". _IEEE Computational Intelligence Magazine_. **9** (2): 48–57. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/MCI.2014.2307227](https://doi.org/10.1109%2FMCI.2014.2307227). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [206451986](https://api.semanticscholar.org/CorpusID:206451986).
*   Cellan-Jones, Rory (2 December 2014). ["Stephen Hawking warns artificial intelligence could end mankind"](https://www.bbc.com/news/technology-30290540). _[BBC News](https://en.wikipedia.org/wiki/BBC_News "BBC News")_. [Archived](https://web.archive.org/web/20151030054329/http://www.bbc.com/news/technology-30290540) from the original on 30 October 2015. Retrieved 30 October 2015.
*   [Chalmers, David](https://en.wikipedia.org/wiki/David_Chalmers "David Chalmers") (1995). ["Facing up to the problem of consciousness"](http://www.imprint.co.uk/chalmers.html). _[Journal of Consciousness Studies](https://en.wikipedia.org/wiki/Journal_of_Consciousness_Studies "Journal of Consciousness Studies")_. **2** (3): 200–219. [Archived](https://web.archive.org/web/20050308163649/http://www.imprint.co.uk/chalmers.html) from the original on 8 March 2005. Retrieved 11 October 2018.
*   [Christian, Brian](https://en.wikipedia.org/wiki/Brian_Christian "Brian Christian") (2020). _[The Alignment Problem](https://en.wikipedia.org/wiki/The_Alignment_Problem "The Alignment Problem"): Machine learning and human values_. W. W. Norton & Company. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-393-86833-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-393-86833-3 "Special:BookSources/978-0-393-86833-3"). [OCLC](https://en.wikipedia.org/wiki/OCLC_\(identifier\) "OCLC (identifier)") [1233266753](https://www.worldcat.org/oclc/1233266753).
*   Ciresan, D.; Meier, U.; Schmidhuber, J. (2012). "Multi-column deep neural networks for image classification". _2012 IEEE Conference on Computer Vision and Pattern Recognition_. pp. 3642–3649. [arXiv](https://en.wikipedia.org/wiki/ArXiv_\(identifier\) "ArXiv (identifier)"):[1202.2745](https://arxiv.org/abs/1202.2745). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/cvpr.2012.6248110](https://doi.org/10.1109%2Fcvpr.2012.6248110). [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-4673-1228-8](https://en.wikipedia.org/wiki/Special:BookSources/978-1-4673-1228-8 "Special:BookSources/978-1-4673-1228-8"). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [2161592](https://api.semanticscholar.org/CorpusID:2161592).
*   Clark, Jack (2015b). ["Why 2015 Was a Breakthrough Year in Artificial Intelligence"](https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence). _Bloomberg.com_. [Archived](https://web.archive.org/web/20161123053855/https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence) from the original on 23 November 2016. Retrieved 23 November 2016.
*   CNA (12 January 2019). ["Commentary: Bad news. Artificial intelligence is biased"](https://www.channelnewsasia.com/news/commentary/artificial-intelligence-big-data-bias-hiring-loans-key-challenge-11097374). _CNA_. [Archived](https://web.archive.org/web/20190112104421/https://www.channelnewsasia.com/news/commentary/artificial-intelligence-big-data-bias-hiring-loans-key-challenge-11097374) from the original on 12 January 2019. Retrieved 19 June 2020.
*   [Cybenko, G.](https://en.wikipedia.org/wiki/George_Cybenko "George Cybenko") (1988). Continuous valued neural networks with two hidden layers are sufficient (Report). Department of Computer Science, Tufts University.
*   Deng, L.; Yu, D. (2014). ["Deep Learning: Methods and Applications"](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) (PDF). _Foundations and Trends in Signal Processing_. **7** (3–4): 1–199. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1561/2000000039](https://doi.org/10.1561%2F2000000039). [Archived](https://web.archive.org/web/20160314152112/http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) (PDF) from the original on 14 March 2016. Retrieved 18 October 2014.
*   [Dennett, Daniel](https://en.wikipedia.org/wiki/Daniel_Dennett "Daniel Dennett") (1991). [_Consciousness Explained_](https://en.wikipedia.org/wiki/Consciousness_Explained "Consciousness Explained"). The Penguin Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-7139-9037-9](https://en.wikipedia.org/wiki/Special:BookSources/978-0-7139-9037-9 "Special:BookSources/978-0-7139-9037-9").
*   DiFeliciantonio, Chase (3 April 2023). ["AI has already changed the world. This report shows how"](https://www.sfchronicle.com/tech/article/ai-artificial-intelligence-report-stanford-17869558.php). _San Francisco Chronicle_. [Archived](https://web.archive.org/web/20230619015309/https://www.sfchronicle.com/tech/article/ai-artificial-intelligence-report-stanford-17869558.php) from the original on 19 June 2023. Retrieved 19 June 2023.
*   Dickson, Ben (2 May 2022). ["Machine learning: What is the transformer architecture?"](https://bdtechtalks.com/2022/05/02/what-is-the-transformer/). _TechTalks_. Retrieved 22 November 2023.
*   Dockrill, Peter (27 June 2022), ["Robots With Flawed AI Make Sexist And Racist Decisions, Experiment Shows"](https://web.archive.org/web/20220627225827/https://www.sciencealert.com/robots-with-flawed-ai-make-sexist-racist-and-toxic-decisions-experiment-shows), _Science Alert_, archived from [the original](https://www.sciencealert.com/robots-with-flawed-ai-make-sexist-racist-and-toxic-decisions-experiment-shows) on 27 June 2022
*   [Domingos, Pedro](https://en.wikipedia.org/wiki/Pedro_Domingos "Pedro Domingos") (2015). _The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World_. [Basic Books](https://en.wikipedia.org/wiki/Basic_Books "Basic Books"). [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0465065707](https://en.wikipedia.org/wiki/Special:BookSources/978-0465065707 "Special:BookSources/978-0465065707").
*   [Dreyfus, Hubert](https://en.wikipedia.org/wiki/Hubert_Dreyfus "Hubert Dreyfus") (1972). [_What Computers Can't Do_](https://en.wikipedia.org/wiki/What_Computers_Can%27t_Do "What Computers Can't Do"). New York: MIT Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-06-011082-6](https://en.wikipedia.org/wiki/Special:BookSources/978-0-06-011082-6 "Special:BookSources/978-0-06-011082-6").
*   [Dreyfus, Hubert](https://en.wikipedia.org/wiki/Hubert_Dreyfus "Hubert Dreyfus"); Dreyfus, Stuart (1986). [_Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer_](https://archive.org/details/mindovermachinep00drey). Oxford: Blackwell. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-02-908060-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-02-908060-3 "Special:BookSources/978-0-02-908060-3"). [Archived](https://web.archive.org/web/20200726131414/https://archive.org/details/mindovermachinep00drey) from the original on 26 July 2020. Retrieved 22 August 2020.
*   [Dyson, George](https://en.wikipedia.org/wiki/George_Dyson_\(science_historian\) "George Dyson (science historian)") (1998). [_Darwin among the Machines_](https://archive.org/details/darwinamongmachi00dyso). Allan Lane Science. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-7382-0030-9](https://en.wikipedia.org/wiki/Special:BookSources/978-0-7382-0030-9 "Special:BookSources/978-0-7382-0030-9"). [Archived](https://web.archive.org/web/20200726131443/https://archive.org/details/darwinamongmachi00dyso) from the original on 26 July 2020. Retrieved 22 August 2020.
*   Edelson, Edward (1991). [_The Nervous System_](https://archive.org/details/nervoussystem0000edel). New York: Chelsea House. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-7910-0464-7](https://en.wikipedia.org/wiki/Special:BookSources/978-0-7910-0464-7 "Special:BookSources/978-0-7910-0464-7"). [Archived](https://web.archive.org/web/20200726131758/https://archive.org/details/nervoussystem0000edel) from the original on 26 July 2020. Retrieved 18 November 2019.
*   Edwards, Benj (17 May 2023). ["Poll: AI poses risk to humanity, according to majority of Americans"](https://arstechnica.com/information-technology/2023/05/poll-61-of-americans-say-ai-threatens-humanitys-future/). _Ars Technica_. [Archived](https://web.archive.org/web/20230619013608/https://arstechnica.com/information-technology/2023/05/poll-61-of-americans-say-ai-threatens-humanitys-future/) from the original on 19 June 2023. Retrieved 19 June 2023.
*   [Evans, Woody](https://en.wikipedia.org/wiki/Woody_Evans "Woody Evans") (2015). ["Posthuman Rights: Dimensions of Transhuman Worlds"](https://doi.org/10.5209%2Frev_TK.2015.v12.n2.49072). _Teknokultura_. **12** (2). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.5209/rev\_TK.2015.v12.n2.49072](https://doi.org/10.5209%2Frev_TK.2015.v12.n2.49072).
*   Fearn, Nicholas (2007). _The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers_. New York: Grove Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-8021-1839-4](https://en.wikipedia.org/wiki/Special:BookSources/978-0-8021-1839-4 "Special:BookSources/978-0-8021-1839-4").
*   Ford, Martin; Colvin, Geoff (6 September 2015). ["Will robots create more jobs than they destroy?"](https://www.theguardian.com/technology/2015/sep/06/will-robots-create-destroy-jobs). _The Guardian_. [Archived](https://web.archive.org/web/20180616204119/https://www.theguardian.com/technology/2015/sep/06/will-robots-create-destroy-jobs) from the original on 16 June 2018. Retrieved 13 January 2018.
*   Fox News (2023). ["Fox News Poll"](https://static.foxnews.com/foxnews.com/content/uploads/2023/05/Fox_April-21-24-2023_Complete_National_Topline_May-1-Release.pdf) (PDF). Fox News. [Archived](https://web.archive.org/web/20230512082712/https://static.foxnews.com/foxnews.com/content/uploads/2023/05/Fox_April-21-24-2023_Complete_National_Topline_May-1-Release.pdf) (PDF) from the original on 12 May 2023. Retrieved 19 June 2023.
*   Frank, Michael (22 September 2023). ["US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order"](https://thediplomat.com/2023/09/us-leadership-in-artificial-intelligence-can-shape-the-21st-century-global-order/). _[The Diplomat](https://en.wikipedia.org/wiki/The_Diplomat "The Diplomat")_. Retrieved 8 December 2023. Instead, the United States has developed a new area of dominance that the rest of the world views with a mixture of awe, envy, and resentment: artificial intelligence... From AI models and research to cloud computing and venture capital, U.S. companies, universities, and research labs – and their affiliates in allied countries – appear to have an enormous lead in both developing cutting-edge AI and commercializing it. The value of U.S. venture capital investments in AI start-ups exceeds that of the rest of the world combined.
*   Frey, Carl Benedikt; Osborne, Michael A (1 January 2017). "The future of employment: How susceptible are jobs to computerisation?". _Technological Forecasting and Social Change_. **114**: 254–280. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10.1.1.395.416](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.416). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/j.techfore.2016.08.019](https://doi.org/10.1016%2Fj.techfore.2016.08.019). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0040-1625](https://www.worldcat.org/issn/0040-1625).
*   ["From not working to neural networking"](https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not). _The Economist_. 2016. [Archived](https://web.archive.org/web/20161231203934/https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not) from the original on 31 December 2016. Retrieved 26 April 2018.
*   Galvan, Jill (1 January 1997). "Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?"". _Science Fiction Studies_. **24** (3): 413–429. [JSTOR](https://en.wikipedia.org/wiki/JSTOR_\(identifier\) "JSTOR (identifier)") [4240644](https://www.jstor.org/stable/4240644).
*   Geist, Edward Moore (9 August 2015). ["Is artificial intelligence really an existential threat to humanity?"](http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577). _Bulletin of the Atomic Scientists_. [Archived](https://web.archive.org/web/20151030054330/http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577) from the original on 30 October 2015. Retrieved 30 October 2015.
*   [Gertner, Jon](https://en.wikipedia.org/w/index.php?title=Jon_Gartner&action=edit&redlink=1 "Jon Gartner (page does not exist)") (18 July 2023). ["Wikipedia's Moment of Truth – Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process? + comment"](https://www.nytimes.com/2023/07/18/magazine/wikipedia-ai-chatgpt.html). _[The New York Times](https://en.wikipedia.org/wiki/The_New_York_Times "The New York Times")_. [Archived](https://web.archive.org/web/20230718233916/https://www.nytimes.com/2023/07/18/magazine/wikipedia-ai-chatgpt.html#permid=126389255) from the original on 18 July 2023. Retrieved 19 July 2023.
*   Gibbs, Samuel (27 October 2014). ["Elon Musk: artificial intelligence is our biggest existential threat"](https://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat). _The Guardian_. [Archived](https://web.archive.org/web/20151030054330/http://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat) from the original on 30 October 2015. Retrieved 30 October 2015.
*   Goffrey, Andrew (2008). "Algorithm". In Fuller, Matthew (ed.). [_Software studies: a lexicon_](https://archive.org/details/softwarestudiesl00full_007). Cambridge, Mass.: MIT Press. pp. [15](https://archive.org/details/softwarestudiesl00full_007/page/n29)–20. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-4356-4787-9](https://en.wikipedia.org/wiki/Special:BookSources/978-1-4356-4787-9 "Special:BookSources/978-1-4356-4787-9").
*   Goldman, Sharon (14 September 2022). ["10 years later, deep learning 'revolution' rages on, say AI pioneers Hinton, LeCun and Li"](https://venturebeat.com/ai/10-years-on-ai-pioneers-hinton-lecun-li-say-deep-learning-revolution-will-continue/). _VentureBeat_. Retrieved 8 December 2023.
*   [Good, I. J.](https://en.wikipedia.org/wiki/I._J._Good "I. J. Good") (1965), [_Speculations Concerning the First Ultraintelligent Machine_](https://exhibits.stanford.edu/feigenbaum/catalog/gz727rg3869)
*   Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016), [_Deep Learning_](https://web.archive.org/web/20160416111010/http://www.deeplearningbook.org/), MIT Press., archived from [the original](http://www.deeplearningbook.org/) on 16 April 2016, retrieved 12 November 2017
*   Goodman, Bryce; Flaxman, Seth (2017). "EU regulations on algorithmic decision-making and a 'right to explanation'". _AI Magazine_. **38** (3): 50. [arXiv](https://en.wikipedia.org/wiki/ArXiv_\(identifier\) "ArXiv (identifier)"):[1606.08813](https://arxiv.org/abs/1606.08813). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1609/aimag.v38i3.2741](https://doi.org/10.1609%2Faimag.v38i3.2741). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [7373959](https://api.semanticscholar.org/CorpusID:7373959).
*   [Government Accountability Office](https://en.wikipedia.org/wiki/Government_Accountability_Office "Government Accountability Office") (13 September 2022). [Consumer Data: Increasing Use Poses Risks to Privacy](https://www.gao.gov/products/gao-22-106096). _gao.gov_ (Report).
*   Grant, Nico; Hill, Kashmir (22 May 2023). ["Google's Photo App Still Can't Find Gorillas. And Neither Can Apple's"](https://www.nytimes.com/2023/05/22/technology/ai-photo-labels-google-apple.html). _The New York Times_.
*   Goswami, Rohan (5 April 2023). ["Here's where the A.I. jobs are"](https://www.cnbc.com/2023/04/05/ai-jobs-see-the-state-by-state-data-from-a-stanford-study.html). _CNBC_. [Archived](https://web.archive.org/web/20230619015309/https://www.cnbc.com/2023/04/05/ai-jobs-see-the-state-by-state-data-from-a-stanford-study.html) from the original on 19 June 2023. Retrieved 19 June 2023.
*   [Harari, Yuval Noah](https://en.wikipedia.org/wiki/Yuval_Noah_Harari "Yuval Noah Harari") (October 2018). ["Why Technology Favors Tyranny"](https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330). _[The Atlantic](https://en.wikipedia.org/wiki/The_Atlantic "The Atlantic")_. [Archived](https://web.archive.org/web/20210925221449/https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/) from the original on 25 September 2021. Retrieved 23 September 2021.
*   Harari, Yuval Noah (2023). ["AI and the future of humanity"](https://www.youtube.com/watch?v=LWiM-LuRe6w). _[YouTube](https://en.wikipedia.org/wiki/YouTube "YouTube")_.
*   [Haugeland, John](https://en.wikipedia.org/wiki/John_Haugeland "John Haugeland") (1985). _Artificial Intelligence: The Very Idea_. Cambridge, Mass.: MIT Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-262-08153-5](https://en.wikipedia.org/wiki/Special:BookSources/978-0-262-08153-5 "Special:BookSources/978-0-262-08153-5").
*   Henderson, Mark (24 April 2007). ["Human rights for robots? We're getting carried away"](http://www.thetimes.co.uk/tto/technology/article1966391.ece). _The Times Online_. London. [Archived](https://web.archive.org/web/20140531104850/http://www.thetimes.co.uk/tto/technology/article1966391.ece) from the original on 31 May 2014. Retrieved 31 May 2014.
*   Hinton, G.; Deng, L.; Yu, D.; Dahl, G.; Mohamed, A.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; [Sainath, T.](https://en.wikipedia.org/wiki/Tara_Sainath "Tara Sainath"); Kingsbury, B. (2012). "Deep Neural Networks for Acoustic Modeling in Speech Recognition – The shared views of four research groups". _IEEE Signal Processing Magazine_. **29** (6): 82–97. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2012ISPM...29...82H](https://ui.adsabs.harvard.edu/abs/2012ISPM...29...82H). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/msp.2012.2205597](https://doi.org/10.1109%2Fmsp.2012.2205597). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [206485943](https://api.semanticscholar.org/CorpusID:206485943).
*   Holley, Peter (28 January 2015). ["Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned'"](https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/). _The Washington Post_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0190-8286](https://www.worldcat.org/issn/0190-8286). [Archived](https://web.archive.org/web/20151030054330/https://www.washingtonpost.com/news/the-switch/wp/2015/01/28/bill-gates-on-dangers-of-artificial-intelligence-dont-understand-why-some-people-are-not-concerned/) from the original on 30 October 2015. Retrieved 30 October 2015.
*   Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert (1989). [_Multilayer Feedforward Networks are Universal Approximators_](http://cognitivemedium.com/magic_paper/assets/Hornik.pdf) (PDF). _Neural Networks_. Vol. 2. Pergamon Press. pp. 359–366.
*   Horst, Steven (2005). ["The Computational Theory of Mind"](http://plato.stanford.edu/entries/computational-mind). _The Stanford Encyclopedia of Philosophy_. [Archived](https://web.archive.org/web/20160306083748/http://plato.stanford.edu/entries/computational-mind/) from the original on 6 March 2016. Retrieved 7 March 2016.
*   Howe, J. (November 1994). ["Artificial Intelligence at Edinburgh University: a Perspective"](http://www.inf.ed.ac.uk/about/AIhistory.html). [Archived](https://web.archive.org/web/20070515072641/http://www.inf.ed.ac.uk/about/AIhistory.html) from the original on 15 May 2007. Retrieved 30 August 2007.
*   IGM Chicago (30 June 2017). ["Robots and Artificial Intelligence"](http://www.igmchicago.org/surveys/robots-and-artificial-intelligence). _www.igmchicago.org_. [Archived](https://web.archive.org/web/20190501114826/http://www.igmchicago.org/surveys/robots-and-artificial-intelligence) from the original on 1 May 2019. Retrieved 3 July 2019.
*   Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". _Contemporary Social Science_. **16** (2): 170–184. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1080/21582041.2018.1563803](https://doi.org/10.1080%2F21582041.2018.1563803). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [2158-2041](https://www.worldcat.org/issn/2158-2041). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [59298502](https://api.semanticscholar.org/CorpusID:59298502).
*   Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". _Science_. **349** (6245): 255–260. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2015Sci...349..255J](https://ui.adsabs.harvard.edu/abs/2015Sci...349..255J). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1126/science.aaa8415](https://doi.org/10.1126%2Fscience.aaa8415). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [26185243](https://pubmed.ncbi.nlm.nih.gov/26185243). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [677218](https://api.semanticscholar.org/CorpusID:677218).
*   [Kahneman, Daniel](https://en.wikipedia.org/wiki/Daniel_Kahneman "Daniel Kahneman") (2011). [_Thinking, Fast and Slow_](https://books.google.com/books?id=ZuKTvERuPG8C). Macmillan. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-4299-6935-2](https://en.wikipedia.org/wiki/Special:BookSources/978-1-4299-6935-2 "Special:BookSources/978-1-4299-6935-2"). [Archived](https://web.archive.org/web/20230315191803/https://books.google.com/books?id=ZuKTvERuPG8C) from the original on 15 March 2023. Retrieved 8 April 2012.
*   [Kahneman, Daniel](https://en.wikipedia.org/wiki/Daniel_Kahneman "Daniel Kahneman"); Slovic, D.; [Tversky, Amos](https://en.wikipedia.org/wiki/Amos_Tversky "Amos Tversky") (1982). "Judgment under uncertainty: Heuristics and biases". _Science_. **185** (4157). New York: Cambridge University Press: 1124–1131. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[1974Sci...185.1124T](https://ui.adsabs.harvard.edu/abs/1974Sci...185.1124T). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1126/science.185.4157.1124](https://doi.org/10.1126%2Fscience.185.4157.1124). [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-521-28414-1](https://en.wikipedia.org/wiki/Special:BookSources/978-0-521-28414-1 "Special:BookSources/978-0-521-28414-1"). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [17835457](https://pubmed.ncbi.nlm.nih.gov/17835457). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [143452957](https://api.semanticscholar.org/CorpusID:143452957).
*   Kasperowicz, Peter (1 May 2023). ["Regulate AI? GOP much more skeptical than Dems that government can do it right: poll"](https://www.foxnews.com/politics/regulate-ai-gop-much-more-skeptical-than-dems-that-the-government-can-do-it-right-poll). _Fox News_. [Archived](https://web.archive.org/web/20230619013616/https://www.foxnews.com/politics/regulate-ai-gop-much-more-skeptical-than-dems-that-the-government-can-do-it-right-poll) from the original on 19 June 2023. Retrieved 19 June 2023.
*   Katz, Yarden (1 November 2012). ["Noam Chomsky on Where Artificial Intelligence Went Wrong"](https://www.theatlantic.com/technology/archive/2012/11/noam-chomsky-on-where-artificial-intelligence-went-wrong/261637/?single_page=true). _The Atlantic_. [Archived](https://web.archive.org/web/20190228154403/https://www.theatlantic.com/technology/archive/2012/11/noam-chomsky-on-where-artificial-intelligence-went-wrong/261637/?single_page=true) from the original on 28 February 2019. Retrieved 26 October 2014.
*   ["Kismet"](http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html). MIT Artificial Intelligence Laboratory, Humanoid Robotics Group. [Archived](https://web.archive.org/web/20141017040432/http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html) from the original on 17 October 2014. Retrieved 25 October 2014.
*   [Kissinger, Henry](https://en.wikipedia.org/wiki/Henry_Kissinger "Henry Kissinger") (1 November 2021). ["The Challenge of Being Human in the Age of AI"](https://www.wsj.com/articles/being-human-artifical-intelligence-ai-chess-antibiotic-philosophy-ethics-bill-of-rights-11635795271). _[The Wall Street Journal](https://en.wikipedia.org/wiki/The_Wall_Street_Journal "The Wall Street Journal")_. [Archived](https://web.archive.org/web/20211104012825/https://www.wsj.com/articles/being-human-artifical-intelligence-ai-chess-antibiotic-philosophy-ethics-bill-of-rights-11635795271) from the original on 4 November 2021. Retrieved 4 November 2021.
*   Kobielus, James (27 November 2019). ["GPUs Continue to Dominate the AI Accelerator Market for Now"](https://www.informationweek.com/ai-or-machine-learning/gpus-continue-to-dominate-the-ai-accelerator-market-for-now). _InformationWeek_. [Archived](https://web.archive.org/web/20211019031104/https://www.informationweek.com/ai-or-machine-learning/gpus-continue-to-dominate-the-ai-accelerator-market-for-now) from the original on 19 October 2021. Retrieved 11 June 2020.
*   Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). ["Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513681). _Journal of the American Medical Informatics Association_. **13** (4): 369–371. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1197/jamia.M2055](https://doi.org/10.1197%2Fjamia.M2055). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [1513681](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513681). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [16622160](https://pubmed.ncbi.nlm.nih.gov/16622160).
*   [Kurzweil, Ray](https://en.wikipedia.org/wiki/Ray_Kurzweil "Ray Kurzweil") (2005). [_The Singularity is Near_](https://en.wikipedia.org/wiki/The_Singularity_is_Near "The Singularity is Near"). Penguin Books. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-670-03384-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-670-03384-3 "Special:BookSources/978-0-670-03384-3").
*   Langley, Pat (2011). ["The changing science of machine learning"](https://doi.org/10.1007%2Fs10994-011-5242-y). _[Machine Learning](https://en.wikipedia.org/wiki/Machine_Learning_\(journal\) "Machine Learning (journal)")_. **82** (3): 275–279. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/s10994-011-5242-y](https://doi.org/10.1007%2Fs10994-011-5242-y).
*   Larson, Jeff; [Angwin, Julia](https://en.wikipedia.org/wiki/Julia_Angwin "Julia Angwin") (23 May 2016). ["How We Analyzed the COMPAS Recidivism Algorithm"](https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm). _ProPublica_. [Archived](https://web.archive.org/web/20190429190950/https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm) from the original on 29 April 2019. Retrieved 19 June 2020.
*   Laskowski, Nicole (November 2023). ["What is Artificial Intelligence and How Does AI Work? TechTarget"](https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence). _Enterprise AI_. Retrieved 30 October 2023.
*   Law Library of Congress (U.S.). Global Legal Research Directorate, issuing body. (2019). _Regulation of artificial intelligence in selected jurisdictions_. [LCCN](https://en.wikipedia.org/wiki/LCCN_\(identifier\) "LCCN (identifier)") [2019668143](https://lccn.loc.gov/2019668143). [OCLC](https://en.wikipedia.org/wiki/OCLC_\(identifier\) "OCLC (identifier)") [1110727808](https://www.worldcat.org/oclc/1110727808).
*   Lee, Timothy B. (22 August 2014). ["Will artificial intelligence destroy humanity? Here are 5 reasons not to worry"](https://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking). _Vox_. [Archived](https://web.archive.org/web/20151030092203/http://www.vox.com/2014/8/22/6043635/5-reasons-we-shouldnt-worry-about-super-intelligent-computers-taking) from the original on 30 October 2015. Retrieved 30 October 2015.
*   [Lenat, Douglas](https://en.wikipedia.org/wiki/Douglas_Lenat "Douglas Lenat"); Guha, R. V. (1989). _Building Large Knowledge-Based Systems_. Addison-Wesley. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-201-51752-1](https://en.wikipedia.org/wiki/Special:BookSources/978-0-201-51752-1 "Special:BookSources/978-0-201-51752-1").
*   [Lighthill, James](https://en.wikipedia.org/wiki/James_Lighthill "James Lighthill") (1973). "Artificial Intelligence: A General Survey". _Artificial Intelligence: a paper symposium_. Science Research Council.
*   Lipartito, Kenneth (6 January 2011), [_The Narrative and the Algorithm: Genres of Credit Reporting from the Nineteenth Century to Today_](https://mpra.ub.uni-muenchen.de/28142/1/MPRA_paper_28142.pdf) (PDF) (Unpublished manuscript), [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.2139/ssrn.1736283](https://doi.org/10.2139%2Fssrn.1736283), [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [166742927](https://api.semanticscholar.org/CorpusID:166742927), [archived](https://ghostarchive.org/archive/20221009/https://mpra.ub.uni-muenchen.de/28142/1/MPRA_paper_28142.pdf) (PDF) from the original on 9 October 2022
*   Lohr, Steve (2017). ["Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says"](https://www.nytimes.com/2017/01/12/technology/robots-will-take-jobs-but-not-as-fast-as-some-fear-new-report-says.html). _The New York Times_. [Archived](https://web.archive.org/web/20180114073704/https://www.nytimes.com/2017/01/12/technology/robots-will-take-jobs-but-not-as-fast-as-some-fear-new-report-says.html) from the original on 14 January 2018. Retrieved 13 January 2018.
*   Lungarella, M.; Metta, G.; Pfeifer, R.; Sandini, G. (2003). "Developmental robotics: a survey". _Connection Science_. **15** (4): 151–190. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10.1.1.83.7615](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.7615). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1080/09540090310001655110](https://doi.org/10.1080%2F09540090310001655110). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [1452734](https://api.semanticscholar.org/CorpusID:1452734).
*   ["Machine Ethics"](https://web.archive.org/web/20141129044821/http://www.aaai.org/Library/Symposia/Fall/fs05-06). _aaai.org_. Archived from [the original](http://www.aaai.org/Library/Symposia/Fall/fs05-06) on 29 November 2014.
*   Madrigal, Alexis C. (27 February 2015). ["The case against killer robots, from a guy actually working on artificial intelligence"](https://www.hrw.org/report/2012/11/19/losing-humanity/case-against-killer-robots). _Fusion.net_. [Archived](https://web.archive.org/web/20160204175716/http://fusion.net/story/54583/the-case-against-killer-robots-from-a-guy-actually-building-ai/) from the original on 4 February 2016. Retrieved 31 January 2016.
*   Mahdawi, Arwa (26 June 2017). ["What jobs will still be around in 20 years? Read this to prepare your future"](https://www.theguardian.com/us-news/2017/jun/26/jobs-future-automation-robots-skills-creative-health). _The Guardian_. [Archived](https://web.archive.org/web/20180114021804/https://www.theguardian.com/us-news/2017/jun/26/jobs-future-automation-robots-skills-creative-health) from the original on 14 January 2018. Retrieved 13 January 2018.
*   Maker, Meg Houston (2006). ["AI@50: AI Past, Present, Future"](https://web.archive.org/web/20070103222615/http://www.engagingexperience.com/2006/07/ai50_ai_past_pr.html). Dartmouth College. Archived from [the original](http://www.engagingexperience.com/2006/07/ai50_ai_past_pr.html) on 3 January 2007. Retrieved 16 October 2008.
*   Marmouyet, Françoise (15 December 2023). ["Google's Gemini: is the new AI model really better than ChatGPT?"](https://theconversation.com/googles-gemini-is-the-new-ai-model-really-better-than-chatgpt-219526). _The Conversation_. Retrieved 25 December 2023.
*   [Minsky, Marvin](https://en.wikipedia.org/wiki/Marvin_Minsky "Marvin Minsky") (1986), [_The Society of Mind_](https://en.wikipedia.org/wiki/The_Society_of_Mind "The Society of Mind"), Simon and Schuster
*   Maschafilm (2010). ["Content: Plug & Pray Film – Artificial Intelligence – Robots"](http://www.plugandpray-film.de/en/content.html). _plugandpray-film.de_. [Archived](https://web.archive.org/web/20160212040134/http://www.plugandpray-film.de/en/content.html) from the original on 12 February 2016.
*   [McCarthy, John](https://en.wikipedia.org/wiki/John_McCarthy_\(computer_scientist\) "John McCarthy (computer scientist)"); [Minsky, Marvin](https://en.wikipedia.org/wiki/Marvin_Minsky "Marvin Minsky"); [Rochester, Nathan](https://en.wikipedia.org/wiki/Nathan_Rochester "Nathan Rochester"); [Shannon, Claude](https://en.wikipedia.org/wiki/Claude_Shannon "Claude Shannon") (1955). ["A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence"](https://web.archive.org/web/20070826230310/http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html). Archived from [the original](http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html) on 26 August 2007. Retrieved 30 August 2007.
*   [McCarthy, John](https://en.wikipedia.org/wiki/John_McCarthy_\(computer_scientist\) "John McCarthy (computer scientist)") (2007), "From Here to Human-Level AI", _Artificial Intelligence_: 171
*   [McCarthy, John](https://en.wikipedia.org/wiki/John_McCarthy_\(computer_scientist\) "John McCarthy (computer scientist)") (1999), [_What is AI?_](http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html), [archived](https://web.archive.org/web/20221204051737/http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html) from the original on 4 December 2022, retrieved 4 December 2022
*   McCauley, Lee (2007). "AI armageddon and the three laws of robotics". _Ethics and Information Technology_. **9** (2): 153–164. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10.1.1.85.8904](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.8904). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/s10676-007-9138-2](https://doi.org/10.1007%2Fs10676-007-9138-2). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [37272949](https://api.semanticscholar.org/CorpusID:37272949).
*   McGarry, Ken (1 December 2005). "A survey of interestingness measures for knowledge discovery". _The Knowledge Engineering Review_. **20** (1): 39–61. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1017/S0269888905000408](https://doi.org/10.1017%2FS0269888905000408). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [14987656](https://api.semanticscholar.org/CorpusID:14987656).
*   McGaughey, E (2022), [_Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy_](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448), p. 51(3) Industrial Law Journal 511–559, [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.2139/ssrn.3044448](https://doi.org/10.2139%2Fssrn.3044448), [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [219336439](https://api.semanticscholar.org/CorpusID:219336439), [SSRN](https://en.wikipedia.org/wiki/SSRN_\(identifier\) "SSRN (identifier)") [3044448](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448), [archived](https://web.archive.org/web/20210131074722/https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3044448) from the original on 31 January 2021, retrieved 27 May 2023
*   Merkle, Daniel; Middendorf, Martin (2013). "Swarm Intelligence". In Burke, Edmund K.; Kendall, Graham (eds.). _Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques_. Springer Science & Business Media. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-1-4614-6940-7](https://en.wikipedia.org/wiki/Special:BookSources/978-1-4614-6940-7 "Special:BookSources/978-1-4614-6940-7").
*   [Minsky, Marvin](https://en.wikipedia.org/wiki/Marvin_Minsky "Marvin Minsky") (1967), _Computation: Finite and Infinite Machines_, Englewood Cliffs, N.J.: Prentice-Hall
*   [Moravec, Hans](https://en.wikipedia.org/wiki/Hans_Moravec "Hans Moravec") (1988). [_Mind Children_](https://archive.org/details/mindchildrenfutu00mora). Harvard University Press. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-674-57616-2](https://en.wikipedia.org/wiki/Special:BookSources/978-0-674-57616-2 "Special:BookSources/978-0-674-57616-2"). [Archived](https://web.archive.org/web/20200726131644/https://archive.org/details/mindchildrenfutu00mora) from the original on 26 July 2020. Retrieved 18 November 2019.
*   Morgenstern, Michael (9 May 2015). ["Automation and anxiety"](https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety). _The Economist_. [Archived](https://web.archive.org/web/20180112214621/https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety) from the original on 12 January 2018. Retrieved 13 January 2018.
*   Müller, Vincent C.; Bostrom, Nick (2014). ["Future Progress in Artificial Intelligence: A Poll Among Experts"](http://www.sophia.de/pdf/2014_PT-AI_polls.pdf) (PDF). _AI Matters_. **1** (1): 9–11. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1145/2639475.2639478](https://doi.org/10.1145%2F2639475.2639478). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [8510016](https://api.semanticscholar.org/CorpusID:8510016). [Archived](https://web.archive.org/web/20160115114604/http://www.sophia.de/pdf/2014_PT-AI_polls.pdf) (PDF) from the original on 15 January 2016.
*   Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". _Image and Vision Computing_. **26** (1): 82–101. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/j.imavis.2007.08.013](https://doi.org/10.1016%2Fj.imavis.2007.08.013). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [10767011](https://api.semanticscholar.org/CorpusID:10767011).
*   [Nilsson, Nils](https://en.wikipedia.org/wiki/Nils_John_Nilsson "Nils John Nilsson") (1995), "Eyes on the Prize", _AI Magazine_, vol. 16, pp. 9–17
*   [Newell, Allen](https://en.wikipedia.org/wiki/Allen_Newell "Allen Newell"); [Simon, H. A.](https://en.wikipedia.org/wiki/Herbert_A._Simon "Herbert A. Simon") (1976). ["Computer Science as Empirical Inquiry: Symbols and Search"](https://doi.org/10.1145%2F360018.360022). _Communications of the ACM_. **19** (3): 113–126. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1145/360018.360022](https://doi.org/10.1145%2F360018.360022).
*   Nicas, Jack (7 February 2018). ["How YouTube Drives People to the Internet's Darkest Corners"](https://www.wsj.com/articles/how-youtube-drives-viewers-to-the-internets-darkest-corners-1518020478). _[The Wall Street Journal](https://en.wikipedia.org/wiki/The_Wall_Street_Journal "The Wall Street Journal")_. [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0099-9660](https://www.worldcat.org/issn/0099-9660). Retrieved 16 June 2018.
*   [Nilsson, Nils](https://en.wikipedia.org/wiki/Nils_Nilsson_\(researcher\) "Nils Nilsson (researcher)") (1983). ["Artificial Intelligence Prepares for 2001"](https://ai.stanford.edu/~nilsson/OnlinePubs-Nils/General%20Essays/AIMag04-04-002.pdf) (PDF). _AI Magazine_. **1** (1). [Archived](https://web.archive.org/web/20200817194457/http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/General%20Essays/AIMag04-04-002.pdf) (PDF) from the original on 17 August 2020. Retrieved 22 August 2020. Presidential Address to the [Association for the Advancement of Artificial Intelligence](https://en.wikipedia.org/wiki/Association_for_the_Advancement_of_Artificial_Intelligence "Association for the Advancement of Artificial Intelligence").
*   [NRC (United States National Research Council)](https://en.wikipedia.org/wiki/United_States_National_Research_Council "United States National Research Council") (1999). "Developments in Artificial Intelligence". _Funding a Revolution: Government Support for Computing Research_. National Academy Press.
*   [Omohundro, Steve](https://en.wikipedia.org/wiki/Steve_Omohundro "Steve Omohundro") (2008). _The Nature of Self-Improving Artificial Intelligence_. presented and distributed at the 2007 Singularity Summit, San Francisco, CA.
*   Oudeyer, P-Y. (2010). ["On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development"](http://www.pyoudeyer.com/IEEETAMDOudeyer10.pdf) (PDF). _IEEE Transactions on Autonomous Mental Development_. **2** (1): 2–16. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/tamd.2009.2039057](https://doi.org/10.1109%2Ftamd.2009.2039057). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [6362217](https://api.semanticscholar.org/CorpusID:6362217). [Archived](https://web.archive.org/web/20181003202543/http://www.pyoudeyer.com/IEEETAMDOudeyer10.pdf) (PDF) from the original on 3 October 2018. Retrieved 4 June 2013.
*   Pennachin, C.; Goertzel, B. (2007). "Contemporary Approaches to Artificial General Intelligence". _Artificial General Intelligence_. Cognitive Technologies. Berlin, Heidelberg: Springer. pp. 1–30. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/978-3-540-68677-4\_1](https://doi.org/10.1007%2F978-3-540-68677-4_1). [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-3-540-23733-4](https://en.wikipedia.org/wiki/Special:BookSources/978-3-540-23733-4 "Special:BookSources/978-3-540-23733-4").
*   [Pinker, Steven](https://en.wikipedia.org/wiki/Steven_Pinker "Steven Pinker") (2007) \[1994\], [_The Language Instinct_](https://en.wikipedia.org/wiki/The_Language_Instinct "The Language Instinct"), Perennial Modern Classics, Harper, [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-06-133646-1](https://en.wikipedia.org/wiki/Special:BookSources/978-0-06-133646-1 "Special:BookSources/978-0-06-133646-1")
*   Poria, Soujanya; Cambria, Erik; Bajpai, Rajiv; Hussain, Amir (September 2017). ["A review of affective computing: From unimodal analysis to multimodal fusion"](http://researchrepository.napier.ac.uk/Output/1792429). _Information Fusion_. **37**: 98–125. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/j.inffus.2017.02.003](https://doi.org/10.1016%2Fj.inffus.2017.02.003). [hdl](https://en.wikipedia.org/wiki/Hdl_\(identifier\) "Hdl (identifier)"):[1893/25490](https://hdl.handle.net/1893%2F25490). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [205433041](https://api.semanticscholar.org/CorpusID:205433041). [Archived](https://web.archive.org/web/20230323165407/https://www.napier.ac.uk/research-and-innovation/research-search/outputs/a-review-of-affective-computing-from-unimodal-analysis-to-multimodal-fusion) from the original on 23 March 2023. Retrieved 27 April 2021.
*   Rawlinson, Kevin (29 January 2015). ["Microsoft's Bill Gates insists AI is a threat"](https://www.bbc.co.uk/news/31047780). _BBC News_. [Archived](https://web.archive.org/web/20150129183607/http://www.bbc.co.uk/news/31047780) from the original on 29 January 2015. Retrieved 30 January 2015.
*   Reisner, Alex (19 August 2023), ["Revealed: The Authors Whose Pirated Books are Powering Generative AI"](https://www.theatlantic.com/technology/archive/2023/08/books3-ai-meta-llama-pirated-books/675063/), _The Atlantic_
*   Roberts, Jacob (2016). ["Thinking Machines: The Search for Artificial Intelligence"](https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence). _Distillations_. Vol. 2, no. 2. pp. 14–23. Archived from [the original](https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence) on 19 August 2018. Retrieved 20 March 2018.
*   Robitzski, Dan (5 September 2018). ["Five experts share what scares them the most about AI"](https://futurism.com/artificial-intelligence-experts-fear/amp). [Archived](https://web.archive.org/web/20191208094101/https://futurism.com/artificial-intelligence-experts-fear/amp) from the original on 8 December 2019. Retrieved 8 December 2019.
*   ["Robots could demand legal rights"](http://news.bbc.co.uk/2/hi/technology/6200005.stm). _BBC News_. 21 December 2006. [Archived](https://web.archive.org/web/20191015042628/http://news.bbc.co.uk/2/hi/technology/6200005.stm) from the original on 15 October 2019. Retrieved 3 February 2011.
*   Rose, Steve (11 July 2023). "AI Utopia or dystopia?". _[The Guardian Weekly](https://en.wikipedia.org/wiki/The_Guardian_Weekly "The Guardian Weekly")_. pp. 42–43.
*   [Russell, Stuart](https://en.wikipedia.org/wiki/Stuart_J._Russell "Stuart J. Russell") (2019). [_Human Compatible: Artificial Intelligence and the Problem of Control_](https://en.wikipedia.org/wiki/Human_Compatible "Human Compatible"). United States: Viking. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-525-55861-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-525-55861-3 "Special:BookSources/978-0-525-55861-3"). [OCLC](https://en.wikipedia.org/wiki/OCLC_\(identifier\) "OCLC (identifier)") [1083694322](https://www.worldcat.org/oclc/1083694322).
*   Sainato, Michael (19 August 2015). ["Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence"](https://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/). _Observer_. [Archived](https://web.archive.org/web/20151030053323/http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/) from the original on 30 October 2015. Retrieved 30 October 2015.
*   Sample, Ian (5 November 2017). ["Computer says no: why making AIs fair, accountable and transparent is crucial"](https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial). _The Guardian_. Retrieved 30 January 2018.
*   Rothman, Denis (7 October 2020). ["Exploring LIME Explanations and the Mathematics Behind It"](https://www.codemotion.com/magazine/ai-ml/lime-explainable-ai/). _Codemotion_. Retrieved 25 November 2023.
*   Scassellati, Brian (2002). "Theory of mind for a humanoid robot". _Autonomous Robots_. **12** (1): 13–24. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1023/A:1013298507114](https://doi.org/10.1023%2FA%3A1013298507114). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [1979315](https://api.semanticscholar.org/CorpusID:1979315).
*   [Schmidhuber, J.](https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber "Jürgen Schmidhuber") (2015). "Deep Learning in Neural Networks: An Overview". _Neural Networks_. **61**: 85–117. [arXiv](https://en.wikipedia.org/wiki/ArXiv_\(identifier\) "ArXiv (identifier)"):[1404.7828](https://arxiv.org/abs/1404.7828). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/j.neunet.2014.09.003](https://doi.org/10.1016%2Fj.neunet.2014.09.003). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [25462637](https://pubmed.ncbi.nlm.nih.gov/25462637). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [11715509](https://api.semanticscholar.org/CorpusID:11715509).
*   [Schmidhuber, Jürgen](https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber "Jürgen Schmidhuber") (2022). ["Annotated History of Modern AI and Deep Learning"](https://people.idsia.ch/~juergen/).
*   Schulz, Hannes; Behnke, Sven (1 November 2012). ["Deep Learning"](https://www.researchgate.net/publication/230690795). _KI – Künstliche Intelligenz_. **26** (4): 357–363. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/s13218-012-0198-z](https://doi.org/10.1007%2Fs13218-012-0198-z). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1610-1987](https://www.worldcat.org/issn/1610-1987). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [220523562](https://api.semanticscholar.org/CorpusID:220523562).
*   [Searle, John](https://en.wikipedia.org/wiki/John_Searle "John Searle") (1980). ["Minds, Brains and Programs"](http://cogprints.org/7150/1/10.1.1.83.5248.pdf) (PDF). _Behavioral and Brain Sciences_. **3** (3): 417–457. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1017/S0140525X00005756](https://doi.org/10.1017%2FS0140525X00005756). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [55303721](https://api.semanticscholar.org/CorpusID:55303721). [Archived](https://web.archive.org/web/20190317230215/http://cogprints.org/7150/1/10.1.1.83.5248.pdf) (PDF) from the original on 17 March 2019. Retrieved 22 August 2020.
*   [Searle, John](https://en.wikipedia.org/wiki/John_Searle "John Searle") (1999). [_Mind, language and society_](https://archive.org/details/mindlanguagesoci00sear). New York: Basic Books. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-465-04521-1](https://en.wikipedia.org/wiki/Special:BookSources/978-0-465-04521-1 "Special:BookSources/978-0-465-04521-1"). [OCLC](https://en.wikipedia.org/wiki/OCLC_\(identifier\) "OCLC (identifier)") [231867665](https://www.worldcat.org/oclc/231867665). [Archived](https://web.archive.org/web/20200726220615/https://archive.org/details/mindlanguagesoci00sear) from the original on 26 July 2020. Retrieved 22 August 2020.
*   Simon, H. A. (1965), _The Shape of Automation for Men and Management_, New York: Harper & Row
*   Simonite, Tom (31 March 2016). ["How Google Plans to Solve Artificial Intelligence"](https://www.technologyreview.com/2016/03/31/161234/how-google-plans-to-solve-artificial-intelligence/). _MIT Technology Review_.
*   Smith, Craig S. (15 March 2023). ["ChatGPT-4 Creator Ilya Sutskever on AI Hallucinations and AI Democracy"](https://www.forbes.com/sites/craigsmith/2023/03/15/gpt-4-creator-ilya-sutskever-on-ai-hallucinations-and-ai-democracy/). _Forbes_. Retrieved 25 December 2023.
*   Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". _IEEE MultiMedia_. **1** (2): 62–72. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1109/93.311653](https://doi.org/10.1109%2F93.311653). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [32710913](https://api.semanticscholar.org/CorpusID:32710913).
*   [Solomonoff, Ray](https://en.wikipedia.org/wiki/Ray_Solomonoff "Ray Solomonoff") (1956). [_An Inductive Inference Machine_](http://world.std.com/~rjs/indinf56.pdf) (PDF). Dartmouth Summer Research Conference on Artificial Intelligence. [Archived](https://web.archive.org/web/20110426161749/http://world.std.com/~rjs/indinf56.pdf) (PDF) from the original on 26 April 2011. Retrieved 22 March 2011 – via std.com, pdf scanned copy of the original. Later published as  
    Solomonoff, Ray (1957). "An Inductive Inference Machine". _IRE Convention Record_. Vol. Section on Information Theory, part 2. pp. 56–62.
*   Stanford University (2023). ["Artificial Intelligence Index Report 2023/Chapter 6: Policy and Governance"](https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report-2023_CHAPTER_6-1.pdf) (PDF). AI Index. [Archived](https://web.archive.org/web/20230619013609/https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report-2023_CHAPTER_6-1.pdf) (PDF) from the original on 19 June 2023. Retrieved 19 June 2023.
*   Tao, Jianhua; Tan, Tieniu (2005). _Affective Computing and Intelligent Interaction_. Affective Computing: A Review. Lecture Notes in Computer Science. Vol. 3784. Springer. pp. 981–995. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1007/11573548](https://doi.org/10.1007%2F11573548). [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-3-540-29621-8](https://en.wikipedia.org/wiki/Special:BookSources/978-3-540-29621-8 "Special:BookSources/978-3-540-29621-8").
*   Taylor, Josh; Hern, Alex (2 May 2023). ["'Godfather of AI' Geoffrey Hinton quits Google and warns over dangers of misinformation"](https://www.theguardian.com/technology/2023/may/02/geoffrey-hinton-godfather-of-ai-quits-google-warns-dangers-of-machine-learning). _[The Guardian](https://en.wikipedia.org/wiki/The_Guardian "The Guardian")_.
*   Thompson, Derek (23 January 2014). ["What Jobs Will the Robots Take?"](https://www.theatlantic.com/business/archive/2014/01/what-jobs-will-the-robots-take/283239/). _The Atlantic_. [Archived](https://web.archive.org/web/20180424202435/https://www.theatlantic.com/business/archive/2014/01/what-jobs-will-the-robots-take/283239/) from the original on 24 April 2018. Retrieved 24 April 2018.
*   Thro, Ellen (1993). [_Robotics: The Marriage of Computers and Machines_](https://archive.org/details/isbn_9780816026289). New York: Facts on File. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-0-8160-2628-9](https://en.wikipedia.org/wiki/Special:BookSources/978-0-8160-2628-9 "Special:BookSources/978-0-8160-2628-9"). [Archived](https://web.archive.org/web/20200726131505/https://archive.org/details/isbn_9780816026289) from the original on 26 July 2020. Retrieved 22 August 2020.
*   Toews, Rob (3 September 2023). ["Transformers Revolutionized AI. What Will Replace Them?"](https://www.forbes.com/sites/robtoews/2023/09/03/transformers-revolutionized-ai-what-will-replace-them/). _Forbes_. Retrieved 8 December 2023.
*   [Turing, Alan](https://en.wikipedia.org/wiki/Alan_Turing "Alan Turing") (October 1950), "Computing Machinery and Intelligence", _[Mind](https://en.wikipedia.org/wiki/Mind_\(journal\) "Mind (journal)")_, **LIX** (236): 433–460, [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1093/mind/LIX.236.433](https://doi.org/10.1093%2Fmind%2FLIX.236.433), [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0026-4423](https://www.worldcat.org/issn/0026-4423)
*   [_UNESCO Science Report: the Race Against Time for Smarter Development_](https://unesdoc.unesco.org/ark:/48223/pf0000377433/PDF/377433eng.pdf.multi). Paris: UNESCO. 2021. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [978-92-3-100450-6](https://en.wikipedia.org/wiki/Special:BookSources/978-92-3-100450-6 "Special:BookSources/978-92-3-100450-6"). [Archived](https://web.archive.org/web/20220618233752/https://unesdoc.unesco.org/ark:/48223/pf0000377433/PDF/377433eng.pdf.multi) from the original on 18 June 2022. Retrieved 18 September 2021.
*   Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric; Ekins, Sean (7 March 2022). ["Dual use of artificial-intelligence-powered drug discovery"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9544280). _Nature Machine Intelligence_. **4** (3): 189–191. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/s42256-022-00465-9](https://doi.org/10.1038%2Fs42256-022-00465-9). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [9544280](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9544280). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [36211133](https://pubmed.ncbi.nlm.nih.gov/36211133). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [247302391](https://api.semanticscholar.org/CorpusID:247302391).
*   Valance, Christ (30 May 2023). ["Artificial intelligence could lead to extinction, experts warn"](https://www.bbc.com/news/uk-65746524). _BBC News_. [Archived](https://web.archive.org/web/20230617200355/https://www.bbc.com/news/uk-65746524) from the original on 17 June 2023. Retrieved 18 June 2023.
*   Valinsky, Jordan (11 April 2019), ["Amazon reportedly employs thousands of people to listen to your Alexa conversations"](https://www.cnn.com/2019/04/11/tech/amazon-alexa-listening/index.html), _CNN.com_
*   Verma, Yugesh (25 December 2021). ["A Complete Guide to SHAP – SHAPley Additive exPlanations for Practitioners"](https://analyticsindiamag.com/a-complete-guide-to-shap-shapley-additive-explanations-for-practitioners/). _Analytics India Magazine_. Retrieved 25 November 2023.
*   Vincent, James (7 November 2019). ["OpenAI has published the text-generating AI it said was too dangerous to share"](https://www.theverge.com/2019/11/7/20953040/openai-text-generation-ai-gpt-2-full-model-release-1-5b-parameters). _The Verge_. [Archived](https://web.archive.org/web/20200611054114/https://www.theverge.com/2019/11/7/20953040/openai-text-generation-ai-gpt-2-full-model-release-1-5b-parameters) from the original on 11 June 2020. Retrieved 11 June 2020.
*   Vincent, James (15 November 2022). ["The scary truth about AI copyright is nobody knows what will happen next"](https://www.theverge.com/23444685/generative-ai-copyright-infringement-legal-fair-use-training-data). _The Verge_. [Archived](https://web.archive.org/web/20230619055201/https://www.theverge.com/23444685/generative-ai-copyright-infringement-legal-fair-use-training-data) from the original on 19 June 2023. Retrieved 19 June 2023.
*   Vincent, James (3 April 2023). ["AI is entering an era of corporate control"](https://www.theverge.com/23667752/ai-progress-2023-report-stanford-corporate-control). _The Verge_. [Archived](https://web.archive.org/web/20230619005803/https://www.theverge.com/23667752/ai-progress-2023-report-stanford-corporate-control) from the original on 19 June 2023. Retrieved 19 June 2023.
*   [Vinge, Vernor](https://en.wikipedia.org/wiki/Vernor_Vinge "Vernor Vinge") (1993). ["The Coming Technological Singularity: How to Survive in the Post-Human Era"](https://web.archive.org/web/20070101133646/http://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html). _Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace_: 11. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[1993vise.nasa...11V](https://ui.adsabs.harvard.edu/abs/1993vise.nasa...11V). Archived from [the original](http://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html) on 1 January 2007. Retrieved 14 November 2011.
*   Waddell, Kaveh (2018). ["Chatbots Have Entered the Uncanny Valley"](https://www.theatlantic.com/technology/archive/2017/04/uncanny-valley-digital-assistants/523806/). _The Atlantic_. [Archived](https://web.archive.org/web/20180424202350/https://www.theatlantic.com/technology/archive/2017/04/uncanny-valley-digital-assistants/523806/) from the original on 24 April 2018. Retrieved 24 April 2018.
*   Wallach, Wendell (2010). _Moral Machines_. Oxford University Press.
*   [Wason, P. C.](https://en.wikipedia.org/wiki/Peter_Cathcart_Wason "Peter Cathcart Wason"); Shapiro, D. (1966). ["Reasoning"](https://archive.org/details/newhorizonsinpsy0000foss). In Foss, B. M. (ed.). _New horizons in psychology_. Harmondsworth: Penguin. [Archived](https://web.archive.org/web/20200726131518/https://archive.org/details/newhorizonsinpsy0000foss) from the original on 26 July 2020. Retrieved 18 November 2019.
*   Weng, J.; McClelland; Pentland, A.; Sporns, O.; Stockman, I.; Sur, M.; Thelen, E. (2001). ["Autonomous mental development by robots and animals"](http://www.cse.msu.edu/dl/SciencePaper.pdf) (PDF). _Science_. **291** (5504): 599–600. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1126/science.291.5504.599](https://doi.org/10.1126%2Fscience.291.5504.599). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [11229402](https://pubmed.ncbi.nlm.nih.gov/11229402). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [54131797](https://api.semanticscholar.org/CorpusID:54131797). [Archived](https://web.archive.org/web/20130904235242/http://www.cse.msu.edu/dl/SciencePaper.pdf) (PDF) from the original on 4 September 2013. Retrieved 4 June 2013 – via msu.edu.
*   ["What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?"](https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/). _Scientific American_. 21 October 1999. [Archived](https://web.archive.org/web/20180506035133/https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/) from the original on 6 May 2018. Retrieved 5 May 2018.
*   Williams, Rhiannon (28 June 2023), ["Humans may be more likely to believe disinformation generated by AI"](https://www.technologyreview.com/2023/06/28/1075683/humans-may-be-more-likely-to-believe-disinformation-generated-by-ai/), _[MIT Technology Review](https://en.wikipedia.org/wiki/MIT_Technology_Review "MIT Technology Review")_
*   Wirtz, Bernd W.; Weyerer, Jan C.; Geyer, Carolin (24 July 2018). ["Artificial Intelligence and the Public Sector – Applications and Challenges"](https://zenodo.org/record/3569435). _International Journal of Public Administration_. **42** (7): 596–615. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1080/01900692.2018.1498103](https://doi.org/10.1080%2F01900692.2018.1498103). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [0190-0692](https://www.worldcat.org/issn/0190-0692). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [158829602](https://api.semanticscholar.org/CorpusID:158829602). [Archived](https://web.archive.org/web/20200818131415/https://zenodo.org/record/3569435) from the original on 18 August 2020. Retrieved 22 August 2020.
*   Wong, Matteo (19 May 2023), ["ChatGPT Is Already Obsolete"](https://www.theatlantic.com/technology/archive/2023/05/ai-advancements-multimodal-models/674113/), _The Atlantic_
*   Yudkowsky, E (2008), ["Artificial Intelligence as a Positive and Negative Factor in Global Risk"](http://intelligence.org/files/AIPosNegFactor.pdf) (PDF), _Global Catastrophic Risks_, Oxford University Press, 2008, [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2008gcr..book..303Y](https://ui.adsabs.harvard.edu/abs/2008gcr..book..303Y), [archived](https://web.archive.org/web/20131019182403/http://intelligence.org/files/AIPosNegFactor.pdf) (PDF) from the original on 19 October 2013, retrieved 24 September 2021

Further reading
---------------

*   Ashish Vaswani, Noam Shazeer, Niki Parmar et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Seminal paper on [transformers](https://en.wikipedia.org/wiki/Transformer_\(machine_learning_model\) "Transformer (machine learning model)").
*   [Autor, David H.](https://en.wikipedia.org/wiki/David_Autor "David Autor"), "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3) _Journal of Economic Perspectives_ 3.
*   [Boden, Margaret](https://en.wikipedia.org/wiki/Margaret_Boden "Margaret Boden"), _Mind As Machine_, [Oxford University Press](https://en.wikipedia.org/wiki/Oxford_University_Press "Oxford University Press"), 2006.
*   [Cukier, Kenneth](https://en.wikipedia.org/wiki/Kenneth_Cukier "Kenneth Cukier"), "Ready for Robots? How to Think about the Future of AI", _[Foreign Affairs](https://en.wikipedia.org/wiki/Foreign_Affairs "Foreign Affairs")_, vol. 98, no. 4 (July/August 2019), pp. 192–98. [George Dyson](https://en.wikipedia.org/wiki/George_Dyson_\(science_historian\) "George Dyson (science historian)"), historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist [Alex Pentland](https://en.wikipedia.org/wiki/Alex_Pentland "Alex Pentland") writes: "Current [AI machine-learning](https://en.wikipedia.org/wiki/Machine_learning "Machine learning") [algorithms](https://en.wikipedia.org/wiki/Algorithm "Algorithm") are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.)
*   Gertner, Jon. (2023) "Wikipedia's Moment of Truth: Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process?" _New York Times Magazine_ (July 18, 2023) [online](https://www.nytimes.com/2023/07/18/magazine/wikipedia-ai-chatgpt.html)
*   [Gleick, James](https://en.wikipedia.org/wiki/James_Gleick "James Gleick"), "The Fate of Free Will" (review of [Kevin J. Mitchell](https://en.wikipedia.org/w/index.php?title=Kevin_J._Mitchell&action=edit&redlink=1 "Kevin J. Mitchell (page does not exist)"), _Free Agents: How Evolution Gave Us Free Will_, Princeton University Press, 2023, 333 pp.), _[The New York Review of Books](https://en.wikipedia.org/wiki/The_New_York_Review_of_Books "The New York Review of Books")_, vol. LXXI, no. 1 (18 January 2024), pp. 27–28, 30. "[Agency](https://en.wikipedia.org/wiki/Agency_\(philosophy\) "Agency (philosophy)") is what distinguishes us from machines. For biological creatures, [reason](https://en.wikipedia.org/wiki/Reason "Reason") and [purpose](https://en.wikipedia.org/wiki/Motivation "Motivation") come from acting in the world and experiencing the consequences. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no occasion for that." (p. 30.)
*   [Hughes-Castleberry, Kenna](https://en.wikipedia.org/w/index.php?title=Kenna_Hughes-Castleberry&action=edit&redlink=1 "Kenna Hughes-Castleberry (page does not exist)"), "A Murder Mystery Puzzle: The literary puzzle _[Cain's Jawbone](https://en.wikipedia.org/wiki/Cain%27s_Jawbone "Cain's Jawbone")_, which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", _[Scientific American](https://en.wikipedia.org/wiki/Scientific_American "Scientific American")_, vol. 329, no. 4 (November 2023), pp. 81–82. "This murder mystery competition has revealed that although NLP ([natural-language processing](https://en.wikipedia.org/wiki/Natural-language_processing "Natural-language processing")) models are capable of incredible feats, their abilities are very much limited by the amount of [context](https://en.wikipedia.org/wiki/Context_\(linguistics\) "Context (linguistics)") they receive. This \[...\] could cause \[difficulties\] for researchers who hope to use them to do things such as analyze [ancient languages](https://en.wikipedia.org/wiki/Ancient_language "Ancient language"). In some cases, there are few historical records on long-gone [civilizations](https://en.wikipedia.org/wiki/Civilization "Civilization") to serve as [training data](https://en.wikipedia.org/wiki/Training_data "Training data") for such a purpose." (p. 82.)
*   [Immerwahr, Daniel](https://en.wikipedia.org/wiki/Daniel_Immerwahr "Daniel Immerwahr"), "Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?", _[The New Yorker](https://en.wikipedia.org/wiki/The_New_Yorker "The New Yorker")_, 20 November 2023, pp. 54–59. "If by '[deepfakes](https://en.wikipedia.org/wiki/Deepfakes "Deepfakes")' we mean realistic videos produced using artificial intelligence that actually deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't fake. \[...\] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their role better resembles that of [cartoons](https://en.wikipedia.org/wiki/Cartoon "Cartoon"), especially smutty ones." (p. 59.)
*   Johnston, John (2008) _The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI_, MIT Press.
*   Jumper, John; Evans, Richard; Pritzel, Alexander; et al. (26 August 2021). ["Highly accurate protein structure prediction with AlphaFold"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605). _Nature_. **596** (7873): 583–589. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2021Natur.596..583J](https://ui.adsabs.harvard.edu/abs/2021Natur.596..583J). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/s41586-021-03819-2](https://doi.org/10.1038%2Fs41586-021-03819-2). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [8371605](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [34265844](https://pubmed.ncbi.nlm.nih.gov/34265844). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [235959867](https://api.semanticscholar.org/CorpusID:235959867).
*   LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (28 May 2015). ["Deep learning"](https://www.nature.com/articles/nature14539). _Nature_. **521** (7553): 436–444. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2015Natur.521..436L](https://ui.adsabs.harvard.edu/abs/2015Natur.521..436L). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/nature14539](https://doi.org/10.1038%2Fnature14539). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [26017442](https://pubmed.ncbi.nlm.nih.gov/26017442). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [3074096](https://api.semanticscholar.org/CorpusID:3074096). [Archived](https://web.archive.org/web/20230605235832/https://www.nature.com/articles/nature14539) from the original on 5 June 2023. Retrieved 19 June 2023.
*   [Marcus, Gary](https://en.wikipedia.org/wiki/Gary_Marcus "Gary Marcus"), "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", _[Scientific American](https://en.wikipedia.org/wiki/Scientific_American "Scientific American")_, vol. 327, no. 4 (October 2022), pp. 42–45.
*   Mitchell, Melanie (2019). _Artificial intelligence: a guide for thinking humans_. New York: Farrar, Straus and Giroux. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)") [9780374257835](https://en.wikipedia.org/wiki/Special:BookSources/9780374257835 "Special:BookSources/9780374257835").
*   Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; et al. (26 February 2015). ["Human-level control through deep reinforcement learning"](https://www.nature.com/articles/nature14236/). _Nature_. **518** (7540): 529–533. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2015Natur.518..529M](https://ui.adsabs.harvard.edu/abs/2015Natur.518..529M). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/nature14236](https://doi.org/10.1038%2Fnature14236). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [25719670](https://pubmed.ncbi.nlm.nih.gov/25719670). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [205242740](https://api.semanticscholar.org/CorpusID:205242740). [Archived](https://web.archive.org/web/20230619055525/https://www.nature.com/articles/nature14236/) from the original on 19 June 2023. Retrieved 19 June 2023. Introduced [DQN](https://en.wikipedia.org/wiki/Deep_Q-learning "Deep Q-learning"), which produced human-level performance on some Atari games.
*   [Press, Eyal](https://en.wikipedia.org/wiki/Eyal_Press "Eyal Press"), "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", _[The New Yorker](https://en.wikipedia.org/wiki/The_New_Yorker "The New Yorker")_, 20 November 2023, pp. 20–26.
*   [Roivainen, Eka](https://en.wikipedia.org/w/index.php?title=Eka_Roivainen&action=edit&redlink=1 "Eka Roivainen (page does not exist)"), "AI's IQ: [ChatGPT](https://en.wikipedia.org/wiki/ChatGPT "ChatGPT") aced a \[standard intelligence\] test but showed that [intelligence](https://en.wikipedia.org/wiki/Intelligence "Intelligence") cannot be measured by [IQ](https://en.wikipedia.org/wiki/IQ "IQ") alone", _[Scientific American](https://en.wikipedia.org/wiki/Scientific_American "Scientific American")_, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, [ChatGPT](https://en.wikipedia.org/wiki/ChatGPT "ChatGPT") fails at tasks that require real humanlike reasoning or an understanding of the physical and social world.... ChatGPT seemed unable to reason logically and tried to rely on its vast database of... facts derived from online texts."
*   Serenko, Alexander; Michael Dohan (2011). ["Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence"](http://www.aserenko.com/papers/JOI_AI_Journal_Ranking_Serenko.pdf) (PDF). _Journal of Informetrics_. **5** (4): 629–49. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1016/j.joi.2011.06.002](https://doi.org/10.1016%2Fj.joi.2011.06.002). [Archived](https://web.archive.org/web/20131004212839/http://www.aserenko.com/papers/JOI_AI_Journal_Ranking_Serenko.pdf) (PDF) from the original on 4 October 2013. Retrieved 12 September 2013.
*   Silver, David; Huang, Aja; Maddison, Chris J.; et al. (28 January 2016). ["Mastering the game of Go with deep neural networks and tree search"](https://www.nature.com/articles/nature16961). _Nature_. **529** (7587): 484–489. [Bibcode](https://en.wikipedia.org/wiki/Bibcode_\(identifier\) "Bibcode (identifier)"):[2016Natur.529..484S](https://ui.adsabs.harvard.edu/abs/2016Natur.529..484S). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10.1038/nature16961](https://doi.org/10.1038%2Fnature16961). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [26819042](https://pubmed.ncbi.nlm.nih.gov/26819042). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [515925](https://api.semanticscholar.org/CorpusID:515925). [Archived](https://web.archive.org/web/20230618213059/https://www.nature.com/articles/nature16961) from the original on 18 June 2023. Retrieved 19 June 2023.
*   [_White Paper: On Artificial Intelligence – A European approach to excellence and trust_](https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf) (PDF). Brussels: European Commission. 2020. [Archived](https://web.archive.org/web/20200220173419/https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf) (PDF) from the original on 20 February 2020. Retrieved 20 February 2020.

External links
--------------

*   ["Artificial Intelligence"](http://www.iep.utm.edu/art-inte). _[Internet Encyclopedia of Philosophy](https://en.wikipedia.org/wiki/Internet_Encyclopedia_of_Philosophy "Internet Encyclopedia of Philosophy")_.
*   Thomason, Richmond. ["Logic and Artificial Intelligence"](https://plato.stanford.edu/entries/logic-ai/). In [Zalta, Edward N.](https://en.wikipedia.org/wiki/Edward_N._Zalta "Edward N. Zalta") (ed.). _[Stanford Encyclopedia of Philosophy](https://en.wikipedia.org/wiki/Stanford_Encyclopedia_of_Philosophy "Stanford Encyclopedia of Philosophy")_.
*   [Artificial Intelligence](https://www.bbc.co.uk/programmes/p003k9fc). BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (_In Our Time_, 8 December 2005).
*   [Theranostics and AI – The Next Advance in Cancer Precision Medicine](https://datascience.cancer.gov/news-events/blog/theranostics-and-ai-next-advance-cancer-precision-medicine)