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{{Short description|Intelligence of machines}}
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<!-- DEFINITIONS -->
'''Artificial intelligence''' ('''AI'''), in its broadest sense, is [[intelligence]] exhibited by [[machine]]s, particularly [[computer|computer systems]]. It is a [[field of research]] in [[computer science]] that develops and studies methods and [[software]] that enable machines to [[Machine perception|perceive their environment]] and use [[machine learning|learning]] and intelligence to take actions that maximize their chances of achieving defined goals.{{Sfnp|Russell|Norvig|2021|pp=1–4}} Such machines may be called AIs.

<!-- APPLICATIONS -->
Some high-profile [[applications of AI]] include advanced [[web search engine]]s (e.g., [[Google Search]]); [[recommendation systems]] (used by [[YouTube]], [[Amazon (company)|Amazon]], and [[Netflix]]); interacting [[natural-language understanding|via human speech]] (e.g., [[Google Assistant]], [[Siri]], and [[Amazon Alexa|Alexa]]); [[autonomous vehicles]] (e.g., [[Waymo]]); [[Generative artificial intelligence|generative]] and [[Computational creativity|creative]] tools (e.g., [[ChatGPT]], and [[AI art]]); and [[Superintelligence|superhuman]] play and analysis in [[strategy game]]s (e.g., [[chess]] and [[Go (game)|Go]]). 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 [[AI effect|not labeled AI anymore]]."<ref>[http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/ AI set to exceed human brain power] {{Webarchive|url=https://web.archive.org/web/20080219001624/http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/|date=2008-02-19}} CNN.com (July 26, 2006)</ref><ref>{{Cite journal |last1=Kaplan |first1=Andreas |last2=Haenlein |first2=Michael |date=2019 |title=Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence |journal=Business Horizons |volume=62 |pages=15–25 |doi=10.1016/j.bushor.2018.08.004 |issn=0007-6813 |s2cid=158433736}}</ref>

<!-- GOALS AND TOOLS: SCOPE OF AI -->
The various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include [[automated reasoning|reasoning]], [[knowledge representation]], [[Automated planning and scheduling|planning]], [[machine learning|learning]], [[natural language processing]], perception, and support for [[robotics]].{{Efn|name="Problems of AI"}} [[Artificial general intelligence|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.<ref name="Artificial general intelligence"/> To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including [[state space search|search]] and [[mathematical optimization]], [[formal logic]], [[artificial neural network]]s, and methods based on [[statistics]], [[operations research]], and [[economics]].{{Efn|name="Tools of AI"}} AI also draws upon [[psychology]], [[linguistics]], [[Philosophy of artificial intelligence|philosophy]], [[neuroscience]], and other fields.<ref>{{Harvtxt|Russell|Norvig|2021|loc=§1.2}}.</ref>

<!-- HISTORY AND ETHICS -->
Artificial intelligence was founded as an academic discipline in 1956,<ref name="Dartmouth workshop"/> and the field went through multiple cycles of optimism,<ref name="Succ1"/><ref name="Fund01"/> followed by periods of disappointment and loss of funding, known as [[AI winter]].<ref name="First AI Winter"/><ref name="Second AI Winter"/> Funding and interest vastly increased after 2012 when [[deep learning]] outperformed previous AI techniques.<ref name="Deep learning revolution"/> This growth accelerated further after 2017 with the [[transformer architecture]],{{Sfnp|Toews|2023}} and by the early 2020s hundreds of billions of dollars were being invested in AI (known as the "[[AI boom]]"). The widespread use of AI in the 21st century exposed several unintended consequences and harms in the present and raised concerns about [[AI risk|its risks]] and [[AI aftermath scenarios|long-term effects]] in the future, prompting discussions about [[Regulation of artificial intelligence|regulatory policies]] to ensure the [[AI safety|safety and benefits of the technology]].

== 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.{{Efn|name="Problems of AI"|This list of intelligent traits is based on the topics covered by the major AI textbooks, including: {{Harvtxt|Russell|Norvig|2021}}, {{Harvtxt|Luger|Stubblefield|2004}}, {{Harvtxt|Poole|Mackworth|Goebel|1998}} and {{Harvtxt|Nilsson|1998}}}}

=== Reasoning and problem-solving ===
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical [[Deductive reasoning|deductions]].<ref>Problem-solving, puzzle solving, game playing, and deduction: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 3–5}}, {{Harvtxt|Russell|Norvig|2021|loc=chpt. 6}} ([[constraint satisfaction]]), {{Harvtxt|Poole|Mackworth|Goebel|1998|loc=chpt. 2, 3, 7, 9}}, {{Harvtxt|Luger|Stubblefield|2004|loc=chpt. 3, 4, 6, 8}}, {{Harvtxt|Nilsson|1998|loc=chpt. 7–12}}</ref> By the late 1980s and 1990s, methods were developed for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].<ref>Uncertain reasoning: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 12–18}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=345–395}}, {{Harvtxt|Luger|Stubblefield|2004|pp=333–381}}, {{Harvtxt|Nilsson|1998|loc=chpt. 7–12}}</ref>

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.<ref name="Intractability and efficiency and the combinatorial explosion">[[Intractably|Intractability and efficiency]] and the [[combinatorial explosion]]: {{Harvtxt|Russell|Norvig|2021|p=21}}</ref> 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.<ref name="Psychological evidence of the prevalence of sub">Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: {{Harvtxt|Kahneman|2011}}, {{Harvtxt|Dreyfus|Dreyfus|1986}}, {{Harvtxt|Wason|Shapiro|1966}}, {{Harvtxt|Kahneman|Slovic|Tversky|1982}}</ref> Accurate and efficient reasoning is an unsolved problem.

=== Knowledge representation ===
[[File:General Formal Ontology.svg|thumb|upright=1.2|An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.]]

[[Knowledge representation]] and [[knowledge engineering]]<ref>[[Knowledge representation]] and [[knowledge engineering]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 10}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=23–46, 69–81, 169–233, 235–277, 281–298, 319–345}}, {{Harvtxt|Luger|Stubblefield|2004|pp=227–243}}, {{Harvtxt|Nilsson|1998|loc=chpt. 17.1–17.4, 18}}</ref> 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,{{Sfnp|Smoliar|Zhang|1994}} scene interpretation,{{Sfnp|Neumann|Möller|2008}} clinical decision support,{{Sfnp|Kuperman|Reichley|Bailey|2006}} knowledge discovery (mining "interesting" and actionable inferences from large [[database]]s),{{Sfnp|McGarry|2005}} and other areas.{{Sfnp|Bertini|Del Bimbo|Torniai|2006}}

A [[knowledge base]] is a body of knowledge represented in a form that can be used by a program. An [[ontology (information science)|ontology]] is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.{{Sfnp|Russell|Norvig|2021|pp=272}} Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;<ref>Representing categories and relations: [[Semantic network]]s, [[description logic]]s, [[Inheritance (object-oriented programming)|inheritance]] (including [[Frame (artificial intelligence)|frames]], and [[Scripts (artificial intelligence)|scripts]]): {{Harvtxt|Russell|Norvig|2021|loc=§10.2 & 10.5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=174–177}}, {{Harvtxt|Luger|Stubblefield|2004|pp=248–258}}, {{Harvtxt|Nilsson|1998|loc=chpt. 18.3}}</ref> situations, events, states, and time;<ref>Representing events and time:[[Situation calculus]], [[event calculus]], [[fluent calculus]] (including solving the [[frame problem]]): {{Harvtxt|Russell|Norvig|2021|loc=§10.3}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=281–298}}, {{Harvtxt|Nilsson|1998|loc=chpt. 18.2}}</ref> causes and effects;<ref>[[Causality#Causal calculus|Causal calculus]]: {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=335–337}}</ref> knowledge about knowledge (what we know about what other people know);<ref>Representing knowledge about knowledge: Belief calculus, [[modal logic]]s: {{Harvtxt|Russell|Norvig|2021|loc=§10.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=275–277}}</ref> [[default reasoning]] (things that humans assume are true until they are told differently and will remain true even when other facts are changing);<ref name="Default reasoning">[[Default reasoning]], [[Frame problem]], [[default logic]], [[non-monotonic logic]]s, [[circumscription (logic)|circumscription]], [[closed world assumption]], [[abductive reasoning|abduction]]: {{Harvtxt|Russell|Norvig|2021|loc=§10.6}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=248–256, 323–335}}, {{Harvtxt|Luger|Stubblefield|2004|pp=335–363}}, {{Harvtxt|Nilsson|1998|loc=~18.3.3}}
(Poole ''et al.'' places abduction under "default reasoning". Luger ''et al.'' places this under "uncertain reasoning").</ref> 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);<ref name="Breadth of commonsense knowledge">Breadth of commonsense knowledge: {{Harvtxt|Lenat|Guha|1989|loc=Introduction}}, {{Harvtxt|Crevier|1993|pp=113–114}}, {{Harvtxt|Moravec|1988|p=13}}, {{Harvtxt|Russell|Norvig|2021|pp=241, 385, 982}} ([[qualification problem]])</ref> 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).<ref name="Psychological evidence of the prevalence of sub"/> There is also the difficulty of [[knowledge acquisition]], the problem of obtaining knowledge for AI applications.{{Efn|It is among the reasons that [[expert system]]s proved to be inefficient for capturing knowledge.{{Sfnp|Newquist|1994|p=296}}{{Sfnp|Crevier|1993|pp=204–208}}}}

=== Planning and decision-making ===
An "agent" is anything that perceives and takes actions in the world. A [[rational agent]] has goals or preferences and takes actions to make them happen.{{Efn|
"Rational agent" is general term used in [[economics]], [[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.
}}{{Sfnp|Russell|Norvig|2021|p=528}} In [[automated planning]], the agent has a specific goal.<ref>[[Automated planning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 11}}.</ref> In [[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]]") that measures how much the agent prefers it. For each possible action, it can calculate the "[[expected utility]]": the [[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.<ref>[[Automated decision making]], [[Decision theory]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 16–18}}.</ref>

In [[Automated planning and scheduling#classical planning|classical planning]], the agent knows exactly what the effect of any action will be.<ref>[[Automated planning and scheduling#classical planning|Classical planning]]: {{Harvtxt|Russell|Norvig|2021|loc=Section 11.2}}.</ref> 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.<ref>Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning): {{Harvtxt|Russell|Norvig|2021|loc=Section 11.5}}.</ref>

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]]), or the agent can seek information to improve its preferences.<ref>Uncertain preferences: {{Harvtxt|Russell|Norvig|2021|loc=Section 16.7}}
[[Inverse reinforcement learning]]: {{Harvtxt|Russell|Norvig|2021|loc=Section 22.6}}</ref> [[Information value theory]] can be used to weigh the value of exploratory or experimental actions.<ref>[[Information value theory]]: {{Harvtxt|Russell|Norvig|2021|loc=Section 16.6}}.</ref> The space of possible future actions and situations is typically [[intractably]] large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.

A [[Markov decision process]] has a [[Finite-state machine|transition model]] that describes the probability that a particular action will change the state in a particular way and a [[reward function]] that supplies the utility of each state and the cost of each action. A [[Reinforcement learning#Policy|policy]] associates a decision with each possible state. The policy could be calculated (e.g., by [[policy iteration|iteration]]), be [[heuristic]], or it can be learned.<ref>[[Markov decision process]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 17}}.</ref>

[[Game theory]] describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.<ref>[[Game theory]] and multi-agent decision theory: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 18}}.</ref>

=== Learning ===
[[Machine learning]] is the study of programs that can improve their performance on a given task automatically.<ref>[[machine learning|Learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 19–22}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=397–438}}, {{Harvtxt|Luger|Stubblefield|2004|pp=385–542}}, {{Harvtxt|Nilsson|1998|loc=chpt. 3.3, 10.3, 17.5, 20}}</ref> It has been a part of AI from the beginning.{{Efn
|[[Alan Turing]] discussed the centrality of learning as early as 1950, in his classic paper "[[Computing Machinery and Intelligence]]".{{Sfnp|Turing|1950}} In 1956, at the original Dartmouth AI summer conference, [[Ray Solomonoff]] wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".{{Sfnp|Solomonoff|1956}}
}}

There are several kinds of machine learning. [[Unsupervised learning]] analyzes a stream of data and finds patterns and makes predictions without any other guidance.<ref>[[Unsupervised learning]]: {{Harvtxt|Russell|Norvig|2021|pp=653}} (definition), {{Harvtxt|Russell|Norvig|2021|pp=738–740}} ([[cluster analysis]]), {{Harvtxt|Russell|Norvig|2021|pp=846–860}} ([[word embedding]])</ref> [[Supervised learning]] requires a human to label the input data first, and comes in two main varieties: [[statistical classification|classification]] (where the program must learn to predict what category the input belongs in) and [[Regression analysis|regression]] (where the program must deduce a numeric function based on numeric input).<ref name="Supervised learning">[[Supervised learning]]: {{Harvtxt|Russell|Norvig|2021|loc=§19.2}} (Definition), {{Harvtxt|Russell|Norvig|2021|loc=Chpt. 19–20}} (Techniques)</ref>

In [[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".<ref>[[Reinforcement learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 22}}, {{Harvtxt|Luger|Stubblefield|2004|pp=442–449}}</ref> [[Transfer learning]] is when the knowledge gained from one problem is applied to a new problem.<ref>[[Transfer learning]]: {{Harvtxt|Russell|Norvig|2021|pp=281}}, {{Harvtxt|The Economist|2016}}</ref> [[Deep learning]] is a type of machine learning that runs inputs through biologically inspired [[artificial neural networks]] for all of these types of learning.<ref>{{Cite web |title=Artificial Intelligence (AI): What Is AI and How Does It Work? {{!}} Built In |url=https://builtin.com/artificial-intelligence |access-date=2023-10-30 |website=builtin.com}}</ref>

[[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization]].<ref>[[Computational learning theory]]: {{Harvtxt|Russell|Norvig|2021|pp=672–674}}, {{Harvtxt|Jordan|Mitchell|2015}}</ref>

{{Clear}}

=== Natural language processing ===
<!-- This is linked to in the introduction -->
[[Natural language processing]] (NLP)<ref>[[Natural language processing]] (NLP): {{Harvtxt|Russell|Norvig|2021|loc=chpt. 23–24}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=91–104}}, {{Harvtxt|Luger|Stubblefield|2004|pp=591–632}}</ref> allows programs to read, write and communicate in human languages such as [[English (language)|English]]. Specific problems include [[speech recognition]], [[speech synthesis]], [[machine translation]], [[information extraction]], [[information retrieval]] and [[question answering]].<ref>Subproblems of [[Natural language processing|NLP]]: {{Harvtxt|Russell|Norvig|2021|pp=849–850}}</ref>

Early work, based on [[Noam Chomsky]]'s [[generative grammar]] and [[semantic network]]s, had difficulty with [[word-sense disambiguation]]{{Efn|See {{Section link|AI winter|Machine translation and the ALPAC report of 1966
}}}} unless restricted to small domains called "[[blocks world|micro-worlds]]" (due to the common sense knowledge problem<ref name="Breadth of commonsense knowledge"/>). [[Margaret Masterman]] believed that it was meaning and not grammar that was the key to understanding languages, and that [[thesauri]] and not dictionaries should be the basis of computational language structure.

Modern deep learning techniques for NLP include [[word embedding]] (representing words, typically as [[Vector space|vectors]] encoding their meaning),{{Sfnp|Russell|Norvig|2021|pp=856–858}} [[transformer (machine learning model)|transformer]]s (a deep learning architecture using an [[Attention (machine learning)|attention]] mechanism),{{Sfnp|Dickson|2022}} and others.<ref>Modern statistical and deep learning approaches to [[Natural language processing|NLP]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 24}}, {{Harvtxt|Cambria|White|2014}}</ref> In 2019, [[generative pre-trained transformer]] (or "GPT") language models began to generate coherent text,{{Sfnp|Vincent|2019}}{{Sfnp|Russell|Norvig|2021|pp=875–878}} and by 2023, these models were able to get human-level scores on the [[bar exam]], [[SAT]] test, [[GRE]] test, and many other real-world applications.{{Sfnp|Bushwick|2023}}

=== Perception ===<!-- This is linked to in the introduction -->

[[Machine perception]] is the ability to use input from sensors (such as cameras, microphones, wireless signals, active [[lidar]], sonar, radar, and [[tactile sensor]]s) to deduce aspects of the world. [[Computer vision]] is the ability to analyze visual input.<ref>[[Computer vision]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 25}}, {{Harvtxt|Nilsson|1998|loc=chpt. 6}}</ref>

The field includes [[speech recognition]],{{Sfnp|Russell|Norvig|2021|pp=849–850}} [[image classification]],{{Sfnp|Russell|Norvig|2021|pp=895–899}} [[facial recognition system|facial recognition]], [[object recognition]],{{Sfnp|Russell|Norvig|2021|pp=899–901}}[[object tracking]],{{Sfnp|Challa|Moreland|Mušicki|Evans|2011}} and [[robotic perception]].{{Sfnp|Russell|Norvig|2021|pp=931–938}}

=== Social intelligence ===
[[File:Kismet-IMG 6007-gradient.jpg|thumb|[[Kismet (robot)|Kismet]], a robot head which was made in the 1990s; it is a machine that can recognize and simulate emotions.{{Sfnp|MIT AIL|2014}}]]

[[Affective computing]] is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human [[Affect (psychology)|feeling, emotion, and mood]].<ref>[[Affective computing]]: {{Harvtxt|Thro|1993}}, {{Harvtxt|Edelson|1991}}, {{Harvtxt|Tao|Tan|2005}}, {{Harvtxt|Scassellati|2002}}</ref> For example, some [[virtual assistant]]s 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]].

However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.{{Sfnp|Waddell|2018}} Moderate successes related to affective computing include textual [[sentiment analysis]] and, more recently, [[multimodal sentiment analysis]], wherein AI classifies the affects displayed by a videotaped subject.{{Sfnp|Poria|Cambria|Bajpai |Hussain|2017}}

=== General intelligence ===

A machine with [[artificial general intelligence]] should be able to solve a wide variety of problems with breadth and versatility similar to [[human intelligence]].<ref name="Artificial general intelligence" >
[[Artificial general intelligence]]: {{Harvtxt|Russell|Norvig|2021|pp=32–33, 1020–1021}}<br />Proposal for the modern version: {{Harvtxt|Pennachin|Goertzel|2007}}<br />Warnings of overspecialization in AI from leading researchers: {{Harvtxt|Nilsson|1995}}, {{Harvtxt|McCarthy|2007}}, {{Harvtxt|Beal|Winston|2009}}</ref>

== Techniques ==
AI research uses a wide variety of techniques to accomplish the goals above.{{Efn|name="Tools of AI"|This list of tools is based on the topics covered by the major AI textbooks, including: {{Harvtxt|Russell|Norvig|2021}}, {{Harvtxt|Luger|Stubblefield|2004}}, {{Harvtxt|Poole|Mackworth|Goebel|1998}} and {{Harvtxt|Nilsson|1998}}}}

=== Search and optimization ===
AI can solve many problems by intelligently searching through many possible solutions.<ref>[[Search algorithm]]s: {{Harvtxt|Russell|Norvig|2021|loc=chpts. 3–5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=113–163}}, {{Harvtxt|Luger|Stubblefield|2004|pp=79–164, 193–219}}, {{Harvtxt|Nilsson|1998|loc=chpts. 7–12}}</ref> There are two very different kinds of search used in AI: [[state space search]] and [[Local search (optimization)|local search]].

==== State space search ====
[[State space search]] searches through a tree of possible states to try to find a goal state.<ref>[[State space search]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 3}}</ref> For example, [[Automated planning and scheduling|planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [[means-ends analysis]].{{Sfnp|Russell|Norvig|2021|loc=sect. 11.2}}

[[Brute force search|Simple exhaustive searches]]<ref>[[Uninformed search]]es ([[breadth first search]], [[depth-first search]] and general [[state space search]]): {{Harvtxt|Russell|Norvig|2021|loc=sect. 3.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=113–132}}, {{Harvtxt|Luger|Stubblefield|2004|pp=79–121}}, {{Harvtxt|Nilsson|1998|loc=chpt. 8}}</ref> are rarely sufficient for most real-world problems: the [[Search algorithm|search space]] (the number of places to search) quickly grows to [[Astronomically large|astronomical numbers]]. The result is a search that is [[Computation time|too slow]] or never completes.<ref name="Intractability and efficiency and the combinatorial explosion"/> "[[Heuristics]]" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.<ref>[[Heuristic]] or informed searches (e.g., greedy [[Best-first search|best first]] and [[A* search algorithm|A*]]): {{Harvtxt|Russell|Norvig|2021|loc=sect. 3.5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=132–147}}, {{Harvtxt|Poole|Mackworth|2017|loc=sect. 3.6}}, {{Harvtxt|Luger|Stubblefield|2004|pp=133–150}}</ref>

[[Adversarial search]] is used for [[game AI|game-playing]] programs, such as chess or Go. It searches through a [[Game tree|tree]] of possible moves and counter-moves, looking for a winning position.<ref>[[Adversarial search]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 5}}</ref>

==== Local search ====
[[File:Gradient descent.gif|thumb|Illustration of [[gradient descent]] for 3 different starting points; two parameters (represented by the plan coordinates) are adjusted in order to minimize the [[loss function]] (the height)]] [[Local search (optimization)|Local search]] uses [[mathematical optimization]] to find a solution to a problem. It begins with some form of guess and refines it incrementally.<ref>[[Local search (optimization)|Local]] or "[[optimization]]" search: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 4}}</ref>

[[Gradient descent]] is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a [[loss function]]. Variants of [[gradient descent]] are commonly used to train neural networks.<ref>{{Cite web |last=Singh Chauhan |first=Nagesh |date=December 18, 2020 |title=Optimization Algorithms in Neural Networks |url=https://www.kdnuggets.com/optimization-algorithms-in-neural-networks |access-date=2024-01-13 |website=KDnuggets}}</ref>

Another type of local search is [[evolutionary computation]], which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, [[Artificial selection|selecting]] only the fittest to survive each generation.<ref>[[Evolutionary computation]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 4.1.2}}</ref>

Distributed search processes can coordinate via [[swarm intelligence]] algorithms. Two popular swarm algorithms used in search are [[particle swarm optimization]] (inspired by bird [[flocking]]) and [[ant colony optimization]] (inspired by [[ant trail]]s).{{Sfnp|Merkle|Middendorf|2013}}

=== Logic ===
Formal [[logic]] is used for [[automatic reasoning|reasoning]] and [[knowledge representation]].<ref>[[Logic]]: {{Harvtxt|Russell|Norvig|2021|loc=chpts. 6–9}}, {{Harvtxt|Luger|Stubblefield|2004|pp=35–77}}, {{Harvtxt|Nilsson|1998|loc=chpt. 13–16}}</ref>
Formal logic comes in two main forms: [[propositional logic]] (which operates on statements that are true or false and uses [[logical connective]]s such as "and", "or", "not" and "implies")<ref>[[Propositional logic]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 6}}, {{Harvtxt|Luger|Stubblefield|2004|pp=45–50}}, {{Harvtxt|Nilsson|1998|loc=chpt. 13}}</ref> and [[predicate logic]] (which also operates on objects, predicates and relations and uses [[Quantifier (logic)|quantifier]]s such as "''Every'' ''X'' is a ''Y''" and "There are ''some'' ''X''s that are ''Y''s").<ref>[[First-order logic]] and features such as [[Equality (mathematics)|equality]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 7}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=268–275}}, {{Harvtxt|Luger|Stubblefield|2004|pp=50–62}}, {{Harvtxt|Nilsson|1998|loc=chpt. 15}}</ref>

[[Deductive reasoning]] in logic is the process of [[logical proof|proving]] a new statement ([[Logical consequence|conclusion]]) from other statements that are given and assumed to be true (the [[premise]]s).<ref>[[Logical inference]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 10}}</ref> Proofs can be structured as proof [[tree structure|trees]], in which nodes are labelled by sentences, and children nodes are connected to parent nodes by [[inference rule]]s.

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 [[axiom]]s. In the case of [[Horn clause]]s, problem-solving search can be performed by reasoning [[Forward chaining|forwards]] from the premises or [[backward chaining|backwards]] from the problem.<ref>logical deduction as search: {{Harvtxt|Russell|Norvig|2021|loc=sects. 9.3, 9.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=~46–52}}, {{Harvtxt|Luger|Stubblefield|2004|pp=62–73}}, {{Harvtxt|Nilsson|1998|loc=chpt. 4.2, 7.2}}</ref> In the more general case of the clausal form of [[first-order logic]], [[resolution (logic)|resolution]] 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.<ref>[[Resolution (logic)|Resolution]] and [[unification (computer science)|unification]]: {{Harvtxt|Russell|Norvig|2021|loc= sections 7.5.2, 9.2, 9.5}}</ref>

Inference in both Horn clause logic and first-order logic is [[Undecidable problem|undecidable]], and therefore [[Intractable problem|intractable]]. However, backward reasoning with Horn clauses, which underpins computation in the [[logic programming]] language [[Prolog]], is [[Turing complete]]. Moreover, its efficiency is competitive with computation in other [[symbolic programming]] languages.<ref>{{Cite journal |last1=Warren |first1=D.H. |last2=Pereira |first2=L.M. |last3=Pereira |first3=F. |date=1977 |title=Prolog-the language and its implementation compared with Lisp |journal=[[ACM SIGPLAN Notices]] |volume=12 |issue=8 |pages=109–115 |doi=10.1145/872734.806939}}</ref>

[[Fuzzy logic]] assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.<ref>Fuzzy logic: {{Harvtxt|Russell|Norvig|2021|pp=214, 255, 459}}, {{Harvtxt|Scientific American|1999}}</ref>

[[Non-monotonic logic]]s, including logic programming with [[negation as failure]], are designed to handle [[default reasoning]].<ref name="Default reasoning"/> Other specialized versions of logic have been developed to describe many complex domains.

=== Probabilistic methods for uncertain reasoning ===
[[File:SimpleBayesNet.svg|thumb|upright=1.7|A simple [[Bayesian network]], with the associated [[conditional probability table]]s]]
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]] theory and economics.<ref name="Stoch">Stochastic methods for uncertain reasoning: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 12–18, 20}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=345–395}}, {{Harvtxt|Luger|Stubblefield|2004|pp=165–191, 333–381}}, {{Harvtxt|Nilsson|1998|loc=chpt. 19}}</ref> Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[decision analysis]],<ref>[[decision theory]] and [[decision analysis]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 16–18}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=381–394}}</ref> and [[information value theory]].<ref>[[Information value theory]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 16.6}}</ref> These tools include models such as [[Markov decision process]]es,<ref>[[Markov decision process]]es and dynamic [[decision network]]s: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 17}}</ref> dynamic [[decision network]]s,<ref name="Stochastic temporal models"/> [[game theory]] and [[mechanism design]].<ref>[[Game theory]] and [[mechanism design]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 18}}</ref>

[[Bayesian network]]s<ref>[[Bayesian network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sects. 12.5–12.6, 13.4–13.5, 14.3–14.5, 16.5, 20.2–20.3}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=361–381}}, {{Harvtxt|Luger|Stubblefield|2004|pp=~182–190, ≈363–379}}, {{Harvtxt|Nilsson|1998|loc=chpt. 19.3–19.4}}</ref> are a tool that can be used for [[automated reasoning|reasoning]] (using the [[Bayesian inference]] algorithm),{{Efn|
Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be [[conditionally independent]] of one another. [[AdSense]] uses a Bayesian network with over 300&nbsp;million edges to learn which ads to serve.{{Sfnp|Domingos|2015|loc=chpt. 6}}
}}<ref>[[Bayesian inference]] algorithm: {{Harvtxt|Russell|Norvig|2021|loc=sect. 13.3–13.5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=361–381}}, {{Harvtxt|Luger|Stubblefield|2004|pp=~363–379}}, {{Harvtxt|Nilsson|1998|loc=chpt. 19.4 & 7}}</ref> [[Machine learning|learning]] (using the [[expectation–maximization algorithm]]),{{Efn|Expectation–maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown [[latent variables]].{{Sfnp|Domingos|2015|p=210}}}}<ref>[[Bayesian learning]] and the [[expectation–maximization algorithm]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 20}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=424–433}}, {{Harvtxt|Nilsson|1998|loc=chpt. 20}}, {{Harvtxt|Domingos|2015|p=210}}</ref> [[Automated planning and scheduling|planning]] (using [[decision network]]s)<ref>[[Bayesian decision theory]] and Bayesian [[decision network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 16.5}}</ref> and [[Machine perception|perception]] (using [[dynamic Bayesian network]]s).<ref name="Stochastic temporal models"/>

Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping [[Machine perception|perception]] systems analyze processes that occur over time (e.g., [[hidden Markov model]]s or [[Kalman filter]]s).<ref name="Stochastic temporal models">Stochastic temporal models: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 14}}
[[Hidden Markov model]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 14.3}}
[[Kalman filter]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 14.4}}
[[Dynamic Bayesian network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 14.5}}</ref>

[[File:EM_Clustering_of_Old_Faithful_data.gif|thumb|upright=1.2|[[Expectation–maximization algorithm|Expectation–maximization]] [[cluster analysis|clustering]] of [[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. [[Classifier (mathematics)|Classifiers]]<ref>Statistical learning methods and [[Classifier (mathematics)|classifiers]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 20}},</ref> are functions that use [[pattern matching]] to determine the closest match. They can be fine-tuned based on chosen examples using [[supervised learning]]. Each pattern (also called an "[[random variate|observation]]") is labeled with a certain predefined class. All the observations combined with their class labels are known as a [[data set]]. When a new observation is received, that observation is classified based on previous experience.<ref name="Supervised learning"/>

There are many kinds of classifiers in use.<ref>{{Cite book |last1=Ciaramella |first1=Alberto |author-link=Alberto Ciaramella |title=Introduction to Artificial Intelligence: from data analysis to generative AI |last2=Ciaramella |first2=Marco |date=2024 |publisher=Intellisemantic Editions |isbn=978-8-8947-8760-3}}</ref> The [[decision tree]] is the simplest and most widely used symbolic machine learning algorithm.<ref>[[Alternating decision tree|Decision tree]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 19.3}}, {{Harvtxt|Domingos|2015|p=88}}</ref> [[K-nearest neighbor]] algorithm was the most widely used analogical AI until the mid-1990s, and [[Kernel methods]] such as the [[support vector machine]] (SVM) displaced k-nearest neighbor in the 1990s.<ref>[[Nonparametric statistics|Non-parameteric]] learning models such as [[K-nearest neighbor]] and [[support vector machines]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 19.7}}, {{Harvtxt|Domingos|2015|p=187}} (k-nearest neighbor)
* {{Harvtxt|Domingos|2015|p=88}} (kernel methods)</ref>
The [[naive Bayes classifier]] is reportedly the "most widely used learner"{{Sfnp|Domingos|2015|p=152}} at Google, due in part to its scalability.<ref>[[Naive Bayes classifier]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 12.6}}, {{Harvtxt|Domingos|2015|p=152}}</ref>
[[Artificial neural network|Neural networks]] are also used as classifiers.<ref name="Neural networks"/>

=== Artificial neural networks ===
[[File:Artificial_neural_network.svg|right|thumb|A neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in the [[human brain]].]]

An artificial neural network is based on a collection of nodes also known as [[artificial neurons]], which loosely model the [[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 [[Weighting|weight]] 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.<ref name="Neural networks">Neural networks: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 21}}, {{Harvtxt|Domingos|2015|loc=Chapter 4}}</ref>

Learning algorithms for neural networks use [[local search (optimization)|local search]] to choose the weights that will get the right output for each input during training. The most common training technique is the [[backpropagation]] algorithm.<ref>Gradient calculation in computational graphs, [[backpropagation]], [[automatic differentiation]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.2}}, {{Harvtxt|Luger|Stubblefield|2004|pp=467–474}}, {{Harvtxt|Nilsson|1998|loc=chpt. 3.3}}</ref> Neural networks learn to model complex relationships between inputs and outputs and [[Pattern recognition|find patterns]] in data. In theory, a neural network can learn any function.<ref>[[Universal approximation theorem]]: {{Harvtxt|Russell|Norvig|2021|p=752}}
The theorem: {{Harvtxt|Cybenko|1988}}, {{Harvtxt|Hornik|Stinchcombe|White|1989}}</ref>

In [[feedforward neural network]]s the signal passes in only one direction.<ref>[[Feedforward neural network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.1}}</ref> [[Recurrent neural network]]s feed the output signal back into the input, which allows short-term memories of previous input events. [[Long short term memory]] is the most successful network architecture for recurrent networks.<ref>[[Recurrent neural network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.6}}</ref> [[Perceptron]]s<ref>[[Perceptron]]s: {{Harvtxt|Russell|Norvig|2021|pp=21, 22, 683, 22}}</ref> use only a single layer of neurons; deep learning<ref name="Deep learning"/> uses multiple layers. [[Convolutional neural network]]s strengthen the connection between neurons that are "close" to each other—this is especially important in [[image processing]], where a local set of neurons must [[edge detection|identify an "edge"]] before the network can identify an object.<ref>[[Convolutional neural networks]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.3}}</ref>

{{Clear}}

=== Deep learning ===
[[File:AI hierarchy.svg|thumb|upright]]
[[Deep learning]]<ref name="Deep learning">[[Deep learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 21}}, {{Harvtxt|Goodfellow|Bengio|Courville|2016}}, {{Harvtxt|Hinton ''et al.''|2016}}, {{Harvtxt|Schmidhuber|2015}}</ref> 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]], lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.{{Sfnp|Deng|Yu|2014|pp=199–200}}

Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including [[computer vision]], [[speech recognition]], [[natural language processing]], [[image classification]],{{Sfnp|Ciresan|Meier|Schmidhuber|2012}} and others. The reason that deep learning performs so well in so many applications is not known as of 2023.{{Sfnp|Russell|Norvig|2021|p=751}} 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){{Efn|
Some form of deep neural networks (without a specific learning algorithm) were described by:
[[Warren S. McCulloch]] and [[Walter Pitts]] (1943){{Sfnp|Russell|Norvig|2021|p=17}}
[[Alan Turing]] (1948);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Karl Steinbuch]] and [[Roger David Joseph]] (1961).{{Sfnp|Schmidhuber|2022|loc=sect. 5}}
Deep or recurrent networks that learned (or used gradient descent) were developed by:
[[Frank Rosenblatt]](1957);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Oliver Selfridge]] (1959);{{Sfnp|Schmidhuber|2022|loc=sect. 5}}
[[Alexey Ivakhnenko]] and [[Valentin Lapa]] (1965);{{Sfnp|Schmidhuber|2022|loc=sect. 6}}
[[Kaoru Nakano]] (1971);{{Sfnp|Schmidhuber|2022|loc=sect. 7}}
[[Shun-Ichi Amari]] (1972);{{Sfnp|Schmidhuber|2022|loc=sect. 7}}
[[John Joseph Hopfield]] (1982).{{Sfnp|Schmidhuber|2022|loc=sect. 7}}
Precursors to backpropagation were developed by:
[[Henry J. Kelley]] (1960);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Arthur E. Bryson]] (1962);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Stuart Dreyfus]] (1962);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Arthur E. Bryson]] and [[Yu-Chi Ho]] (1969);{{Sfnp|Russell|Norvig|2021|p=785}}
Backpropagation was independently developed by:
[[Seppo Linnainmaa]] (1970);{{Sfnp|Schmidhuber|2022|loc=sect. 8}}
[[Paul Werbos]] (1974).{{Sfnp|Russell|Norvig|2021|p=785}}
}} but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to [[GPU]]s) and the availability of vast amounts of training data, especially the giant [[List of datasets for machine-learning research|curated datasets]] used for benchmark testing, such as [[ImageNet]].{{Efn|[[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."<ref>Quoted in {{Harvtxt|Christian|2020|p=22}}</ref>}}

===GPT===
[[Generative pre-trained transformer]]s (GPT) are [[large language model]]s (LLMs) that generate text based on the semantic relationships between words in sentences. Text-based GPT models are pretrained on a large [[corpus of text]] that can be from the Internet. The pretraining consists of predicting the next [[Lexical analysis|token]] (a token being usually a word, subword, or punctuation). Throughout this pretraining, 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]] (RLHF). Current GPT models are prone to generating falsehoods called "[[Hallucination (artificial intelligence)|hallucinations]]", although this can be reduced with RLHF and quality data. They are used in [[chatbot]]s, which allow people to ask a question or request a task in simple text.{{Sfnp|Smith|2023}}<ref>{{Cite web |date=9 November 2023 |title=Explained: Generative AI |url=https://news.mit.edu/2023/explained-generative-ai-1109}}</ref>

Current models and services include [[Gemini (chatbot)|Gemini]] (formerly Bard), [[ChatGPT]], [[Grok (chatbot)|Grok]], [[Anthropic#Claude|Claude]], [[Microsoft Copilot|Copilot]], and [[LLaMA]].<ref>{{Cite web |title=AI Writing and Content Creation Tools |url=https://mitsloanedtech.mit.edu/ai/tools/writing |access-date=25 December 2023 |publisher=MIT Sloan Teaching & Learning Technologies |archive-date=25 December 2023 |archive-url=https://web.archive.org/web/20231225232503/https://mitsloanedtech.mit.edu/ai/tools/writing/ |url-status=live }}</ref> [[Multimodal learning|Multimodal]] GPT models can process different types of data ([[Modality (human–computer interaction)|modalities]]) such as images, videos, sound, and text.{{Sfnp|Marmouyet|2023}}

===Hardware and software===
{{Main|Programming languages for artificial intelligence|Hardware for artificial intelligence}}

In the late 2010s, [[graphics processing unit]]s (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized [[TensorFlow]] software had replaced previously used [[central processing unit]] (CPUs) as the dominant means for large-scale (commercial and academic) [[machine learning]] models' training.{{Sfnp|Kobielus|2019}} Specialized [[programming language]]s such as [[Prolog]] were used in early AI research,<ref>{{Cite web |last=Thomason |first=James |date=2024-05-21 |title=Mojo Rising: The resurgence of AI-first programming languages |url=https://venturebeat.com/ai/mojo-rising-the-resurgence-of-ai-first-programming-languages |access-date=2024-05-26 |website=VentureBeat |archive-date=27 June 2024 |archive-url=https://web.archive.org/web/20240627143853/https://venturebeat.com/ai/mojo-rising-the-resurgence-of-ai-first-programming-languages/ |url-status=live }}</ref> but [[general-purpose programming language]]s like [[Python (programming language)|Python]] have become predominant.<ref>{{Cite news |last=Wodecki |first=Ben |date=May 5, 2023 |title=7 AI Programming Languages You Need to Know |url=https://aibusiness.com/verticals/7-ai-programming-languages-you-need-to-know |work=AI Business |access-date=5 October 2024 |archive-date=25 July 2024 |archive-url=https://web.archive.org/web/20240725164443/https://aibusiness.com/verticals/7-ai-programming-languages-you-need-to-know |url-status=live }}</ref>

The transistor density in [[integrated circuit]]s has been observed to roughly double every 18 months—a trend known as [[Moore's law]], named after the [[Intel]] co-founder [[Gordon Moore]], who first identified it. Improvements in [[GPUs]] have been even faster.<ref>{{Cite web |last=Plumb |first=Taryn |date=2024-09-18 |title=Why Jensen Huang and Marc Benioff see 'gigantic' opportunity for agentic AI |url=https://venturebeat.com/ai/why-jensen-huang-and-marc-benioff-see-gigantic-opportunity-for-agentic-ai/ |access-date=2024-10-04 |website=VentureBeat |language=en-US |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165649/https://venturebeat.com/ai/why-jensen-huang-and-marc-benioff-see-gigantic-opportunity-for-agentic-ai/ |url-status=live }}</ref>

== Applications ==
{{Main|Applications of artificial intelligence}}AI and machine learning technology is used in most of the essential applications of the 2020s, including: [[search engines]] (such as [[Google Search]]), [[Targeted advertising|targeting online advertisements]], [[recommendation systems]] (offered by [[Netflix]], [[YouTube]] or [[Amazon (company)|Amazon]]), driving [[internet traffic]], [[Marketing and artificial intelligence|targeted advertising]] ([[AdSense]], [[Facebook]]), [[virtual assistant]]s (such as [[Siri]] or [[Amazon Alexa|Alexa]]), [[autonomous vehicles]] (including [[Unmanned aerial vehicle|drones]], [[Advanced driver-assistance system|ADAS]] and [[self-driving cars]]), [[automatic language translation]] ([[Microsoft Translator]], [[Google Translate]]), [[Facial recognition system|facial recognition]] ([[Apple Computer|Apple]]'s [[Face ID]] or [[Microsoft]]'s [[DeepFace]] and [[Google]]'s [[FaceNet]]) and [[image labeling]] (used by [[Facebook]], Apple's [[iPhoto]] and [[TikTok]]). The deployment of AI may be overseen by a [[Chief automation officer]] (CAO).

===Health and medicine===
{{Main|Artificial intelligence in healthcare}}
The application of AI in [[medicine]] and [[medical research]] has the potential to increase patient care and quality of life.<ref>{{Cite journal |last1=Davenport |first1=T |last2=Kalakota |first2=R |date=June 2019 |title=The potential for artificial intelligence in healthcare |journal=Future Healthc J. |volume=6 |issue=2 |pages=94–98 |doi=10.7861/futurehosp.6-2-94 |pmc=6616181 |pmid=31363513}}</ref> Through the lens of the [[Hippocratic Oath]], medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.<ref>{{Cite journal |last1=Lyakhova |first1=U.A. |last2=Lyakhov |first2=P.A. |date=2024 |title=Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects |url=https://linkinghub.elsevier.com/retrieve/pii/S0010482524008278 |journal=Computers in Biology and Medicine |language=en |volume=178 |pages=108742 |doi=10.1016/j.compbiomed.2024.108742|pmid=38875908 }}</ref><ref>{{Cite journal |last1=Alqudaihi |first1=Kawther S. |last2=Aslam |first2=Nida |last3=Khan |first3=Irfan Ullah |last4=Almuhaideb |first4=Abdullah M. |last5=Alsunaidi |first5=Shikah J. |last6=Ibrahim |first6=Nehad M. Abdel Rahman |last7=Alhaidari |first7=Fahd A. |last8=Shaikh |first8=Fatema S. |last9=Alsenbel |first9=Yasmine M. |last10=Alalharith |first10=Dima M. |last11=Alharthi |first11=Hajar M. |last12=Alghamdi |first12=Wejdan M. |last13=Alshahrani |first13=Mohammed S. |date=2021 |title=Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities |journal=IEEE Access |volume=9 |pages=102327–102344 |doi=10.1109/ACCESS.2021.3097559 |issn=2169-3536 |pmc=8545201 |pmid=34786317|bibcode=2021IEEEA...9j2327A }}</ref>

For medical research, AI is an important tool for processing and integrating [[big data]]. This is particularly important for [[organoid]] and [[tissue engineering]] development which use [[microscopy]] imaging as a key technique in fabrication.<ref name="Bax-2023">{{Cite journal |last1=Bax |first1=Monique |last2=Thorpe |first2=Jordan |last3=Romanov |first3=Valentin |date=December 2023 |title=The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence |journal=Frontiers in Sensors |volume=4 |doi=10.3389/fsens.2023.1294721 |issn=2673-5067 |doi-access=free}}</ref> It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.<ref name="Bax-2023"/> New AI tools can deepen the understanding of biomedically relevant pathways. For example, [[AlphaFold 2]] (2021) demonstrated the ability to approximate, in hours rather than months, the 3D [[Protein structure|structure of a protein]].<ref>{{Cite journal |last1=Jumper |first1=J |last2=Evans |first2=R |last3=Pritzel |first3=A |date=2021 |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |volume=596 |issue=7873 |pages=583–589 |bibcode=2021Natur.596..583J |doi=10.1038/s41586-021-03819-2 |pmc=8371605 |pmid=34265844}}</ref> 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.<ref>{{Cite web |date=2023-12-20 |title=AI discovers new class of antibiotics to kill drug-resistant bacteria |url=https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/ |access-date=5 October 2024 |archive-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916014421/https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/ |url-status=live }}</ref> In 2024, researchers used machine learning to accelerate the search for [[Parkinson's disease]] drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of [[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.<ref>{{Cite web |date=2024-04-17 |title=AI speeds up drug design for Parkinson's ten-fold |url=https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold |publisher=Cambridge University |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165755/https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold |url-status=live }}</ref><ref>{{Cite journal |last1=Horne |first1=Robert I. |last2=Andrzejewska |first2=Ewa A. |last3=Alam |first3=Parvez |last4=Brotzakis |first4=Z. Faidon |last5=Srivastava |first5=Ankit |last6=Aubert |first6=Alice |last7=Nowinska |first7=Magdalena |last8=Gregory |first8=Rebecca C. |last9=Staats |first9=Roxine |last10=Possenti |first10=Andrea |last11=Chia |first11=Sean |last12=Sormanni |first12=Pietro |last13=Ghetti |first13=Bernardino |last14=Caughey |first14=Byron |last15=Knowles |first15=Tuomas P. J. |last16=Vendruscolo |first16=Michele |date=2024-04-17 |title=Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning |journal=Nature Chemical Biology |publisher=Nature |volume=20 |issue=5 |pages=634–645 |doi=10.1038/s41589-024-01580-x |pmc=11062903 |pmid=38632492}}</ref>

=== Games ===
{{Main|Game artificial intelligence}}

[[Game AI|Game playing]] programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.<ref>{{Cite magazine |last1=Grant |first1=Eugene F. |last2=Lardner |first2=Rex |date=1952-07-25 |title=The Talk of the Town – It |url=https://www.newyorker.com/magazine/1952/08/02/it |access-date=2024-01-28 |magazine=The New Yorker |issn=0028-792X |archive-date=16 February 2020 |archive-url=https://web.archive.org/web/20200216034025/https://www.newyorker.com/magazine/1952/08/02/it |url-status=live }}</ref> [[IBM Deep Blue|Deep Blue]] became the first computer chess-playing system to beat a reigning world chess champion, [[Garry Kasparov]], on 11 May 1997.<ref>{{Cite web |last=Anderson |first=Mark Robert |date=2017-05-11 |title=Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution |url=http://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |access-date=2024-01-28 |website=The Conversation |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917000827/https://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |url-status=live }}</ref> In 2011, in a ''[[Jeopardy!]]'' [[quiz show]] exhibition match, [[IBM]]'s [[question answering system]], [[Watson (artificial intelligence software)|Watson]], defeated the two greatest ''Jeopardy!'' champions, [[Brad Rutter]] and [[Ken Jennings]], by a significant margin.<ref>{{Cite news |last=Markoff |first=John |date=2011-02-16 |title=Computer Wins on 'Jeopardy!': Trivial, It's Not |url=https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-access=subscription |access-date=2024-01-28 |work=The New York Times |issn=0362-4331 |archive-date=22 October 2014 |archive-url=https://web.archive.org/web/20141022023202/http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-status=live }}</ref> In March 2016, [[AlphaGo]] won 4 out of 5 games of [[Go (game)|Go]] in a match with Go champion [[Lee Sedol]], becoming the first [[computer Go]]-playing system to beat a professional Go player without [[Go handicaps|handicaps]]. Then, in 2017, it [[AlphaGo versus Ke Jie|defeated Ke Jie]], who was the best Go player in the world.<ref>{{Cite web |last=Byford |first=Sam |date=2017-05-27 |title=AlphaGo retires from competitive Go after defeating world number one 3–0 |url=https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |access-date=2024-01-28 |website=The Verge |archive-date=7 June 2017 |archive-url=https://web.archive.org/web/20170607184301/https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |url-status=live }}</ref> Other programs handle [[Imperfect information|imperfect-information]] games, such as the [[poker]]-playing program [[Pluribus (poker bot)|Pluribus]].<ref>{{Cite journal |last1=Brown |first1=Noam |last2=Sandholm |first2=Tuomas |date=2019-08-30 |title=Superhuman AI for multiplayer poker |url=https://www.science.org/doi/10.1126/science.aay2400 |journal=Science |volume=365 |issue=6456 |pages=885–890 |bibcode=2019Sci...365..885B |doi=10.1126/science.aay2400 |issn=0036-8075 |pmid=31296650}}</ref> [[DeepMind]] developed increasingly generalistic [[reinforcement learning]] models, such as with [[MuZero]], which could be trained to play chess, Go, or [[Atari]] games.<ref>{{Cite web |date=2020-12-23 |title=MuZero: Mastering Go, chess, shogi and Atari without rules |url=https://deepmind.google/discover/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules |access-date=2024-01-28 |website=Google DeepMind}}</ref> In 2019, DeepMind's AlphaStar achieved grandmaster level in [[StarCraft II]], a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map.<ref>{{Cite news |last=Sample |first=Ian |date=2019-10-30 |title=AI becomes grandmaster in 'fiendishly complex' StarCraft II |url=https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |access-date=2024-01-28 |work=The Guardian |issn=0261-3077 |archive-date=29 December 2020 |archive-url=https://web.archive.org/web/20201229185547/https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |url-status=live }}</ref> In 2021, an AI agent competed in a PlayStation [[Gran Turismo (series)|Gran Turismo]] competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.<ref>{{Cite journal |last1=Wurman |first1=P. R. |last2=Barrett |first2=S. |last3=Kawamoto |first3=K. |date=2022 |title=Outracing champion Gran Turismo drivers with deep reinforcement learning |journal=Nature |volume=602 |issue=7896 |pages=223–228 |bibcode=2022Natur.602..223W |doi=10.1038/s41586-021-04357-7 |pmid=35140384|url=https://www.researchsquare.com/article/rs-795954/latest.pdf }}</ref> In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen [[open-world]] video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.<ref>{{Cite web |last=Wilkins |first=Alex |date=13 March 2024 |title=Google AI learns to play open-world video games by watching them |url=https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them |access-date=2024-07-21 |website=New Scientist |archive-date=26 July 2024 |archive-url=https://web.archive.org/web/20240726182946/https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them/ |url-status=live }}</ref>

=== Mathematics ===
In mathematics, special forms of formal step-by-step [[Automatic reasoning|reasoning]] are used. In contrast, LLMs such as ''[[GPT-4]] Turbo'', ''[[Gemini (chatbot)|Gemini Ultra]]'', ''[[Claude (language model)|Claude Opus]]'', ''[[Llama (language model)|LLaMa-2]]'' or ''[[Mistral AI|Mistral Large]]'' are working with probabilistic models, which can produce wrong answers in the form of [[Hallucination (artificial intelligence)|hallucinations]]. Therefore, they need not only a large database of mathematical problems to learn from but also methods such as [[Supervised learning|supervised]] [[Fine-tuning (deep learning)|fine-tuning]] or trained [[Statistical classification|classifiers]] with human-annotated data to improve answers for new problems and learn from corrections.<ref>Uesato, J. et al.: [https://openai.com/index/improving-mathematical-reasoning-with-process-supervision/ ''Improving mathematical reasoning with process supervision.''] {{Webarchive|url=https://web.archive.org/web/20240915161758/https://openai.com/index/improving-mathematical-reasoning-with-process-supervision/ |date=15 September 2024 }} openai.com, May 31, 2023. Retrieved 2024-08-07.</ref> A 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low, even for problems with only minor deviations from trained data.<ref>{{Cite arXiv |eprint=2402.19450 |class=cs.AI |first=Saurabh |last=Srivastava |title=Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap |date=2024-02-29}}</ref>

Alternatively, dedicated models for mathematic problem solving with higher precision for the outcome including proof of theorems have been developed such as ''Alpha Tensor'', ''Alpha Geometry'' and ''Alpha Proof'' all from [[Google DeepMind]],<ref>{{Cite web |last=Roberts |first=Siobhan |date=July 25, 2024 |title=AI achieves silver-medal standard solving International Mathematical Olympiad problems |url=https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html |access-date=2024-08-07 |website=[[The New York Times]] |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926131402/https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html |url-status=live }}</ref> ''Llemma'' from eleuther<ref>[https://blog.eleuther.ai/llemma/ ''LLEMMA''.] eleuther.ai. Retrieved 2024-08-07.</ref> or ''Julius''.<ref>[https://julius.ai/home/ai-math AI Math.] {{Webarchive|url=https://web.archive.org/web/20241005165649/https://julius.ai/home/ai-math |date=5 October 2024 }} Caesars Labs, 2024. Retrieved 2024-08-07.</ref>

When natural language is used to describe mathematical problems, converters transform such prompts into a formal language such as [[Lean (proof assistant)|Lean]] to define mathematic tasks.

Some models have been developed to solve challenging problems and reach good results in benchmark tests, others to serve as educational tools in mathematics.<ref>Alex McFarland: [https://www.unite.ai/best-ai-for-math-tools/ ''7 Best AI for Math Tools.''] {{Webarchive|url=https://web.archive.org/web/20240911125615/https://www.unite.ai/best-ai-for-math-tools/ |date=11 September 2024 }} unite.ai. Retrieved 2024-08-07</ref>

=== Finance ===
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.<ref>Matthew Finio & Amanda Downie: IBM Think 2024 Primer, "What is Artificial Intelligence (AI) in Finance?" 8 Dec. 2023</ref>

[[World Pensions & Investments Forum|World Pensions]] experts like Nicolas Firzli insist it may be too early to see the emergence of highly innovative AI-informed financial products and services: "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."<ref>M. Nicolas, J. Firzli: Pensions Age/European Pensions magazine, "Artificial Intelligence: Ask the Industry" May June 2024 https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ {{Webarchive|url=https://web.archive.org/web/20240911125502/https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ |date=11 September 2024 }}.</ref>

=== Military ===
{{Main|Military artificial intelligence}}
Various countries are deploying AI military applications.<ref name="Congressional Research Service-2019">{{Cite book |author=Congressional Research Service |url=https://fas.org/sgp/crs/natsec/R45178.pdf |title=Artificial Intelligence and National Security |date=2019 |publisher=Congressional Research Service |location=Washington, DC |access-date=5 October 2024 |archive-date=8 May 2020 |archive-url=https://web.archive.org/web/20200508062631/https://fas.org/sgp/crs/natsec/R45178.pdf |url-status=live }}[[Template:PD-notice|PD-notice]]</ref> The main applications enhance [[command and control]], communications, sensors, integration and interoperability.<ref name="Slyusar-2019">{{Cite journal |last=Slyusar |first=Vadym |date=2019 |title=Artificial intelligence as the basis of future control networks |url=https://www.researchgate.net/publication/334573170 |doi=10.13140/RG.2.2.30247.50087 |website=ResearchGate |access-date=20 July 2019 |archive-date=28 April 2021 |archive-url=https://web.archive.org/web/20210428013200/https://www.researchgate.net/publication/334573170 |url-status=live }}</ref> Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and [[autonomous vehicles]].<ref name="Congressional Research Service-2019"/> AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, [[target acquisition]], coordination and deconfliction of distributed [[Forward observers in the U.S. military|Joint Fires]] between networked combat vehicles involving manned and unmanned teams.<ref name="Slyusar-2019"/> AI was incorporated into military operations in Iraq and Syria.<ref name="Congressional Research Service-2019"/>

In November 2023,&nbsp;US Vice President [[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.<ref>{{Cite magazine |last=Knight |first=Will |title=The US and 30 Other Nations Agree to Set Guardrails for Military AI |url=https://www.wired.com/story/the-us-and-30-other-nations-agree-to-set-guardrails-for-military-ai |access-date=2024-01-24 |magazine=Wired |issn=1059-1028 |archive-date=20 September 2024 |archive-url=https://web.archive.org/web/20240920061531/https://www.wired.com/story/the-us-and-30-other-nations-agree-to-set-guardrails-for-military-ai/ |url-status=live }}</ref>

=== Generative AI ===
{{Main|Generative artificial intelligence}}
[[File:Vincent van Gogh in watercolour.png|thumb|[[Vincent van Gogh]] in watercolour created by generative AI software]]
In the early 2020s, [[generative AI]] gained widespread prominence. GenAI is AI capable of generating text, images, videos, or other data using [[generative model]]s,<ref>{{Cite web |last1=Newsom, Gavin |last2=Weber, Shirley N. |date=September 6, 2023 |title=Executive Order N-12-23 |url=https://www.gov.ca.gov/wp-content/uploads/2023/09/AI-EO-No.12-_-GGN-Signed.pdf |url-status=live |archive-url=https://web.archive.org/web/20240221222035/https://www.gov.ca.gov/wp-content/uploads/2023/09/AI-EO-No.12-_-GGN-Signed.pdf |archive-date=February 21, 2024 |access-date=September 7, 2023 |publisher=Executive Department, State of California}}</ref><ref>{{Cite arXiv |eprint=2307.15208 |class=eess.IV |first1=Walter H. L. |last1=Pinaya |first2=Mark S. |last2=Graham |title=Generative AI for Medical Imaging: extending the MONAI Framework |date=2023 |last3=Kerfoot |first3=Eric |last4=Tudosiu |first4=Petru-Daniel |last5=Dafflon |first5=Jessica |last6=Fernandez |first6=Virginia |last7=Sanchez |first7=Pedro |last8=Wolleb |first8=Julia |last9=da Costa |first9=Pedro F. |last10=Patel |first10=Ashay}}</ref> often in response to [[Prompt (natural language)|prompts]].<ref>{{Cite web |last1=Griffith |first1=Erin |last2=Metz |first2=Cade |date=2023-01-27 |title=Anthropic Said to Be Closing In on $300 Million in New A.I. Funding |url=https://www.nytimes.com/2023/01/27/technology/anthropic-ai-funding.html |url-status=live |archive-url=https://web.archive.org/web/20231209074235/https://www.nytimes.com/2023/01/27/technology/anthropic-ai-funding.html |archive-date=December 9, 2023 |access-date=2023-03-14 |website=[[The New York Times]]}}</ref><ref>{{Cite news |last1=Lanxon |first1=Nate |last2=Bass |first2=Dina |last3=Davalos |first3=Jackie |date=March 10, 2023 |title=A Cheat Sheet to AI Buzzwords and Their Meanings |url=https://news.bloomberglaw.com/tech-and-telecom-law/a-cheat-sheet-to-ai-buzzwords-and-their-meanings-quicktake |url-status=live |archive-url=https://web.archive.org/web/20231117140835/https://news.bloomberglaw.com/tech-and-telecom-law/a-cheat-sheet-to-ai-buzzwords-and-their-meanings-quicktake |archive-date=November 17, 2023 |access-date=March 14, 2023 |work=Bloomberg News}}</ref>

In March 2023, 58% of U.S. adults had heard about [[ChatGPT]] and 14% had tried it.<ref>{{Cite web |last=Marcelline |first=Marco |date=May 27, 2023 |title=ChatGPT: Most Americans Know About It, But Few Actually Use the AI Chatbot |url=https://www.pcmag.com/news/few-americans-have-actually-tried-chatgpt-despite-most-knowing-about-it |access-date=2024-01-28 |website=PCMag |archive-date=21 May 2024 |archive-url=https://web.archive.org/web/20240521070709/https://www.pcmag.com/news/few-americans-have-actually-tried-chatgpt-despite-most-knowing-about-it |url-status=live }}</ref> The increasing realism and ease-of-use of AI-based [[text-to-image]] generators such as [[Midjourney]], [[DALL-E]], and [[Stable Diffusion]] sparked a trend of [[Viral phenomenon|viral]] AI-generated photos. Widespread attention was gained by a fake photo of [[Pope Francis]] wearing a white puffer coat, the fictional arrest of [[Donald Trump]], and a hoax of an attack on the [[The Pentagon|Pentagon]], as well as the usage in professional creative arts.<ref>{{Cite news |last=Lu |first=Donna |date=2023-03-31 |title=Misinformation, mistakes and the Pope in a puffer: what rapidly evolving AI can – and can't – do |url=https://www.theguardian.com/technology/2023/apr/01/misinformation-mistakes-and-the-pope-in-a-puffer-what-rapidly-evolving-ai-can-and-cant-do |access-date=2024-01-28 |work=The Guardian |issn=0261-3077 |archive-date=10 June 2024 |archive-url=https://web.archive.org/web/20240610171215/https://www.theguardian.com/technology/2023/apr/01/misinformation-mistakes-and-the-pope-in-a-puffer-what-rapidly-evolving-ai-can-and-cant-do |url-status=live }}</ref><ref>{{Cite web |last=Hurst |first=Luke |date=2023-05-23 |title=How a fake image of a Pentagon explosion shared on Twitter caused a real dip on Wall Street |url=https://www.euronews.com/next/2023/05/23/fake-news-about-an-explosion-at-the-pentagon-spreads-on-verified-accounts-on-twitter |access-date=2024-01-28 |website=euronews}}</ref>

===Agents===
Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including [[virtual assistant]]s, [[chatbots]], [[autonomous vehicles]], [[Video game console|game-playing systems]], and [[industrial robotics]]. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations. This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities. In real-world applications, AI agents often face time constraints for decision-making and action execution. Many AI agents incorporate learning algorithms, enabling them to improve their performance over time through experience or training. Using machine learning, AI agents can adapt to new situations and optimise their behaviour for their designated tasks.<ref>{{Cite book |last1=Poole |first1=David |url=https://doi.org/10.1017/9781009258227 |title=Artificial Intelligence, Foundations of Computational Agents |last2=Mackworth |first2=Alan |date=2023 |publisher=Cambridge University Press |isbn=978-1-0092-5819-7 |edition=3rd |doi=10.1017/9781009258227 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://www.cambridge.org/highereducation/books/artificial-intelligence/C113F6CE284AB00F5489EBA5A59B93B7#overview |url-status=live }}</ref><ref>{{Cite book |last1=Russell |first1=Stuart |title=[[Artificial Intelligence: A Modern Approach]] |last2=Norvig |first2=Peter |publisher=Pearson |date=2020 |isbn=978-0-1346-1099-3 |edition=4th}}</ref><ref>{{Cite web |date=2024-07-24 |title=Why agents are the next frontier of generative AI |url=https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai |access-date=2024-08-10 |website=McKinsey Digital |archive-date=3 October 2024 |archive-url=https://web.archive.org/web/20241003212335/https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai |url-status=live }}</ref>

===Other 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.<ref>{{Cite journal |last1=Ransbotham |first1=Sam |last2=Kiron |first2=David |last3=Gerbert |first3=Philipp |last4=Reeves |first4=Martin |date=2017-09-06 |title=Reshaping Business With Artificial Intelligence |url=https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence |url-status=live |journal=MIT Sloan Management Review |archive-url=https://web.archive.org/web/20240213070751/https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence |archive-date=Feb 13, 2024}}</ref> A few examples are [[energy storage]], medical diagnosis, military logistics, applications that predict the result of judicial decisions, [[foreign policy]], or supply chain management.

AI applications for evacuation and [[disaster]] management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions.<ref>{{Citation |last1=Sun |first1=Yuran |title=8 – AI for large-scale evacuation modeling: promises and challenges |date=2024-01-01 |work=Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure |pages=185–204 |editor-last=Naser |editor-first=M. Z. |url=https://www.sciencedirect.com/science/article/pii/B9780128240731000149 |access-date=2024-06-28 |series=Woodhead Publishing Series in Civil and Structural Engineering |publisher=Woodhead Publishing |isbn=978-0-1282-4073-1 |last2=Zhao |first2=Xilei |last3=Lovreglio |first3=Ruggiero |last4=Kuligowski |first4=Erica |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121547/https://www.sciencedirect.com/science/article/abs/pii/B9780128240731000149 |url-status=live }}.</ref><ref>{{Cite journal |last1=Gomaa |first1=Islam |last2=Adelzadeh |first2=Masoud |last3=Gwynne |first3=Steven |last4=Spencer |first4=Bruce |last5=Ko |first5=Yoon |last6=Bénichou |first6=Noureddine |last7=Ma |first7=Chunyun |last8=Elsagan |first8=Nour |last9=Duong |first9=Dana |last10=Zalok |first10=Ehab |last11=Kinateder |first11=Max |date=2021-11-01 |title=A Framework for Intelligent Fire Detection and Evacuation System |url=https://doi.org/10.1007/s10694-021-01157-3 |journal=Fire Technology |volume=57 |issue=6 |pages=3179–3185 |doi=10.1007/s10694-021-01157-3 |issn=1572-8099 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://link.springer.com/article/10.1007/s10694-021-01157-3 |url-status=live }}</ref><ref>{{Cite journal |last1=Zhao |first1=Xilei |last2=Lovreglio |first2=Ruggiero |last3=Nilsson |first3=Daniel |date=2020-05-01 |title=Modelling and interpreting pre-evacuation decision-making using machine learning |url=https://www.sciencedirect.com/science/article/pii/S0926580519313184 |journal=Automation in Construction |volume=113 |pages=103140 |doi=10.1016/j.autcon.2020.103140 |issn=0926-5805 |access-date=5 October 2024 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121548/https://www.sciencedirect.com/science/article/abs/pii/S0926580519313184 |url-status=live }}</ref>

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, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.

During the [[2024 Indian general election|2024 Indian elections]], US$50 millions was spent on authorized AI-generated content, notably by creating [[Deepfake|deepfakes]] of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages.<ref>{{Cite web |date=2024-06-12 |title=India's latest election embraced AI technology. Here are some ways it was used constructively |url=https://www.pbs.org/newshour/world/indias-latest-election-embraced-ai-technology-here-are-some-ways-it-was-used-constructively |access-date=2024-10-28 |website=PBS News |language=en-us}}</ref> 

==Ethics==
{{Main|Ethics of artificial intelligence}}
AI has potential benefits and potential risks.<ref>{{Cite web |title=Ethics of Artificial Intelligence and Robotics |url=https://plato.stanford.edu/archives/fall2023/entries/ethics-ai/ |website=Stanford Encyclopedia of Philosophy Archive |date=30 April 2020 |last1=Müller |first1=Vincent C. |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://plato.stanford.edu/archives/fall2023/entries/ethics-ai/ |url-status=live }}</ref> AI may be able to advance science and find solutions for serious problems: [[Demis Hassabis]] of [[DeepMind|Deep Mind]] hopes to "solve intelligence, and then use that to solve everything else".{{Sfnp|Simonite|2016}} However, as the use of AI has become widespread, several unintended consequences and risks have been identified.{{Sfnp|Russell|Norvig|2021|p=987}} 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.{{Sfnp|Laskowski|2023}}

=== Risks and harm ===
==== Privacy and copyright ====
{{Further|Information privacy|Artificial intelligence and copyright}}

Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about [[privacy]], [[surveillance]] and [[copyright]].

<!-- PRIVACY PROBLEM -->
AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency.

Sensitive user data collected may include online activity records, geolocation data, video or audio.{{Sfnp|GAO|2022}} For example, in order to build [[speech recognition]] algorithms, [[Amazon (company)|Amazon]] has recorded millions of private conversations and allowed [[temporary worker]]s to listen to and transcribe some of them.{{Sfnp|Valinsky|2019}} Opinions about this widespread surveillance range from those who see it as a [[necessary evil]] to those for whom it is clearly [[unethical]] and a violation of the [[right to privacy]].{{Sfnp|Russell|Norvig|2021|p=991}}

<!-- PRIVACY SOLUTIONS -->
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]], [[de-identification]] and [[differential privacy]].{{Sfnp|Russell|Norvig|2021|pp=991–992}} Since 2016, some privacy experts, such as [[Cynthia Dwork]], have begun to view privacy in terms of [[fairness (machine learning)|fairness]]. [[Brian Christian]] wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."{{Sfnp|Christian|2020|p=63}}

<!-- COPYRIGHT AND GENERATIVE AI -->
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]]". 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".{{Sfnp|Vincent|2022}}<ref>{{Cite web |last=Kopel |first=Matthew |title=Copyright Services: Fair Use |url=https://guides.library.cornell.edu/copyright/fair-use |access-date=2024-04-26 |website=Cornell University Library |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926194057/https://guides.library.cornell.edu/copyright/fair-use |url-status=live }}</ref> Website owners who do not wish to have their content scraped can indicate it in a "[[robots.txt]]" file.<ref>{{Cite magazine |last=Burgess |first=Matt |title=How to Stop Your Data From Being Used to Train AI |url=https://www.wired.com/story/how-to-stop-your-data-from-being-used-to-train-ai |access-date=2024-04-26 |magazine=Wired |issn=1059-1028 |archive-date=3 October 2024 |archive-url=https://web.archive.org/web/20241003180100/https://www.wired.com/story/how-to-stop-your-data-from-being-used-to-train-ai/ |url-status=live }}</ref> In 2023, leading authors (including [[John Grisham]] and [[Jonathan Franzen]]) sued AI companies for using their work to train generative AI.{{Sfnp|Reisner|2023}}{{Sfnp|Alter|Harris|2023}} Another discussed approach is to envision a separate ''[[sui generis]]'' system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.<ref>{{Cite web |title=Getting the Innovation Ecosystem Ready for AI. An IP policy toolkit |url=https://www.wipo.int/edocs/pubdocs/en/wipo-pub-2003-en-getting-the-innovation-ecosystem-ready-for-ai.pdf |website=[[WIPO]]}}</ref>

====Dominance by tech giants====
The commercial AI scene is dominated by [[Big Tech]] companies such as [[Alphabet Inc.]], [[Amazon (company)|Amazon]], [[Apple Inc.]], [[Meta Platforms]], and [[Microsoft]].<ref>{{Cite web |last=Hammond |first=George |date=27 December 2023 |title=Big Tech is spending more than VC firms on AI startups |url=https://arstechnica.com/ai/2023/12/big-tech-is-spending-more-than-vc-firms-on-ai-startups |url-status=live |archive-url=https://web.archive.org/web/20240110195706/https://arstechnica.com/ai/2023/12/big-tech-is-spending-more-than-vc-firms-on-ai-startups |archive-date=Jan 10, 2024 |website=Ars Technica}}</ref><ref>{{Cite web |last=Wong |first=Matteo |date=24 October 2023 |title=The Future of AI Is GOMA |url=https://www.theatlantic.com/technology/archive/2023/10/big-ai-silicon-valley-dominance/675752 |url-access=subscription |url-status=live |archive-url=https://web.archive.org/web/20240105020744/https://www.theatlantic.com/technology/archive/2023/10/big-ai-silicon-valley-dominance/675752 |archive-date=Jan 5, 2024 |website=The Atlantic |ref=none}}</ref><ref>{{Cite news |date=Mar 26, 2023 |title=Big tech and the pursuit of AI dominance |url=https://www.economist.com/business/2023/03/26/big-tech-and-the-pursuit-of-ai-dominance |url-access=subscription |url-status=live |archive-url=https://web.archive.org/web/20231229021351/https://www.economist.com/business/2023/03/26/big-tech-and-the-pursuit-of-ai-dominance |archive-date=Dec 29, 2023 |newspaper=The Economist}}</ref> Some of these players already own the vast majority of existing [[cloud computing|cloud infrastructure]] and [[computing]] power from [[data center]]s, allowing them to entrench further in the marketplace.<ref>{{Cite news |last=Fung |first=Brian |date=19 December 2023 |title=Where the battle to dominate AI may be won |url=https://www.cnn.com/2023/12/19/tech/cloud-competition-and-ai/index.html |url-status=live |archive-url=https://web.archive.org/web/20240113053332/https://www.cnn.com/2023/12/19/tech/cloud-competition-and-ai/index.html |archive-date=Jan 13, 2024 |work=CNN Business}}</ref><ref>{{Cite news |last=Metz |first=Cade |date=5 July 2023 |title=In the Age of A.I., Tech's Little Guys Need Big Friends |url=https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html |work=The New York Times |access-date=5 October 2024 |archive-date=8 July 2024 |archive-url=https://web.archive.org/web/20240708214644/https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html |url-status=live }}</ref>

====Power needs and environmental impacts====
{{See also|Environmental impacts of artificial intelligence}}
In January 2024, the [[International Energy Agency]] (IEA) released ''Electricity 2024, Analysis and Forecast to 2026'', forecasting electric power use.<ref>{{Cite web |date=2024-01-24 |title=Electricity 2024 – Analysis |url=https://www.iea.org/reports/electricity-2024 |access-date=2024-07-13 |website=IEA}}</ref> This is the first IEA report to make projections for data centers and power consumption for artificial intelligence and cryptocurrency. The report states that power demand for these uses might double by 2026, with additional electric power usage equal to electricity used by the whole Japanese nation.<ref>{{Cite web |last=Calvert |first=Brian |date=28 March 2024 |title=AI already uses as much energy as a small country. It's only the beginning. |url=https://www.vox.com/climate/2024/3/28/24111721/ai-uses-a-lot-of-energy-experts-expect-it-to-double-in-just-a-few-years |website=Vox |location=New York, New York |access-date=5 October 2024 |archive-date=3 July 2024 |archive-url=https://web.archive.org/web/20240703080555/https://www.vox.com/climate/2024/3/28/24111721/ai-uses-a-lot-of-energy-experts-expect-it-to-double-in-just-a-few-years |url-status=live }}</ref>

Prodigious power consumption by AI is responsible for the growth of fossil fuels use, and might delay closings of obsolete, carbon-emitting coal energy facilities. There is a feverish rise in the construction of data centers throughout the US, making large technology firms (e.g., Microsoft, Meta, Google, Amazon) into voracious consumers of electric power. Projected electric consumption is so immense that there is concern that it will be fulfilled no matter the source. A ChatGPT search involves the use of 10 times the electrical energy as a Google search. The large firms are in haste to find power sources – from nuclear energy to geothermal to fusion. The tech firms argue that – in the long view – AI will be eventually kinder to the environment, but they need the energy now. AI makes the power grid more efficient and "intelligent", will assist in the growth of nuclear power, and track overall carbon emissions, according to technology firms.<ref>{{Cite news |last1=Halper |first1=Evan |last2=O'Donovan |first2=Caroline |date=21 June 2024 |title=AI is exhausting the power grid. Tech firms are seeking a miracle solution. |url=https://www.washingtonpost.com/business/2024/06/21/artificial-intelligence-nuclear-fusion-climate/?utm_campaign=wp_post_most&utm_medium=email&utm_source=newsletter&wpisrc=nl_most&carta-url=https%3A%2F%2Fs2.washingtonpost.com%2Fcar-ln-tr%2F3e0d678%2F6675a2d2c2c05472dd9ec0f4%2F596c09009bbc0f20865036e7%2F12%2F52%2F6675a2d2c2c05472dd9ec0f4 |newspaper=Washington Post}}</ref>

A 2024 [[Goldman Sachs]] Research Paper, ''AI Data Centers and the Coming US Power Demand Surge'', found "US power demand (is) likely to experience growth not seen in a generation...." and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging growth for the electrical power generation industry by a variety of means.<ref>{{Cite web |last=Davenport |first=Carly |title=AI Data Centers and the Coming YS Power Demand Surge |url=https://www.goldmansachs.com/intelligence/pages/gs-research/generational-growth-ai-data-centers-and-the-coming-us-power-surge/report.pdf |website=Goldman Sachs |access-date=5 October 2024 |archive-date=26 July 2024 |archive-url=https://web.archive.org/web/20240726080428/https://www.goldmansachs.com/intelligence/pages/gs-research/generational-growth-ai-data-centers-and-the-coming-us-power-surge/report.pdf |url-status=dead }}</ref> Data centers' need for more and more electrical power is such that they might max out the electrical grid. The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all.<ref>{{Cite news |last=Ryan |first=Carol |date=12 April 2024 |title=Energy-Guzzling AI Is Also the Future of Energy Savings |url=https://www.wsj.com/business/energy-oil/ai-data-centers-energy-savings-d602296e |work=Wall Street Journal |publisher=Dow Jones}}</ref>

In 2024, the ''Wall Street Journal'' reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers. In March 2024 Amazon purchased a Pennsylvania nuclear-powered data center for $650 Million (US).<ref>{{Cite news |last=Hiller |first=Jennifer |date=1 July 2024 |title=Tech Industry Wants to Lock Up Nuclear Power for AI |url=https://www.wsj.com/business/energy-oil/tech-industry-wants-to-lock-up-nuclear-power-for-ai-6cb75316?mod=djem10point |work=Wall Street Journal |publisher=Dow Jones |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://www.wsj.com/business/energy-oil/tech-industry-wants-to-lock-up-nuclear-power-for-ai-6cb75316?mod=djem10point |url-status=live }}</ref> [[Nvidia]] CEO [[Jen-Hsun Huang]] said nuclear power is a good option for the data centers.<ref>{{Cite news |last1=Kendall |first1=Tyler |date=28 September 2024 |title=Nvidia's Huang Says Nuclear Power an Option to Feed Data Centers |url=https://www.bloomberg.com/news/articles/2024-09-27/nvidia-s-huang-says-nuclear-power-an-option-to-feed-data-centers |newspaper=Bloomberg}}</ref>

In September 2024, [[Microsoft]] announced an agreement with [[Constellation Energy]] to re-open the [[Three Mile Island]] nuclear power plant to provide Microsoft with 100% of all electric power produced by the plant for 20 years. Reopening the plant, which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979, will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US [[Nuclear Regulatory Commission]]. If approved (this will be the first ever US re-commissioning of a nuclear plant), over 835 megawatts of power – enough for 800,000 homes – of energy will be produced. The cost for re-opening and upgrading is estimated at $1.6 billion (US) and is dependent on tax breaks for nuclear power contained in the 2022 US [[Inflation Reduction Act]].<ref>{{Cite news |last=Halper |first=Evan |date=20 September 2024 |title=Microsoft deal would reopen Three Mile Island nuclear plant to power AI |url=https://www.washingtonpost.com/business/2024/09/20/microsoft-three-mile-island-nuclear-constellation |newspaper=Washington Post}}</ref> The US government and the state of Michigan are investing almost $2 billion (US) to reopen the [[Palisades Nuclear Generating Station|Palisades Nuclear]] reactor on Lake Michigan. Closed since 2022, the plant is planned to be reopened in October 2025. The Three Mile Island facility will be renamed the Crane Clean Energy Center after Chris Crane, a nuclear proponent and former CEO of [[Exelon]] who was responsible for Exelon spinoff of Constellation.<ref>{{Cite news |last=Hiller |first=Jennifer |date=20 September 2024 |title=Three Mile Island's Nuclear Plant to Reopen, Help Power Microsoft's AI Centers |url=https://www.wsj.com/business/energy-oil/three-mile-islands-nuclear-plant-to-reopen-help-power-microsofts-ai-centers-aebfb3c8?mod=Searchresults_pos1&page=1 |work=Wall Street Journal |publisher=Dow Jones |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170152/https://www.wsj.com/business/energy-oil/three-mile-islands-nuclear-plant-to-reopen-help-power-microsofts-ai-centers-aebfb3c8?mod=Searchresults_pos1&page=1 |url-status=live }}</ref>

After the last approval in September 2023, [[Taiwan]] suspended the approval of data centers north of [[Taoyuan, Taiwan|Taoyuan]] with a capacity of more than 5 MW in 2024, due to power supply shortages.<ref name="DatacenterDynamics">{{Cite news |author=Niva Yadav |date=19 August 2024 |title=Taiwan to stop large data centers in the North, cites insufficient power |url=https://www.datacenterdynamics.com/en/news/taiwan-to-stop-large-data-centers-in-the-north-cites-insufficient-power/ |publisher=DatacenterDynamics}}</ref> Taiwan aims to [[Nuclear power phase-out|phase out nuclear power]] by 2025.<ref name="DatacenterDynamics" /> On the other hand, [[Singapore]] imposed a ban on the opening of data centers in 2019 due to electric power, but in 2022, lifted this ban.<ref name="DatacenterDynamics" />

Although most nuclear plants in Japan have been shut down after the 2011 [[Fukushima nuclear accident]], according to an October 2024 ''Bloomberg'' article in Japanese, cloud gaming services company Ubitus, in which Nvidia has a stake, is looking for land in Japan near nuclear power plant for a new data center for generative AI.<ref name=bloombergjp>{{Cite news |last1=Mochizuki  |first1=Takashi |last2=Oda |first2=Shoko |date=18 October 2024 |title=エヌビディア出資の日本企業、原発近くでＡＩデータセンター新設検討|url=https://www.bloomberg.co.jp/news/articles/2024-10-18/SLHGKKT0AFB400 |newspaper=Bloomberg |language=Japanese}}</ref> Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient, cheap and stable power for AI.<ref name=bloombergjp />

On 1 November 2024, the [[Federal Energy Regulatory Commission]] (FERC) rejected an application submitted by [[Talen Energy]] for approval to supply some electricity from the nuclear power station [[Susquehanna Steam Electric Station|Susquehanna]] to Amazon's data center.<ref name="Bloomberg20241104">{{Cite news |author=Naureen S Malik and Will Wade |date=5 November 2024 |title=Nuclear-Hungry AI Campuses Need New Plan to Find Power Fast |url=https://www.bloomberg.com/news/articles/2024-11-04/nuclear-hungry-ai-campuses-need-new-strategy-to-find-power-fast |publisher=Bloomberg}}</ref> 
According to the Commission Chairman [[Willie L. Phillips]], it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors.<ref name="Bloomberg20241104" />

==== Misinformation ====
{{See also|YouTube#Moderation and offensive content}}

[[YouTube]], [[Facebook]] and others use [[recommender system]]s to guide users to more content. These AI programs were given the goal of [[mathematical optimization|maximizing]] user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose [[misinformation]], [[conspiracy theories]], and extreme [[partisan (politics)|partisan]] 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]] where they received multiple versions of the same misinformation.{{Sfnp|Nicas|2018}} This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government.<ref>{{Cite web |last1=Rainie |first1=Lee |last2=Keeter |first2=Scott |last3=Perrin |first3=Andrew |date=July 22, 2019 |title=Trust and Distrust in America |url=https://www.pewresearch.org/politics/2019/07/22/trust-and-distrust-in-america |url-status=live |archive-url=https://web.archive.org/web/20240222000601/https://www.pewresearch.org/politics/2019/07/22/trust-and-distrust-in-america |archive-date=Feb 22, 2024 |website=Pew Research Center}}</ref> 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 {{Citation needed|reason=This assertion is not obviously supported by other information on this page|date=June 2024}}.

In 2022, [[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.{{Sfnp|Williams|2023}} AI pioneer [[Geoffrey Hinton]] expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.{{Sfnp|Taylor|Hern|2023}}

====Algorithmic bias and fairness====
{{Main|Algorithmic bias|Fairness (machine learning)}}

Machine learning applications will be [[algorithmic bias|biased]]{{Efn|In statistics, a [[Bias (statistics)|bias]] is a systematic error or deviation from the correct value. But in the context of [[Fairness (machine learning)|fairness]], it refers to a tendency in favor or against a certain group or individual characteristic, usually in a way that is considered unfair or harmful. A statistically unbiased AI system that produces disparate outcomes for different demographic groups may thus be viewed as biased in the ethical sense.<ref name="Samuel-2022"/>}} if they learn from biased data.{{Sfnp|Rose|2023}} The developers may not be aware that the bias exists.{{Sfnp|CNA|2019}} Bias can be introduced by the way [[training data]] is selected and by the way a model is deployed.{{Sfnp|Goffrey|2008|p=17}}{{Sfnp|Rose|2023}} If a biased algorithm is used to make decisions that can seriously [[harm]] people (as it can in [[health equity|medicine]], [[credit rating|finance]], [[recruitment]], [[public housing|housing]] or [[policing]]) then the algorithm may cause [[discrimination]].<ref>{{Harvtxt|Berdahl|Baker|Mann|Osoba|2023}}; {{Harvtxt|Goffrey|2008|p=17}}; {{Harvtxt|Rose|2023}}; {{Harvtxt|Russell|Norvig|2021|p=995}}</ref> The field of [[fairness (machine learning)|fairness]] studies how to prevent harms from algorithmic biases.

On June 28, 2015, [[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,{{Sfnp|Christian|2020|p=25}} a problem called "sample size disparity".{{Sfnp|Russell|Norvig|2021|p=995}} 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.{{Sfnp|Grant|Hill|2023}}

[[COMPAS (software)|COMPAS]] is a commercial program widely used by [[U.S. court]]s to assess the likelihood of a [[defendant]] becoming a [[recidivist]]. In 2016, [[Julia Angwin]] at [[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.{{Sfnp|Larson|Angwin|2016}} In 2017, several researchers{{Efn|Including [[Jon Kleinberg]] ([[Cornell University]]), Sendhil Mullainathan ([[University of Chicago]]), Cynthia Chouldechova ([[Carnegie Mellon]]) and Sam Corbett-Davis ([[Stanford]]){{Sfnp|Christian|2020|p=67–70}}}} 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.<ref>{{Harvtxt|Christian|2020|pp=67–70}}; {{Harvtxt|Russell|Norvig|2021|pp=993–994}}</ref>

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".<ref>{{Harvtxt|Russell|Norvig|2021|p=995}}; {{Harvtxt|Lipartito|2011|p=36}}; {{Harvtxt|Goodman|Flaxman|2017|p=6}}; {{Harvtxt|Christian|2020|pp=39–40, 65}}</ref> Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."<ref>Quoted in {{Harvtxt|Christian|2020|p=65}}.</ref>

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.<ref>{{Harvtxt|Russell|Norvig|2021|p=994}}; {{Harvtxt|Christian|2020|pp=40, 80–81}}</ref> 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 descriptive rather than prescriptive.{{Efn|Moritz Hardt (a director at the [[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."<ref>Quoted in {{Harvtxt|Christian|2020|p=80}}</ref>}}

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.{{Sfnp|Russell|Norvig|2021|p=995}}

There are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is [[Distributive justice|distributive fairness]], which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative [[stereotype]]s or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with [[anti-discrimination law]]s.<ref name="Samuel-2022">{{Cite web |last=Samuel |first=Sigal |date=2022-04-19 |title=Why it's so damn hard to make AI fair and unbiased |url=https://www.vox.com/future-perfect/22916602/ai-bias-fairness-tradeoffs-artificial-intelligence |access-date=2024-07-24 |website=Vox |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170153/https://www.vox.com/future-perfect/22916602/ai-bias-fairness-tradeoffs-artificial-intelligence |url-status=live }}</ref>

At its 2022 [[ACM Conference on Fairness, Accountability, and Transparency|Conference on Fairness, Accountability, and Transparency]] (ACM FAccT 2022), the [[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.{{Dubious|date=July 2024|reason=Depending on what is meant by "free of bias", it may be impossible in practice to demonstrate it. Additionally, the study evaluates the priors (initial assumptions) of the robots, rather than their decision-making in scenarios where there is a correct choice. For example, it may not be sexist to have the prior that most doctors are males (it's actually an accurate statistical prior in the world we currently live in, so the bias may arguably be to not have this prior). If forced to choose which one is the doctor based solely on gender, a rational person seeking to maximize the number of correct answers would choose the man 100% of the time. The real issue arises when such priors lead to significant discrimination.}}{{Sfnp|Dockrill|2022}}

==== Lack of transparency ====
{{See also|Explainable AI|Algorithmic transparency|Right to explanation}}
Many AI systems are so complex that their designers cannot explain how they reach their decisions.{{Sfnp|Sample|2017}} Particularly with [[deep neural networks]], in which there are a large amount of non-[[linear]] relationships between inputs and outputs. But some popular explainability techniques exist.<ref>{{Cite web |date=16 June 2023 |title=Black Box AI |url=https://www.techopedia.com/definition/34940/black-box-ai |access-date=5 October 2024 |archive-date=15 June 2024 |archive-url=https://web.archive.org/web/20240615100800/https://www.techopedia.com/definition/34940/black-box-ai |url-status=live }}</ref>

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]] as "cancerous", because pictures of malignancies typically include a ruler to show the scale.{{Sfnp|Christian|2020|p=110}} 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.{{Sfnp|Christian|2020|pp=88–91}}

People who have been harmed by an algorithm's decision have a right to an explanation.<ref>{{Harvtxt|Christian|2020|p=83}}; {{Harvtxt|Russell|Norvig|2021|p=997}}</ref> 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]] in 2016 included an explicit statement that this right exists.{{Efn|When the law was passed in 2018, it still contained a form of this provision.}} 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.{{Sfnp|Christian|2020|p=91}}

[[DARPA]] established the [[Explainable Artificial Intelligence|XAI]] ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems.{{Sfnp|Christian|2020|p=83}}

Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output.{{Sfnp|Verma|2021}} LIME can locally approximate a model's outputs with a simpler, interpretable model.{{Sfnp|Rothman|2020}} [[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.{{Sfnp|Christian|2020|pp=105–108}} [[Deconvolution]], [[DeepDream]] and other [[generative AI|generative]] methods can allow developers to see what different layers of a deep network for computer vision have learned, and produce output that can suggest what the network is learning.{{Sfnp|Christian|2020|pp=108–112}} For [[generative pre-trained transformer]]s, [[Anthropic]] developed a technique based on [[dictionary learning]] that associates patterns of neuron activations with human-understandable concepts.<ref>{{Cite web |last=Ropek |first=Lucas |date=2024-05-21 |title=New Anthropic Research Sheds Light on AI's 'Black Box' |url=https://gizmodo.com/new-anthropic-research-sheds-light-on-ais-black-box-1851491333 |access-date=2024-05-23 |website=Gizmodo |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170309/https://gizmodo.com/new-anthropic-research-sheds-light-on-ais-black-box-1851491333 |url-status=live }}</ref>

==== Bad actors and weaponized AI ====
{{Main|Lethal autonomous weapon|Artificial intelligence arms race|AI safety}}

Artificial intelligence provides a number of tools that are useful to [[bad actor]]s, such as [[authoritarian|authoritarian governments]], [[terrorist]]s, [[criminals]] or [[rogue states]].

A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision.{{Efn|This is the [[United Nations]]' definition, and includes things like [[land mines]] as well.{{Sfnp|Russell|Norvig|2021|p=989}}}} 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]].{{Sfnp|Russell|Norvig|2021|pp=987–990}} Even when used in conventional warfare, it is unlikely that they will be unable to reliably choose targets and could potentially [[murder|kill an innocent person]].{{Sfnp|Russell|Norvig|2021|pp=987–990}} In 2014, 30 nations (including China) supported a ban on autonomous weapons under the [[United Nations]]' [[Convention on Certain Conventional Weapons]], however the [[United States]] and others disagreed.{{Sfnp|Russell|Norvig|2021|p=988}} By 2015, over fifty countries were reported to be researching battlefield robots.<ref>{{Harvtxt|Robitzski|2018}}; {{Harvtxt|Sainato|2015}}</ref>

AI tools make it easier for [[Authoritarian|authoritarian governments]] to efficiently control their citizens in several ways. [[Facial recognition system|Face]] and [[Speaker recognition|voice recognition]] allow widespread [[surveillance]]. [[Machine learning]], operating this data, can [[classifier (machine learning)|classify]] potential enemies of the state and prevent them from hiding. [[Recommendation systems]] can precisely target [[propaganda]] and [[misinformation]] for maximum effect. [[Deepfakes]] and [[generative AI]] aid in producing misinformation. Advanced AI can make authoritarian [[technocracy|centralized decision making]] more competitive than liberal and decentralized systems such as [[market (economics)|market]]s. It lowers the cost and difficulty of [[digital warfare]] and [[spyware|advanced spyware]].{{Sfnp|Harari|2018}} All these technologies have been available since 2020 or earlier—AI [[facial recognition system]]s are already being used for [[mass surveillance]] in China.<ref>{{Cite news |last1=Buckley |first1=Chris |last2=Mozur |first2=Paul |date=22 May 2019 |title=How China Uses High-Tech Surveillance to Subdue Minorities |url=https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html |work=The New York Times |access-date=2 July 2019 |archive-date=25 November 2019 |archive-url=https://web.archive.org/web/20191125180459/https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html |url-status=live }}</ref><ref>{{Cite web |date=3 May 2019 |title=Security lapse exposed a Chinese smart city surveillance system |url=https://techcrunch.com/2019/05/03/china-smart-city-exposed |url-status=live |archive-url=https://web.archive.org/web/20210307203740/https://consent.yahoo.com/v2/collectConsent?sessionId=3_cc-session_c8562b93-9863-4915-8523-6c7b930a3efc |archive-date=7 March 2021 |access-date=14 September 2020}}</ref>

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.{{Sfnp|Urbina|Lentzos|Invernizzi|Ekins|2022}}

==== Technological unemployment ====
{{Main|Workplace impact of artificial intelligence|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.<ref name="E">E. McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022), [https://academic.oup.com/ilj/article/51/3/511/6321008 51(3) Industrial Law Journal 511–559]. {{Webarchive|url=https://web.archive.org/web/20230527163045/https://academic.oup.com/ilj/article/51/3/511/6321008|date=27 May 2023}}.</ref>

<!-- TOPIC: ESTIMATES OF THE AMOUNT OF UNEMPLOYMENT -->
In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.<ref>{{Harvtxt|Ford|Colvin|2015}};{{Harvtxt|McGaughey|2022}}</ref> A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term [[unemployment]], but they generally agree that it could be a net benefit if [[productivity]] gains are [[Redistribution of income and wealth|redistributed]].{{Sfnp|IGM Chicago|2017}} Risk estimates vary; for example, in the 2010s, Michael Osborne and [[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".{{Efn|See table 4; 9% is both the OECD average and the U.S. average.{{Sfnp|Arntz|Gregory|Zierahn|2016|p=33}}}}<ref>{{Harvtxt|Lohr|2017}}; {{Harvtxt|Frey|Osborne|2017}}; {{Harvtxt|Arntz|Gregory|Zierahn|2016|p=33}}</ref> 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.<ref name="E"/> In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.<ref>{{Cite web |last=Zhou |first=Viola |date=2023-04-11 |title=AI is already taking video game illustrators' jobs in China |url=https://restofworld.org/2023/ai-image-china-video-game-layoffs |access-date=2023-08-17 |website=Rest of World |archive-date=21 February 2024 |archive-url=https://web.archive.org/web/20240221131748/https://restofworld.org/2023/ai-image-china-video-game-layoffs/ |url-status=live }}</ref><ref>{{Cite web |last=Carter |first=Justin |date=2023-04-11 |title=China's game art industry reportedly decimated by growing AI use |url=https://www.gamedeveloper.com/art/china-s-game-art-industry-reportedly-decimated-ai-art-use |access-date=2023-08-17 |website=Game Developer |archive-date=17 August 2023 |archive-url=https://web.archive.org/web/20230817010519/https://www.gamedeveloper.com/art/china-s-game-art-industry-reportedly-decimated-ai-art-use |url-status=live }}</ref>

<!-- TOPIC: WHICH JOBS ARE AT RISK? -->
Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''[[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".{{Sfnp|Morgenstern|2015}} Jobs at extreme risk range from [[paralegal]]s to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.<ref>{{Harvtxt|Mahdawi|2017}}; {{Harvtxt|Thompson|2014}}</ref>

From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by [[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.<ref>{{Cite news |last=Tarnoff |first=Ben |date=4 August 2023 |title=Lessons from Eliza |work=[[The Guardian Weekly]] |pages=34–39}}</ref>

==== Existential risk ====
{{Main|Existential risk from artificial general intelligence}}

It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist [[Stephen Hawking]] stated, "[[Global catastrophic risk|spell the end of the human race]]".{{Sfnp|Cellan-Jones|2014}} 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.{{Efn|Sometimes called a "[[robopocalypse]]"{{Sfn|Russell|Norvig|2021|p=1001}}}} These sci-fi scenarios are misleading in several ways.

First, AI does not require human-like "[[sentience]]" to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher [[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 [[Instrumental convergence#Paperclip maximizer|paperclip factory manager]]).{{Sfnp|Bostrom|2014}} [[Stuart J. Russell|Stuart 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."{{Sfnp|Russell|2019}} In order to be safe for humanity, a [[superintelligence]] would have to be genuinely [[AI alignment|aligned]] with humanity's morality and values so that it is "fundamentally on our side".<ref>{{Harvtxt|Bostrom|2014}}; {{Harvtxt|Müller|Bostrom|2014}}; {{Harvtxt|Bostrom|2015}}.</ref>

Second, [[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]], [[law]], [[government]], [[money]] and the [[economy]] are made of [[language]]; they exist because there are stories that billions of people believe. The current prevalence of [[misinformation]] suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.{{Sfnp|Harari|2023}}

<!-- Warnings of existential risk -->
The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.{{Sfnp|Müller|Bostrom|2014}} Personalities such as [[Stephen Hawking]], [[Bill Gates]], and [[Elon Musk]],<ref>Leaders' concerns about the existential risks of AI around 2015: {{Harvtxt|Rawlinson|2015}}, {{Harvtxt|Holley|2015}}, {{Harvtxt|Gibbs|2014}}, {{Harvtxt|Sainato|2015}}</ref> as well as AI pioneers such as [[Yoshua Bengio]], [[Stuart J. Russell|Stuart Russell]], [[Demis Hassabis]], and [[Sam Altman]], have expressed concerns about existential risk from AI.

In May 2023, [[Geoffrey Hinton]] announced his resignation from Google in order to be able to "freely speak out about the risks of AI" without "considering how this impacts Google."<ref>{{Cite news |date=25 March 2023 |title="Godfather of artificial intelligence" talks impact and potential of new AI |url=https://www.cbsnews.com/video/godfather-of-artificial-intelligence-talks-impact-and-potential-of-new-ai |url-status=live |archive-url=https://web.archive.org/web/20230328225221/https://www.cbsnews.com/video/godfather-of-artificial-intelligence-talks-impact-and-potential-of-new-ai |archive-date=28 March 2023 |access-date=2023-03-28 |work=CBS News}}</ref> He notably mentioned risks of an [[AI takeover]],<ref>{{Cite news |last=Pittis |first=Don |date=May 4, 2023 |title=Canadian artificial intelligence leader Geoffrey Hinton piles on fears of computer takeover |url=https://www.cbc.ca/news/business/ai-doom-column-don-pittis-1.6829302 |work=CBC |access-date=5 October 2024 |archive-date=7 July 2024 |archive-url=https://web.archive.org/web/20240707032135/https://www.cbc.ca/news/business/ai-doom-column-don-pittis-1.6829302 |url-status=live }}</ref> and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI.<ref>{{Cite web |date=2024-06-14 |title='50–50 chance' that AI outsmarts humanity, Geoffrey Hinton says |url=https://www.bnnbloomberg.ca/50-50-chance-that-ai-outsmarts-humanity-geoffrey-hinton-says-1.2085394 |access-date=2024-07-06 |website=Bloomberg BNN}}</ref>

In 2023, many leading AI experts issued [[Statement on AI risk of extinction|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".{{Sfnp|Valance|2023}}

<!-- Arguments against existential risk -->
Other researchers, however, spoke in favor of a less dystopian view. AI pioneer [[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."<ref>{{Cite news |last=Taylor |first=Josh |date=7 May 2023 |title=Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says |url=https://www.theguardian.com/technology/2023/may/07/rise-of-artificial-intelligence-is-inevitable-but-should-not-be-feared-father-of-ai-says |access-date=26 May 2023 |work=The Guardian |archive-date=23 October 2023 |archive-url=https://web.archive.org/web/20231023061228/https://www.theguardian.com/technology/2023/may/07/rise-of-artificial-intelligence-is-inevitable-but-should-not-be-feared-father-of-ai-says |url-status=live }}</ref> 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."<ref>{{Cite news |last=Colton |first=Emma |date=7 May 2023 |title='Father of AI' says tech fears misplaced: 'You cannot stop it' |url=https://www.foxnews.com/tech/father-ai-jurgen-schmidhuber-says-tech-fears-misplaced-cannot-stop |access-date=26 May 2023 |work=Fox News |archive-date=26 May 2023 |archive-url=https://web.archive.org/web/20230526162642/https://www.foxnews.com/tech/father-ai-jurgen-schmidhuber-says-tech-fears-misplaced-cannot-stop |url-status=live }}</ref><ref>{{Cite news |last=Jones |first=Hessie |date=23 May 2023 |title=Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life's Work Won't Lead To Dystopia |url=https://www.forbes.com/sites/hessiejones/2023/05/23/juergen-schmidhuber-renowned-father-of-modern-ai-says-his-lifes-work-wont-lead-to-dystopia |access-date=26 May 2023 |work=Forbes |archive-date=26 May 2023 |archive-url=https://web.archive.org/web/20230526163102/https://www.forbes.com/sites/hessiejones/2023/05/23/juergen-schmidhuber-renowned-father-of-modern-ai-says-his-lifes-work-wont-lead-to-dystopia/ |url-status=live }}</ref> [[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."<ref>{{Cite news |last=McMorrow |first=Ryan |date=19 Dec 2023 |title=Andrew Ng: 'Do we think the world is better off with more or less intelligence?' |url=https://www.ft.com/content/2dc07f9e-d2a9-4d98-b746-b051f9352be3 |access-date=30 Dec 2023 |work=Financial Times |archive-date=25 January 2024 |archive-url=https://web.archive.org/web/20240125014121/https://www.ft.com/content/2dc07f9e-d2a9-4d98-b746-b051f9352be3 |url-status=live }}</ref> [[Yann LeCun]] "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."<ref>{{Cite magazine |last=Levy |first=Steven |date=22 Dec 2023 |title=How Not to Be Stupid About AI, With Yann LeCun |url=https://www.wired.com/story/artificial-intelligence-meta-yann-lecun-interview |access-date=30 Dec 2023 |magazine=Wired |archive-date=28 December 2023 |archive-url=https://web.archive.org/web/20231228152443/https://www.wired.com/story/artificial-intelligence-meta-yann-lecun-interview/ |url-status=live }}</ref> 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.<ref>Arguments that AI is not an imminent risk: {{Harvtxt|Brooks|2014}}, {{Harvtxt|Geist|2015}}, {{Harvtxt|Madrigal|2015}}, {{Harvtxt|Lee|2014}}</ref> However, after 2016, the study of current and future risks and possible solutions became a serious area of research.{{Sfnp|Christian|2020|pp=67, 73}}

=== Ethical machines and alignment ===
{{Main|Machine ethics|AI safety|Friendly artificial intelligence|Artificial moral agents|Human Compatible}}

Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. [[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.{{Sfnp|Yudkowsky|2008}}

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.{{Sfnp|Anderson|Anderson|2011}}
The field of machine ethics is also called computational morality,{{Sfnp|Anderson|Anderson|2011}}
and was founded at an [[AAAI]] symposium in 2005.{{Sfnp|AAAI|2014}}

Other approaches include [[Wendell Wallach]]'s "artificial moral agents"{{Sfnp|Wallach|2010}} and [[Stuart J. Russell]]'s [[Human Compatible#Russell's three principles|three principles]] for developing provably beneficial machines.{{Sfnp|Russell|2019|p=173}}

=== Open source ===
Active organizations in the AI open-source community include [[Hugging Face]],<ref>{{Cite web |last1=Stewart |first1=Ashley |last2=Melton |first2=Monica |title=Hugging Face CEO says he's focused on building a 'sustainable model' for the $4.5 billion open-source-AI startup |url=https://www.businessinsider.com/hugging-face-open-source-ai-approach-2023-12 |access-date=2024-04-14 |website=Business Insider |archive-date=25 September 2024 |archive-url=https://web.archive.org/web/20240925013220/https://www.businessinsider.com/hugging-face-open-source-ai-approach-2023-12 |url-status=live }}</ref> [[Google]],<ref>{{Cite web |last=Wiggers |first=Kyle |date=2024-04-09 |title=Google open sources tools to support AI model development |url=https://techcrunch.com/2024/04/09/google-open-sources-tools-to-support-ai-model-development |access-date=2024-04-14 |website=TechCrunch |archive-date=10 September 2024 |archive-url=https://web.archive.org/web/20240910112401/https://techcrunch.com/2024/04/09/google-open-sources-tools-to-support-ai-model-development/ |url-status=live }}</ref> [[EleutherAI]] and [[Meta Platforms|Meta]].<ref>{{Cite web |last=Heaven |first=Will Douglas |date=May 12, 2023 |title=The open-source AI boom is built on Big Tech's handouts. How long will it last? |url=https://www.technologyreview.com/2023/05/12/1072950/open-source-ai-google-openai-eleuther-meta |access-date=2024-04-14 |website=MIT Technology Review}}</ref> Various AI models, such as [[LLaMA|Llama 2]], [[Mistral AI|Mistral]] or [[Stable Diffusion]], have been made open-weight,<ref>{{Cite news |last=Brodsky |first=Sascha |date=December 19, 2023 |title=Mistral AI's New Language Model Aims for Open Source Supremacy |url=https://aibusiness.com/nlp/mistral-ai-s-new-language-model-aims-for-open-source-supremacy |work=AI Business |access-date=5 October 2024 |archive-date=5 September 2024 |archive-url=https://web.archive.org/web/20240905212607/https://aibusiness.com/nlp/mistral-ai-s-new-language-model-aims-for-open-source-supremacy |url-status=live }}</ref><ref>{{Cite web |last=Edwards |first=Benj |date=2024-02-22 |title=Stability announces Stable Diffusion 3, a next-gen AI image generator |url=https://arstechnica.com/information-technology/2024/02/stability-announces-stable-diffusion-3-a-next-gen-ai-image-generator |access-date=2024-04-14 |website=Ars Technica |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170201/https://arstechnica.com/information-technology/2024/02/stability-announces-stable-diffusion-3-a-next-gen-ai-image-generator/ |url-status=live }}</ref> meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely [[Fine-tuning (deep learning)|fine-tuned]], which allows companies to specialize them with their own data and for their own use-case.<ref>{{Cite news |last=Marshall |first=Matt |date=January 29, 2024 |title=How enterprises are using open source LLMs: 16 examples |url=https://venturebeat.com/ai/how-enterprises-are-using-open-source-llms-16-examples |work=VentureBeat |access-date=5 October 2024 |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926171131/https://venturebeat.com/ai/how-enterprises-are-using-open-source-llms-16-examples/ |url-status=live }}</ref> 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]]) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.<ref>{{Cite web |last=Piper |first=Kelsey |date=2024-02-02 |title=Should we make our most powerful AI models open source to all? |url=https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake |access-date=2024-04-14 |website=Vox |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170204/https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake |url-status=live }}</ref>

=== 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]] tests projects in four main areas:<ref>{{Cite web |author=Alan Turing Institute |date=2019 |title=Understanding artificial intelligence ethics and safety |url=https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf |access-date=5 October 2024 |archive-date=11 September 2024 |archive-url=https://web.archive.org/web/20240911131935/https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf |url-status=live }}</ref><ref>{{Cite web |author=Alan Turing Institute |date=2023 |title=AI Ethics and Governance in Practice |url=https://www.turing.ac.uk/sites/default/files/2023-12/aieg-ati-ai-ethics-an-intro_1.pdf |access-date=5 October 2024 |archive-date=11 September 2024 |archive-url=https://web.archive.org/web/20240911125504/https://www.turing.ac.uk/sites/default/files/2023-12/aieg-ati-ai-ethics-an-intro_1.pdf |url-status=live }}</ref>
* '''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 on Beneficial AI|Asilomar Conference]], the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others;<ref>{{Cite journal |last1=Floridi |first1=Luciano |last2=Cowls |first2=Josh |date=2019-06-23 |title=A Unified Framework of Five Principles for AI in Society |url=https://hdsr.mitpress.mit.edu/pub/l0jsh9d1 |journal=Harvard Data Science Review |volume=1 |issue=1 |doi=10.1162/99608f92.8cd550d1 |s2cid=198775713 |doi-access=free}}</ref> however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks.<ref>{{Cite journal |last1=Buruk |first1=Banu |last2=Ekmekci |first2=Perihan Elif |last3=Arda |first3=Berna |date=2020-09-01 |title=A critical perspective on guidelines for responsible and trustworthy artificial intelligence |url=https://doi.org/10.1007/s11019-020-09948-1 |journal=Medicine, Health Care and Philosophy |volume=23 |issue=3 |pages=387–399 |doi=10.1007/s11019-020-09948-1 |issn=1572-8633 |pmid=32236794 |s2cid=214766800 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170206/https://link.springer.com/article/10.1007/s11019-020-09948-1 |url-status=live }}</ref>

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.<ref>{{Cite journal |last1=Kamila |first1=Manoj Kumar |last2=Jasrotia |first2=Sahil Singh |date=2023-01-01 |title=Ethical issues in the development of artificial intelligence: recognizing the risks |url=https://doi.org/10.1108/IJOES-05-2023-0107 |journal=International Journal of Ethics and Systems |volume=ahead-of-print |issue=ahead-of-print |doi=10.1108/IJOES-05-2023-0107 |issn=2514-9369 |s2cid=259614124 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170207/https://www.emerald.com/insight/content/doi/10.1108/IJOES-05-2023-0107/full/html |url-status=live }}</ref>

The [[AI Safety Institute (United Kingdom)|UK AI Safety Institute]] released in 2024 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.<ref>{{Cite web |date=10 May 2024 |title=AI Safety Institute releases new AI safety evaluations platform |url=https://www.gov.uk/government/news/ai-safety-institute-releases-new-ai-safety-evaluations-platform |access-date=14 May 2024 |publisher=UK Government |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170207/https://www.gov.uk/government/news/ai-safety-institute-releases-new-ai-safety-evaluations-platform |url-status=live }}</ref>

=== Regulation ===
{{Main|Regulation of artificial intelligence|Regulation of algorithms|AI safety}}
[[File:Vice President Harris at the group photo of the 2023 AI Safety Summit.jpg|upright=1.2|thumb|alt=AI Safety Summit|The first global [[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 AI; it is therefore related to the broader regulation of algorithms.<ref>Regulation of AI to mitigate risks: {{Harvtxt|Berryhill|Heang|Clogher|McBride|2019}}, {{Harvtxt|Barfield|Pagallo|2018}}, {{Harvtxt|Iphofen|Kritikos|2019}}, {{Harvtxt|Wirtz|Weyerer|Geyer|2018}}, {{Harvtxt|Buiten|2019}}</ref> The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.{{Sfnp|Law Library of Congress (U.S.). Global Legal Research Directorate|2019}} According to AI Index at [[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.{{Sfnp|Vincent|2023}}{{Sfnp|Stanford University|2023}} Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.{{Sfnp|UNESCO|2021}} 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.{{Sfnp|UNESCO|2021}} The [[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.{{Sfnp|UNESCO|2021}} [[Henry Kissinger]], [[Eric Schmidt]], and [[Daniel Huttenlocher]] published a joint statement in November 2021 calling for a government commission to regulate AI.{{Sfnp|Kissinger|2021}} In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.{{Sfnp|Altman|Brockman|Sutskever |2023}} 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.<ref>{{Cite web |last=VOA News |date=October 25, 2023 |title=UN Announces Advisory Body on Artificial Intelligence |url=https://www.voanews.com/a/un-announces-advisory-body-on-artificial-intelligence-/7328732.html |access-date=5 October 2024 |archive-date=18 September 2024 |archive-url=https://web.archive.org/web/20240918071530/https://www.voanews.com/a/un-announces-advisory-body-on-artificial-intelligence-/7328732.html |url-status=live }}</ref> In 2024, the [[Council of Europe]] created the first international legally binding treaty on AI, called the "[[Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law]]". It was adopted by the European Union, the United States, the United Kingdom, and other signatories.<ref>{{Cite web |date=5 September 2024 |title=Council of Europe opens first ever global treaty on AI for signature |url=https://www.coe.int/en/web/portal/-/council-of-europe-opens-first-ever-global-treaty-on-ai-for-signature |access-date=2024-09-17 |website=Council of Europe |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917001330/https://www.coe.int/en/web/portal/-/council-of-europe-opens-first-ever-global-treaty-on-ai-for-signature |url-status=live }}</ref>

In a 2022 [[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".{{Sfnp|Vincent|2023}} A 2023 [[Reuters]]/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.{{Sfnp|Edwards|2023}} In a 2023 [[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".{{Sfnp|Kasperowicz|2023}}{{Sfnp|Fox News|2023}}

In November 2023, the first global [[AI Safety Summit]] was held in [[Bletchley Park]] in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks.<ref>{{Cite news |last=Milmo |first=Dan |date=3 November 2023 |title=Hope or Horror? The great AI debate dividing its pioneers |work=[[The Guardian Weekly]] |pages=10–12}}</ref> 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.<ref>{{Cite web |date=1 November 2023 |title=The Bletchley Declaration by Countries Attending the AI Safety Summit, 1–2 November 2023 |url=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 |archive-url=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 |archive-date=1 November 2023 |access-date=2 November 2023 |website=GOV.UK}}</ref><ref>{{Cite press release |title=Countries agree to safe and responsible development of frontier AI in landmark Bletchley Declaration |url=https://www.gov.uk/government/news/countries-agree-to-safe-and-responsible-development-of-frontier-ai-in-landmark-bletchley-declaration |access-date=1 November 2023 |url-status=live |archive-url=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 |archive-date=1 November 2023 |website=GOV.UK}}</ref> In May 2024 at the [[AI Seoul Summit]], 16 global AI tech companies agreed to safety commitments on the development of AI.<ref>{{Cite web |date=21 May 2024 |title=Second global AI summit secures safety commitments from companies |url=https://www.reuters.com/technology/global-ai-summit-seoul-aims-forge-new-regulatory-agreements-2024-05-21 |access-date=23 May 2024 |publisher=Reuters}}</ref><ref>{{Cite web |date=21 May 2024 |title=Frontier AI Safety Commitments, AI Seoul Summit 2024 |url=https://www.gov.uk/government/publications/frontier-ai-safety-commitments-ai-seoul-summit-2024/frontier-ai-safety-commitments-ai-seoul-summit-2024 |archive-url=https://web.archive.org/web/20240523201611/https://www.gov.uk/government/publications/frontier-ai-safety-commitments-ai-seoul-summit-2024/frontier-ai-safety-commitments-ai-seoul-summit-2024 |archive-date=23 May 2024 |access-date=23 May 2024 |publisher=gov.uk}}</ref>

== History ==
{{Main|History of artificial intelligence}}
{{For timeline}}
<!-- DON'T INCLUDE HISTORICAL PRECURSORS (THEY BELONG TO THE SEPARATE 'HISTORY OF AI' ARTICLE) -->
<!-- MAJOR INTELLECTUAL PRECURSORS: LOGIC, THEORY OF COMPUTATION, CYBERNETICS, INFORMATION THEORY, NEUROBIOLOGY, SPECULATION: Antiquity - 1955 -->
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to [[Alan Turing]]'s [[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.{{Sfn|Russell|Norvig|2021|p=9}}<ref name="Clarendon Press-2004"/> This, along with concurrent discoveries in [[cybernetics]], [[information theory]] and [[neurobiology]], led researchers to consider the possibility of building an "electronic brain".{{Efn|"Electronic brain" was the term used by the press around this time.{{Sfn|Russell|Norvig|2021|p=9}}<ref>{{Cite web |title=Google books ngram |url=https://books.google.com/ngrams/graph?content=electronic+brain&year_start=1930&year_end=2019&corpus=en-2019&smoothing=3 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170209/https://books.google.com/ngrams/graph?content=electronic+brain&year_start=1930&year_end=2019&corpus=en-2019&smoothing=3 |url-status=live }}</ref>}} They developed several areas of research that would become part of AI,<ref>AI's immediate precursors: {{Harvtxt|McCorduck|2004|pp=51–107}}, {{Harvtxt|Crevier|1993|pp=27–32}}, {{Harvtxt|Russell|Norvig|2021|pp=8–17}}, {{Harvtxt|Moravec|1988|p=3}}</ref> such as [[Warren McCullouch|McCullouch]] and [[Walter Pitts|Pitts]] design for "artificial neurons" in 1943,{{Sfnp|Russell|Norvig|2021|p=17}} and Turing's influential 1950 paper '[[Computing Machinery and Intelligence]]', which introduced the [[Turing test]] and showed that "machine intelligence" was plausible.<ref name="Turing"/><ref name="Clarendon Press-2004">{{Cite book |title=The Essential Turing: the ideas that gave birth to the computer age |date=2004 |publisher=Clarendon Press |isbn=0-1982-5079-7 |editor-last=Copeland |editor-first=J. |location=Oxford, England}}</ref>

<!-- 1956-1974 -->
The field of AI research was founded at [[Dartmouth workshop|a workshop]] at [[Dartmouth College]] in 1956.{{Efn|
Daniel Crevier wrote, "the conference is generally recognized as the official birthdate of the new science."{{Sfnp|Crevier|1993|pp=47–49}} [[Stuart J. Russell|Russell]] and [[Norvig]] called the conference "the inception of artificial intelligence."{{Sfnp|Russell|Norvig|2021|p=17}}}}<ref name="Dartmouth workshop">[[Dartmouth workshop]]: {{Harvtxt|Russell|Norvig|2021|p=18}}, {{Harvtxt|McCorduck|2004|pp=111–136}}, {{Harvtxt|NRC|1999|pp=200–201}}<br />The proposal: {{Harvtxt|McCarthy|Minsky|Rochester|Shannon|1955}}</ref> The attendees became the leaders of AI research in the 1960s.{{Efn|
[[Stuart J. Russell|Russell]] and [[Norvig]] wrote "for the next 20 years the field would be dominated by these people and their students."{{Sfnp|Russell|Norvig|2003|p=17}}
}} They and their students produced programs that the press described as "astonishing":{{Efn|
[[Stuart J. Russell|Russell]] and [[Norvig]] wrote "it was astonishing whenever a computer did anything kind of smartish".{{Sfnp|Russell|Norvig|2003|p=18}}
}} computers were learning [[checkers]] strategies, solving word problems in algebra, proving [[Theorem|logical theorems]] and speaking English.{{Efn|
The programs described are [[Arthur Samuel (computer scientist)|Arthur Samuel]]'s checkers program for the [[IBM 701]], [[Daniel Bobrow]]'s [[STUDENT]], [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]]'s [[Logic Theorist]] and [[Terry Winograd]]'s [[SHRDLU]].
}}<ref name="Succ1">Successful programs of the 1960s: {{Harvtxt|McCorduck|2004|pp=243–252}}, {{Harvtxt|Crevier|1993|pp=52–107}}, {{Harvtxt|Moravec|1988|p=9}}, {{Harvtxt|Russell|Norvig|2021|pp=19–21}}</ref> Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s.<ref name="Clarendon Press-2004"/>

<!-- Optimism of the 60s and first AI "winter": 1974 -->
Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with [[artificial general intelligence|general intelligence]] and considered this the goal of their field.{{Sfnp|Newquist|1994|pp=86–86}} In 1965 [[Herbert A. Simon|Herbert Simon]] predicted, "machines will be capable, within twenty years, of doing any work a man can do".<ref>{{Harvtxt|Simon|1965|p=96}} quoted in {{Harvtxt|Crevier|1993|p=109}}</ref> In 1967 [[Marvin Minsky]] agreed, writing that "within a generation&nbsp;... the problem of creating 'artificial intelligence' will substantially be solved".<ref>{{Harvtxt|Minsky|1967|p=2}} quoted in {{Harvtxt|Crevier|1993|p=109}}</ref> They had, however, underestimated the difficulty of the problem.{{Efn|[[Stuart J. Russell|Russell]] and [[Norvig]] write: "in almost all cases, these early systems failed on more difficult problems"{{Sfnp|Russell|Norvig|2021|p=21}}}} In 1974, both the U.S. and British governments cut off exploratory research in response to the [[Lighthill report|criticism]] of [[Sir James Lighthill]]{{Sfnp|Lighthill|1973}} and ongoing pressure from the U.S. Congress to [[Mansfield Amendment|fund more productive projects]].{{Sfn|NRC|1999|pp=212–213}} [[Marvin Minsky|Minsky]]'s and [[Papert]]'s book ''[[Perceptron]]s'' was understood as proving that [[artificial neural networks]] would never be useful for solving real-world tasks, thus discrediting the approach altogether.{{Sfnp|Russell|Norvig|2021|p=22}} The "[[AI winter]]", a period when obtaining funding for AI projects was difficult, followed.<ref name="First AI Winter">First [[AI Winter]], [[Lighthill report]], [[Mansfield Amendment]]: {{Harvtxt|Crevier|1993|pp=115–117}}, {{Harvtxt|Russell|Norvig|2021|pp=21–22}}, {{Harvtxt|NRC|1999|pp=212–213}}, {{Harvtxt|Howe|1994}}, {{Harvtxt|Newquist|1994|pp=189–201}}</ref>

<!-- 1980s and second AI winter -->
In the early 1980s, AI research was revived by the commercial success of [[expert system]]s,<ref>[[Expert systems]]: {{Harvtxt|Russell|Norvig|2021|pp=23, 292}}, {{Harvtxt|Luger|Stubblefield|2004|pp=227–331}}, {{Harvtxt|Nilsson|1998|loc=chpt. 17.4}}, {{Harvtxt|McCorduck|2004|pp=327–335, 434–435}}, {{Harvtxt|Crevier|1993|pp=145–162, 197–203}}, {{Harvtxt|Newquist|1994|pp=155–183}}</ref> 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]] project inspired the U.S. and British governments to restore funding for [[academic research]].<ref name="Fund01">Funding initiatives in the early 1980s: [[Fifth Generation Project]] (Japan), [[Alvey]] (UK), [[Microelectronics and Computer Technology Corporation]] (US), [[Strategic Computing Initiative]] (US): {{Harvtxt|McCorduck|2004|pp=426–441}}, {{Harvtxt|Crevier|1993|pp=161–162, 197–203, 211, 240}}, {{Harvtxt|Russell|Norvig|2021|p=23}}, {{Harvtxt|NRC|1999|pp=210–211}}, {{Harvtxt|Newquist|1994|pp=235–248}}</ref> However, beginning with the collapse of the [[Lisp Machine]] market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.<ref name="Second AI Winter">Second [[AI Winter]]: {{Harvtxt|Russell|Norvig|2021|p=24}}, {{Harvtxt|McCorduck|2004|pp=430–435}}, {{Harvtxt|Crevier|1993|pp=209–210}}, {{Harvtxt|NRC|1999|pp=214–216}}, {{Harvtxt|Newquist|1994|pp=301–318}}</ref>

<!-- Embodied robotics, uncertain reasoning and connectionism in the 1980s -->
Up to this point, most of AI's funding had gone to projects that used high-level [[symbolic AI|symbols]] to represent [[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 [[machine perception|perception]], [[robotics]], [[machine learning|learning]] and [[pattern recognition]],{{Sfnp|Russell|Norvig|2021|p=24}} and began to look into "sub-symbolic" approaches.{{Sfnp|Nilsson|1998|p=7}} [[Rodney Brooks]] rejected "representation" in general and focussed directly on engineering machines that move and survive.{{Efn|
[[embodied mind|Embodied]] approaches to AI{{Sfnp|McCorduck|2004|pp=454–462}} were championed by [[Hans Moravec]]{{Sfnp|Moravec|1988}} and [[Rodney Brooks]]{{Sfnp|Brooks|1990}} and went by many names: [[Nouvelle AI]].{{Sfnp|Brooks|1990}} [[Developmental robotics]].<ref>[[Developmental robotics]]: {{Harvtxt|Weng|McClelland|Pentland|Sporns|2001}}, {{Harvtxt|Lungarella|Metta|Pfeifer|Sandini|2003}}, {{Harvtxt|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}, {{Harvtxt|Oudeyer|2010}}</ref>
}} [[Judea Pearl]], [[Lofti Zadeh]] and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.<ref name="Stoch"/>{{Sfnp|Russell|Norvig|2021|p=25}} But the most important development was the revival of "[[connectionism]]", including neural network research, by [[Geoffrey Hinton]] and others.<ref>{{Harvtxt|Crevier|1993|pp=214–215}}, {{Harvtxt|Russell|Norvig|2021|pp=24, 26}}</ref> In 1990, [[Yann LeCun]] successfully showed that [[convolutional neural networks]] can recognize handwritten digits, the first of many successful applications of neural networks.{{Sfnp|Russell|Norvig|2021|p=26}}

<!-- 1990s: narrow, formal AI and its detractors -->
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 AI|narrow]]" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as [[statistics]], [[economics]] and [[mathematical optimization|mathematics]]).<ref name="Formal and narrow methods adopted in the 1990s">[[#Neat vs. scruffy|Formal]] and [[#Narrow vs. general AI|narrow]] methods adopted in the 1990s: {{Harvtxt |Russell|Norvig|2021|pp=24–26}}, {{Harvtxt|McCorduck|2004|pp=486–487}}</ref> By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as the [[AI effect]]).<ref>AI widely used in the late 1990s: {{Harvtxt|Kurzweil|2005|p=265}}, {{Harvtxt|NRC|1999|pp=216–222}}, {{Harvtxt|Newquist|1994|pp=189–201}}</ref>
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]] (or "AGI"), which had several well-funded institutions by the 2010s.<ref name="Artificial general intelligence"/>

<!--DEEP LEARNING BOOM 2012–present-->
[[Deep learning]] began to dominate industry benchmarks in 2012 and was adopted throughout the field.<ref name="Deep learning revolution">[[Deep learning]] revolution, [[AlexNet]]: {{Harvtxt|Goldman|2022}}, {{Harvtxt|Russell|Norvig|2021|p=26}}, {{Harvtxt|McKinsey|2018}}</ref>
For many specific tasks, other methods were abandoned.{{Efn|Matteo Wong wrote in [[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."{{Sfnp|Wong|2023}}}}
Deep learning's success was based on both hardware improvements ([[Moore's law|faster computers]],<ref>[[Moore's Law]] and AI: {{Harvtxt|Russell|Norvig|2021|pp=14, 27}}</ref> [[graphics processing unit]]s, [[cloud computing]]{{Sfnp|Clark|2015b}}) and access to [[big data|large amounts of data]]<ref>[[Big data]]: {{Harvtxt|Russell|Norvig|2021|p=26}}</ref> (including curated datasets,{{Sfnp|Clark|2015b}} such as [[ImageNet]]). Deep learning's success led to an enormous increase in interest and funding in AI.{{Efn|Jack Clark wrote in [[Bloomberg News|Bloomberg]]: "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]] increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.{{Sfnp|Clark|2015b}}}} The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.{{Sfnp|UNESCO|2021}}

<!-- ALIGNMENT PROBLEM -->
In 2016, issues of [[algorithmic fairness|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 [[AI alignment|alignment problem]] became a serious field of academic study.{{Sfnp|Christian|2020|pp=67, 73}}

<!-- AI Boom 2020-present -->
In the late teens and early 2020s, [[artificial general intelligence|AGI]] companies began to deliver programs that created enormous interest. In 2015, [[AlphaGo]], developed by [[DeepMind]], beat the world champion [[Go player]]. The program was taught only the rules of the game and developed strategy by itself. [[GPT-3]] is a [[large language model]] that was released in 2020 by [[OpenAI]] and is capable of generating high-quality human-like text.<ref>{{Cite web |last=Sagar |first=Ram |date=2020-06-03 |title=OpenAI Releases GPT-3, The Largest Model So Far |url=https://analyticsindiamag.com/open-ai-gpt-3-language-model |url-status=live |archive-url=https://web.archive.org/web/20200804173452/https://analyticsindiamag.com/open-ai-gpt-3-language-model |archive-date=2020-08-04 |access-date=2023-03-15 |website=Analytics India Magazine}}</ref> These programs, and others, inspired an aggressive [[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".{{Sfnp|DiFeliciantonio|2023}} About 800,000 "AI"-related U.S. job openings existed in 2022.{{Sfnp|Goswami|2023}}

== Philosophy ==
{{Main|Philosophy of artificial intelligence}}

Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines.<ref>{{Cite web |last1=Grayling |first1=Anthony |last2=Ball |first2=Brian |date=2024-08-01 |title=Philosophy is crucial in the age of AI |url=https://theconversation.com/philosophy-is-crucial-in-the-age-of-ai-235907 |access-date=2024-10-04 |website=The Conversation |language=en-US |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005170243/https://theconversation.com/philosophy-is-crucial-in-the-age-of-ai-235907 |url-status=live }}</ref> Another major focus has been whether machines can be conscious, and the associated ethical implications.<ref name="Jarow-2024">{{Cite web |last=Jarow |first=Oshan |date=2024-06-15 |title=Will AI ever become conscious? It depends on how you think about biology. |url=https://www.vox.com/future-perfect/351893/consciousness-ai-machines-neuroscience-mind |access-date=2024-10-04 |website=Vox |language=en-US |archive-date=21 September 2024 |archive-url=https://web.archive.org/web/20240921035218/https://www.vox.com/future-perfect/351893/consciousness-ai-machines-neuroscience-mind |url-status=live }}</ref> Many other topics in philosophy are relevant to AI, such as [[epistemology]] and [[free will]].<ref>{{Cite web |last=McCarthy |first=John |title=The Philosophy of AI and the AI of Philosophy |url=http://jmc.stanford.edu/articles/aiphil2.html |archive-url=https://web.archive.org/web/20181023181725/http://jmc.stanford.edu/articles/aiphil2.html |archive-date=2018-10-23 |access-date=2024-10-03 |website=jmc.stanford.edu}}</ref> Rapid advancements have intensified public discussions on the philosophy and ethics of AI.<ref name="Jarow-2024" />

=== Defining artificial intelligence ===
{{See also|Turing test|Intelligent agent|Dartmouth workshop|Synthetic intelligence}}
[[Alan Turing]] wrote in 1950 "I propose to consider the question 'can machines think'?"{{Sfnp|Turing|1950|p=1}} He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".{{Sfnp|Turing|1950|p=1}} He devised the Turing test, which measures the ability of a machine to simulate human conversation.<ref name="Turing">Turing's original publication of the [[Turing test]] in "[[Computing machinery and intelligence]]": {{Harvtxt|Turing|1950}}
Historical influence and philosophical implications: {{Harvtxt|Haugeland|1985|pp=6–9}}, {{Harvtxt|Crevier|1993|p=24}}, {{Harvtxt|McCorduck|2004|pp=70–71}}, {{Harvtxt|Russell|Norvig|2021|pp=2, 984}}</ref> 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 [[Problem of other minds|we can not determine these things about other people]] but "it is usual to have a polite convention that everyone thinks."{{Sfnp|Turing|1950|loc=Under "The Argument from Consciousness"}}
[[File:Weakness of Turing test 1.svg|thumb|The Turing test can provide some evidence of intelligence, but it penalizes non-human intelligent behavior.<ref>{{Cite web |last1=Kirk-Giannini |first1=Cameron Domenico |last2=Goldstein |first2=Simon |date=2023-10-16 |title=AI is closer than ever to passing the Turing test for 'intelligence'. What happens when it does? |url=https://theconversation.com/ai-is-closer-than-ever-to-passing-the-turing-test-for-intelligence-what-happens-when-it-does-214721 |access-date=2024-08-17 |website=The Conversation |archive-date=25 September 2024 |archive-url=https://web.archive.org/web/20240925040612/https://theconversation.com/ai-is-closer-than-ever-to-passing-the-turing-test-for-intelligence-what-happens-when-it-does-214721 |url-status=live }}</ref>]]
[[Stuart J. Russell|Russell]] and [[Norvig]] agree with Turing that intelligence must be defined in terms of external behavior, not internal structure.{{Sfnp|Russell|Norvig|2021|pp=1–4}} However, they are critical that the test requires the machine to imitate humans. "[[Aeronautics|Aeronautical engineering]] texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like [[pigeon]]s that they can fool other pigeons.{{' "}}{{Sfnp|Russell|Norvig|2021|p=3}} AI founder [[John McCarthy (computer scientist)|John McCarthy]] agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".{{Sfnp|Maker|2006}}

McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world".{{Sfnp|McCarthy|1999}} Another AI founder, [[Marvin Minsky]] similarly describes it as "the ability to solve hard problems".{{Sfnp|Minsky|1986}} 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.{{Sfnp|Russell|Norvig|2021|pp=1–4}} 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,<ref>{{Cite web |title=What Is Artificial Intelligence (AI)? |url=https://cloud.google.com/learn/what-is-artificial-intelligence |url-status=live |archive-url=https://web.archive.org/web/20230731114802/https://cloud.google.com/learn/what-is-artificial-intelligence |archive-date=31 July 2023 |access-date=16 October 2023 |website=[[Google Cloud Platform]]}}</ref> 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.

Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with contention as to whether classical algorithms should be categorised as AI,<ref>{{Cite web |title=One of the Biggest Problems in Regulating AI Is Agreeing on a Definition |url=https://carnegieendowment.org/posts/2022/10/one-of-the-biggest-problems-in-regulating-ai-is-agreeing-on-a-definition?lang=en |access-date=2024-07-31 |website=carnegieendowment.org}}</ref> with many companies during the early 2020s AI boom using the term as a marketing [[buzzword]], often even if they did "not actually use AI in a material way".<ref>{{Cite web |title=AI or BS? How to tell if a marketing tool really uses artificial intelligence |url=https://www.thedrum.com/opinion/2023/03/30/ai-or-bs-how-tell-if-marketing-tool-really-uses-artificial-intelligence |access-date=2024-07-31 |website=The Drum}}</ref>

=== Evaluating approaches to AI ===

No established unifying theory or [[paradigm]] has guided AI research for most of its history.{{Efn
|[[Nils Nilsson (researcher)|Nils Nilsson]] wrote in 1983: "Simply put, there is wide disagreement in the field about what AI is all about."{{Sfnp|Nilsson|1983|p=10}}}} 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]], [[soft computing|soft]] and [[artificial general intelligence|narrow]]. Critics argue that these questions may have to be revisited by future generations of AI researchers.

====Symbolic AI and its limits====
[[Symbolic AI]] (or "[[GOFAI]]"){{Sfnp|Haugeland|1985|pp=112–117}} 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]]: "A physical symbol system has the necessary and sufficient means of general intelligent action."<ref>Physical symbol system hypothesis: {{Harvtxt|Newell|Simon|1976|p=116}}
Historical significance: {{Harvtxt|McCorduck|2004|p=153}}, {{Harvtxt|Russell|Norvig|2021|p=19}}</ref>

However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. [[Moravec's paradox]] is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.<ref>[[Moravec's paradox]]: {{Harvtxt|Moravec|1988|pp=15–16}}, {{Harvtxt|Minsky|1986|p=29}}, {{Harvtxt|Pinker|2007|pp=190–191}}</ref> Philosopher [[Hubert Dreyfus]] had [[Dreyfus' critique of AI|argued]] 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.<ref>[[Dreyfus' critique of AI]]: {{Harvtxt|Dreyfus|1972}}, {{Harvtxt|Dreyfus|Dreyfus|1986}}
Historical significance and philosophical implications: {{Harvtxt|Crevier|1993|pp=120–132}}, {{Harvtxt|McCorduck|2004|pp=211–239}}, {{Harvtxt|Russell|Norvig|2021|pp=981–982}}, {{Harvtxt|Fearn|2007|loc=chpt. 3}}</ref> Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.{{Efn|
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."{{Sfnp|Crevier|1993|p=125}}
}}<ref name="Psychological evidence of the prevalence of sub"/>

The issue is not resolved: [[sub-symbolic]] reasoning can make many of the same inscrutable mistakes that human intuition does, such as [[algorithmic bias]]. Critics such as [[Noam Chomsky]] argue continuing research into symbolic AI will still be necessary to attain general intelligence,{{Sfnp|Langley|2011}}{{Sfnp|Katz|2012}} in part because sub-symbolic AI is a move away from [[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 AI|neuro-symbolic artificial intelligence]] attempts to bridge the two approaches.

==== Neat vs. scruffy ====
{{Main|Neats and scruffies}}
"Neats" hope that intelligent behavior is described using simple, elegant principles (such as [[logic]], [[optimization]], or [[Artificial neural network|neural networks]]). "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,<ref>[[Neats vs. scruffies]], the historic debate: {{Harvtxt|McCorduck|2004|pp=421–424, 486–489}}, {{Harvtxt|Crevier|1993|p=168}}, {{Harvtxt|Nilsson|1983|pp=10–11}}, {{Harvtxt|Russell|Norvig|2021|p=24}}
A classic example of the "scruffy" approach to intelligence: {{Harvtxt|Minsky|1986}}
A modern example of neat AI and its aspirations in the 21st century: {{Harvtxt|Domingos|2015}}</ref> but eventually was seen as irrelevant. Modern AI has elements of both.

==== Soft vs. hard computing ====
{{Main|Soft computing}}
Finding a provably correct or optimal solution is [[Intractability (complexity)|intractable]] for many important problems.<ref name="Intractability and efficiency and the combinatorial explosion"/> Soft computing is a set of techniques, including [[genetic algorithms]], [[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 ====
{{Main|Weak artificial intelligence|Artificial general intelligence}}
AI researchers are divided as to whether to pursue the goals of artificial general intelligence and [[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.{{Sfnp|Pennachin|Goertzel|2007}}{{Sfnp|Roberts|2016}} 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 sub-field of artificial general intelligence studies this area exclusively.

=== Machine consciousness, sentience, and mind ===
{{Main|Philosophy of artificial intelligence|Artificial consciousness}}
The [[philosophy of mind]] does not know whether a machine can have a [[mind]], [[consciousness]] and [[philosophy of mind|mental states]], 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. [[Stuart J. Russell|Russell]] and [[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."{{Sfnp|Russell|Norvig|2021|p=986}} 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]].

==== Consciousness ====
{{Main|Hard problem of consciousness|Theory of mind}}
[[David Chalmers]] identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.{{Sfnp|Chalmers|1995}} 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 (psychology)|information processing]] is easy to explain, human [[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''.{{Sfnp|Dennett|1991}}

==== Computationalism and functionalism ====
{{Main|Computational theory of mind|Functionalism (philosophy of mind)|Chinese room}}
Computationalism is the position in the [[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]]. 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]] and [[Hilary Putnam]].{{Sfnp|Horst|2005}}

Philosopher [[John Searle]] characterized this position as "[[Strong AI hypothesis|strong AI]]": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."{{Efn|name="Searle's strong AI"|
Searle presented this definition of "Strong AI" in 1999.{{Sfnp|Searle|1999}} 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."{{Sfnp|Searle|1980|p=1}} Strong AI is defined similarly by [[Stuart J. Russell|Russell]] and [[Norvig]]: "Stong AI – the assertion that machines that do so are ''actually'' thinking (as opposed to ''simulating'' thinking)."{{Sfnp|Russell|Norvig|2021|p=9817}}
}} 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.<ref>Searle's [[Chinese room]] argument: {{Harvtxt|Searle|1980}}. Searle's original presentation of the thought experiment., {{Harvtxt|Searle|1999}}.
Discussion: {{Harvtxt|Russell|Norvig|2021|pp=985}}, {{Harvtxt|McCorduck|2004|pp=443–445}}, {{Harvtxt|Crevier|1993|pp=269–271}}</ref>

==== AI welfare and rights ====
It is difficult or impossible to reliably evaluate whether an advanced [[Sentient AI|AI is sentient]] (has the ability to feel), and if so, to what degree.<ref>{{Cite web |last=Leith |first=Sam |date=2022-07-07 |title=Nick Bostrom: How can we be certain a machine isn't conscious? |url=https://www.spectator.co.uk/article/nick-bostrom-how-can-we-be-certain-a-machine-isnt-conscious |access-date=2024-02-23 |website=The Spectator |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926155639/https://www.spectator.co.uk/article/nick-bostrom-how-can-we-be-certain-a-machine-isnt-conscious/ |url-status=live }}</ref> 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.<ref name="Thomson-2022">{{Cite web |last=Thomson |first=Jonny |date=2022-10-31 |title=Why don't robots have rights? |url=https://bigthink.com/thinking/why-dont-robots-have-rights |access-date=2024-02-23 |website=Big Think |archive-date=13 September 2024 |archive-url=https://web.archive.org/web/20240913055336/https://bigthink.com/thinking/why-dont-robots-have-rights/ |url-status=live }}</ref><ref name="Kateman-2023">{{Cite magazine |last=Kateman |first=Brian |date=2023-07-24 |title=AI Should Be Terrified of Humans |url=https://time.com/6296234/ai-should-be-terrified-of-humans |access-date=2024-02-23 |magazine=Time |archive-date=25 September 2024 |archive-url=https://web.archive.org/web/20240925041601/https://time.com/6296234/ai-should-be-terrified-of-humans/ |url-status=live }}</ref> [[Sapience]] (a set of capacities related to high intelligence, such as discernment or [[self-awareness]]) may provide another moral basis for AI rights.<ref name="Thomson-2022"/> [[Robot rights]] are also sometimes proposed as a practical way to integrate autonomous agents into society.<ref>{{Cite news |last=Wong |first=Jeff |date=July 10, 2023 |title=What leaders need to know about robot rights |url=https://www.fastcompany.com/90920769/what-leaders-need-to-know-about-robot-rights |work=Fast Company |ref=none}}</ref>

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.<ref>{{Cite news |last=Hern |first=Alex |date=2017-01-12 |title=Give robots 'personhood' status, EU committee argues |url=https://www.theguardian.com/technology/2017/jan/12/give-robots-personhood-status-eu-committee-argues |access-date=2024-02-23 |work=The Guardian |issn=0261-3077 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005171222/https://www.theguardian.com/technology/2017/jan/12/give-robots-personhood-status-eu-committee-argues |url-status=live }}</ref> Critics argued in 2018 that granting rights to AI systems would downplay the importance of [[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.<ref>{{Cite web |last=Dovey |first=Dana |date=2018-04-14 |title=Experts Don't Think Robots Should Have Rights |url=https://www.newsweek.com/robots-human-rights-electronic-persons-humans-versus-machines-886075 |access-date=2024-02-23 |website=Newsweek |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005171333/https://www.newsweek.com/robots-human-rights-electronic-persons-humans-versus-machines-886075 |url-status=live }}</ref><ref>{{Cite web |last=Cuddy |first=Alice |date=2018-04-13 |title=Robot rights violate human rights, experts warn EU |url=https://www.euronews.com/2018/04/13/robot-rights-violate-human-rights-experts-warn-eu |access-date=2024-02-23 |website=euronews |archive-date=19 September 2024 |archive-url=https://web.archive.org/web/20240919022327/https://www.euronews.com/2018/04/13/robot-rights-violate-human-rights-experts-warn-eu |url-status=live }}</ref>

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 blindness|moral blind spot]] analogous to [[slavery]] or [[factory farming]], which could lead to [[Suffering risks|large-scale suffering]] if sentient AI is created and carelessly exploited.<ref name="Kateman-2023"/><ref name="Thomson-2022"/>

== Future ==
=== Superintelligence and the singularity ===<!--SUPERINTELLIGENCE-->

A [[superintelligence]] is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.{{Sfnp|Roberts|2016}}<!--SINGULARITY-->If research into [[artificial general intelligence]] produced sufficiently intelligent software, it might be able to [[Recursive self-improvement|reprogram and improve itself]]. The improved software would be even better at improving itself, leading to what [[I. J. Good]] called an "[[intelligence explosion]]" and [[Vernor Vinge]] called a "[[Technological singularity|singularity]]".<ref>The [[Intelligence explosion]] and [[technological singularity]]: {{Harvtxt|Russell|Norvig|2021|pp=1004–1005}}, {{Harvtxt|Omohundro|2008}}, {{Harvtxt|Kurzweil|2005}}

[[I. J. Good]]'s "intelligence explosion": {{Harvtxt|Good|1965}}

[[Vernor Vinge]]'s "singularity": {{Harvtxt|Vinge|1993}}</ref><!--ANTI-SINGULARITIANISM-->

However, technologies cannot improve exponentially indefinitely, and typically follow an [[S-shaped curve]], slowing when they reach the physical limits of what the technology can do.{{Sfnp|Russell|Norvig|2021|p=1005}}

=== Transhumanism ===
{{Main|Transhumanism}}
<!-- TRANSHUMANISM -->
Robot designer [[Hans Moravec]], cyberneticist [[Kevin Warwick]] and inventor [[Ray Kurzweil]] have predicted that humans and machines may merge in the future into [[cyborg]]s that are more capable and powerful than either. This idea, called [[transhumanism]], has roots in the writings of [[Aldous Huxley]] and [[Robert Ettinger]].<ref>[[Transhumanism]]: {{Harvtxt|Moravec|1988}}, {{Harvtxt|Kurzweil|2005}}, {{Harvtxt|Russell|Norvig|2021|p=1005}}</ref>

<!-- EVOLUTION / HUMAN REPLACEMENT -->
[[Edward Fredkin]] argues that "artificial intelligence is the next step in evolution", an idea first proposed by [[Samuel Butler (novelist)|Samuel Butler]]'s "[[Darwin among the Machines]]" as far back as 1863, and expanded upon by [[George Dyson (science historian)|George Dyson]] in his 1998 book ''[[Darwin Among the Machines#Evolution of Global Intelligence|Darwin Among the Machines: The Evolution of Global Intelligence]]''.<ref>AI as evolution: [[Edward Fredkin]] is quoted in {{Harvtxt|McCorduck|2004|p=401}}, {{Harvtxt|Butler|1863}}, {{Harvtxt|Dyson|1998}}</ref>

== In fiction ==
{{Main|Artificial intelligence in fiction}}
[[File:Capek play.jpg|thumb|upright=1.2|The word "robot" itself was coined by [[Karel Čapek]] in his 1921 play ''[[R.U.R.]]'', the title standing for "Rossum's Universal Robots".]]

Thought-capable artificial beings have appeared as storytelling devices since antiquity,<ref name="AI in myth">AI in myth: {{Harvtxt|McCorduck|2004|pp=4–5}}</ref> and have been a persistent theme in [[science fiction]].{{Sfnp|McCorduck|2004|pp=340–400}}

A common [[Trope (literature)|trope]] in these works began with [[Mary Shelley]]'s ''[[Frankenstein]]'', where a human creation becomes a threat to its masters. This includes such works as [[2001: A Space Odyssey (novel)|Arthur C. Clarke's]] and [[2001: A Space Odyssey (film)|Stanley Kubrick's]] ''2001: A Space Odyssey'' (both 1968), with [[HAL 9000]], the murderous computer in charge of the ''[[Discovery One]]'' spaceship, as well as ''[[The Terminator]]'' (1984) and ''[[The Matrix]]'' (1999). In contrast, the rare loyal robots such as Gort from ''[[The Day the Earth Stood Still]]'' (1951) and Bishop from ''[[Aliens (film)|Aliens]]'' (1986) are less prominent in popular culture.{{Sfnp|Buttazzo|2001}}

[[Isaac Asimov]] introduced the [[Three Laws of Robotics]] in many stories, most notably with the "[[Multivac]]" super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics;{{Sfnp|Anderson|2008}} 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.{{Sfnp|McCauley|2007}}

Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have [[sentience|the ability to feel]], and thus to suffer. This appears in [[Karel Čapek]]'s ''[[R.U.R.]]'', the films ''[[A.I. Artificial Intelligence]]'' and ''[[Ex Machina (film)|Ex Machina]]'', as well as the novel ''[[Do Androids Dream of Electric Sheep?]]'', by [[Philip K. Dick]]. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.{{Sfnp|Galvan|1997}}

== See also ==
* [[Artificial general intelligence]]
* {{Annotated link |Artificial intelligence content detection}}
* {{Annotated link |Behavior selection algorithm}}
* {{Annotated link |Business process automation}}
* {{Annotated link |Case-based reasoning}}
* {{Anl|Computational intelligence}}
* {{Anl|Digital immortality}}
* {{Annotated link |Emergent algorithm}}
* {{Annotated link |Female gendering of AI technologies}}
* {{Annotated link |Glossary of artificial intelligence}}
* {{Anl|Intelligence amplification}}
* {{Anl|Mind uploading}}
* {{Anl|Moravec's paradox}}
* [[Organoid intelligence]] – Use of brain cells and brain organoids for intelligent computing
* {{Annotated link |Robotic process automation}}
* {{Annotated link |Weak artificial intelligence}}
* {{Annotated link|Wetware computer}}
* {{Anl|Hallucination (artificial intelligence)}}

== Explanatory notes ==
{{Notelist}}

== References ==
{{Reflist}}

=== AI textbooks ===
The two most widely used textbooks in 2023 (see the [https://explorer.opensyllabus.org/result/field?id=Computer+Science Open Syllabus]):
* {{Cite book |last1=Russell |first1=Stuart J. |author-link=Stuart J. Russell |title=[[Artificial Intelligence: A Modern Approach]] |last2=Norvig |first2=Peter |author-link2=Peter Norvig |publisher=Pearson |date=2021 |isbn=978-0-1346-1099-3 |edition=4th |location=Hoboken |lccn=20190474}}
* {{Cite book |last1=Rich |first1=Elaine |author-link=Elaine Rich |title=Artificial Intelligence |last2=Knight |first2=Kevin |last3=Nair |first3=Shivashankar B |date=2010 |publisher=Tata McGraw Hill India |isbn=978-0-0700-8770-5 |edition=3rd |location=New Delhi |ref=none}}

These were the four of the most widely used AI textbooks in 2008:
{{Refbegin|indent=yes|30em}}
* {{Cite book |last1=Luger |first1=George |author-link=George Luger |url=https://archive.org/details/artificialintell0000luge |title=Artificial Intelligence: Structures and Strategies for Complex Problem Solving |last2=Stubblefield |first2=William |author-link2=William Stubblefield |date=2004 |publisher=Benjamin/Cummings |isbn=978-0-8053-4780-7 |edition=5th |access-date=17 December 2019 |url-access=registration |archive-url=https://web.archive.org/web/20200726220613/https://archive.org/details/artificialintell0000luge |archive-date=26 July 2020 |url-status=live}}
* {{Cite book |last=Nilsson |first=Nils |author-link=Nils Nilsson (researcher) |url=https://archive.org/details/artificialintell0000nils |title=Artificial Intelligence: A New Synthesis |date=1998 |publisher=Morgan Kaufmann |isbn=978-1-5586-0467-4 |access-date=18 November 2019 |url-access=registration |archive-url=https://web.archive.org/web/20200726131654/https://archive.org/details/artificialintell0000nils |archive-date=26 July 2020 |url-status=live}}
* {{Russell Norvig 2003}}.
* {{Cite book |last1=Poole |first1=David |author-link=David Poole (researcher) |url=https://archive.org/details/computationalint00pool |title=Computational Intelligence: A Logical Approach |last2=Mackworth |first2=Alan |author-link2=Alan Mackworth |last3=Goebel |first3=Randy |author-link3=Randy Goebel |date=1998 |publisher=Oxford University Press |isbn=978-0-1951-0270-3 |location=New York |access-date=22 August 2020 |archive-url=https://web.archive.org/web/20200726131436/https://archive.org/details/computationalint00pool |archive-date=26 July 2020 |url-status=live}} Later edition: {{Cite book |last1=Poole |first1=David |url=http://artint.info/index.html |title=Artificial Intelligence: Foundations of Computational Agents |last2=Mackworth |first2=Alan |author-link2=Alan Mackworth |date=2017 |publisher=Cambridge University Press |isbn=978-1-1071-9539-4 |edition=2nd |access-date=6 December 2017 |archive-url=https://web.archive.org/web/20171207013855/http://artint.info/index.html |archive-date=7 December 2017 |url-status=live}}

{{Refend}}

Other textbooks:
* {{Cite book |last=Ertel |first=Wolfgang |title=Introduction to Artificial Intelligence |date=2017 |publisher=Springer |isbn=978-3-3195-8486-7 |edition=2nd |ref=none}}
* {{Cite book |last1=Ciaramella |first1=Alberto |author-link=Alberto Ciaramella |title=Introduction to Artificial Intelligence: from data analysis to generative AI |last2=Ciaramella |first2=Marco |date=2024 |publisher=Intellisemantic Editions |isbn=978-8-8947-8760-3 |edition=1st |ref=none}}

=== History of AI ===
{{Refbegin|indent=yes|30em}}
* {{Crevier 1993}}
* {{McCorduck 2004}}
* {{Cite book |last=Newquist |first=H. P. |author-link=HP Newquist |title=The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think |date=1994 |publisher=Macmillan/SAMS |isbn=978-0-6723-0412-5 |location=New York}}
{{Refend}}

=== Other sources ===
<!-- TO BE ALPHABETIZED -->
{{Refbegin|indent=yes|30em}}
* [https://suli.pppl.gov/2023/course/Rea-PPPL-SULI2023.pdf AI & ML in Fusion]
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* {{Citation |last=Reisner |first=Alex |title=Revealed: The Authors Whose Pirated Books are Powering Generative AI |date=August 19, 2023 |work=The Atlantic |url=https://www.theatlantic.com/technology/archive/2023/08/books3-ai-meta-llama-pirated-books/675063/ |access-date=5 October 2024 |archive-date=3 October 2024 |archive-url=https://web.archive.org/web/20241003071505/https://www.theatlantic.com/technology/archive/2023/08/books3-ai-meta-llama-pirated-books/675063/ |url-status=live }}
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{{Refend}}

== Further reading ==
{{Refbegin|indent=yes|30em}}
* [[David Autor|Autor, David H.]], "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3) ''Journal of Economic Perspectives'' 3.
* {{Cite book |last=Berlinski |first=David |author-link=David Berlinski |url=https://archive.org/details/adventofalgorith0000berl |title=The Advent of the Algorithm |publisher=Harcourt Books |date=2000 |isbn=978-0-1560-1391-8 |oclc=46890682 |access-date=22 August 2020 |archive-url=https://web.archive.org/web/20200726215744/https://archive.org/details/adventofalgorith0000berl |archive-date=26 July 2020 |url-status=live }}
* [[Margaret Boden|Boden, Margaret]], ''Mind As Machine'', [[Oxford University Press]], 2006.
* [[Kenneth Cukier|Cukier, Kenneth]], "Ready for Robots? How to Think about the Future of AI", ''[[Foreign Affairs]]'', vol. 98, no. 4 (July/August 2019), pp.&nbsp;192–198. [[George Dyson (science historian)|George Dyson]], 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.&nbsp;197.) Computer scientist [[Alex Pentland]] writes: "Current [[machine learning|AI machine-learning]] [[algorithm]]s are, at their core, dead simple stupid. They work, but they work by brute force." (p.&nbsp;198.)
* {{Cite journal |last=Evans |first=Woody |author-link=Woody Evans |date=2015 |title=Posthuman Rights: Dimensions of Transhuman Worlds |journal=Teknokultura |volume=12 |issue=2 |doi=10.5209/rev_TK.2015.v12.n2.49072 |doi-access=free|s2cid=147612763 }}
* {{Cite web |last=Frank |first=Michael |date=September 22, 2023 |title=US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order |url=https://thediplomat.com/2023/09/us-leadership-in-artificial-intelligence-can-shape-the-21st-century-global-order |access-date=2023-12-08 |website=[[The Diplomat]] |quote=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. |archive-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916014433/https://thediplomat.com/2023/09/us-leadership-in-artificial-intelligence-can-shape-the-21st-century-global-order/ |url-status=live }}
* 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) [https://www.nytimes.com/2023/07/18/magazine/wikipedia-ai-chatgpt.html online] {{Webarchive|url=https://web.archive.org/web/20230720125400/https://www.nytimes.com/2023/07/18/magazine/wikipedia-ai-chatgpt.html |date=20 July 2023 }}
* [[Gleick, James]], "The Fate of Free Will" (review of Kevin J. Mitchell, ''Free Agents: How Evolution Gave Us Free Will'', Princeton University Press, 2023, 333 pp.), ''[[The New York Review of Books]]'', vol. LXXI, no. 1 (18 January 2024), pp. 27–28, 30. "[[Agency (philosophy)|Agency]] is what distinguishes us from machines. For biological creatures, [[reason]] and [[motivation|purpose]] 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.)
* Halpern, Sue, "The Coming Tech Autocracy" (review of [[Verity Harding]], ''AI Needs You: How We Can Change AI's Future and Save Our Own'', Princeton University Press, 274 pp.; [[Gary Marcus]], ''Taming Silicon Valley: How We Can Ensure That AI Works for Us'', MIT Press, 235 pp.; [[Daniela Rus]] and [[Gregory Mone]], ''The Mind's Mirror: Risk and Reward in the Age of AI'', Norton, 280 pp.; [[Madhumita Murgia]], ''Code Dependent: Living in the Shadow of AI'', Henry Holt, 311 pp.), ''[[The New York Review of Books]]'', vol. LXXI, no. 17 (7 November 2024), pp. 44–46. "'We can't realistically expect that those who hope to get rich from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus]. 'We can't count on [[government]]s driven by [[campaign finance]] contributions [from tech companies] to push back.'... Marcus details the demands that citizens should make of their governments and the [[tech company|tech companies]]. They include [[Transparency (behavior)|transparency]] on how AI systems work; compensation for individuals if their data [are] used to train LLMs ([[large language model]])s and the right to consent to this use; and the ability to hold tech companies liable for the harms they cause by eliminating [[Section 230]], imposing cash penalties, and passing stricter [[product liability]] laws... Marcus also suggests... that a new, AI-specific federal agency, akin to the [[FDA]], the [[FCC]], or the [[Federal Trade Commission|FTC]], might provide the most robust oversight.... [T]he [[Fordham University|Fordham]] law professor [[Chinmayi Sharma]]... suggests... establish[ing] a professional licensing regime for engineers that would function in a similar way to [[medical license]]s, [[malpractice]] suits, and the [[Hippocratic oath]] in medicine. 'What if, like doctors,' she asks..., 'AI engineers also vowed to [[Primum non nocere|do no harm]]?'" (p. 46.) 
* {{Cite news |last=Henderson |first=Mark |date=24 April 2007 |title=Human rights for robots? We're getting carried away |url=http://www.thetimes.co.uk/tto/technology/article1966391.ece |url-status=live |archive-url=https://web.archive.org/web/20140531104850/http://www.thetimes.co.uk/tto/technology/article1966391.ece |archive-date=31 May 2014 |access-date=31 May 2014 |work=The Times Online |location=London }}
* Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle ''[[Cain's Jawbone]]'', which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", ''[[Scientific American]]'', vol. 329, no. 4 (November 2023), pp.&nbsp;81–82. "This murder mystery competition has revealed that although NLP ([[natural-language processing]]) models are capable of incredible feats, their abilities are very much limited by the amount of [[context (linguistics)|context]] they receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyze [[ancient language]]s. In some cases, there are few historical records on long-gone [[civilization]]s to serve as [[training data]] for such a purpose." (p.&nbsp;82.)
* [[Immerwahr, Daniel]], "Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?", ''[[The New Yorker]]'', 20 November 2023, pp.&nbsp;54–59. "If by '[[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 [[cartoon]]s, especially smutty ones." (p.&nbsp;59.)
* Johnston, John (2008) ''The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI'', MIT Press.
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* {{Cite journal |last1=LeCun |first1=Yann |last2=Bengio |first2=Yoshua |last3=Hinton |first3=Geoffrey |date=28 May 2015 |title=Deep learning |url=https://www.nature.com/articles/nature14539 |url-status=live |journal=Nature |volume=521 |issue=7553 |pages=436–444 |bibcode=2015Natur.521..436L |doi=10.1038/nature14539 |pmid=26017442 |s2cid=3074096 |archive-url=https://web.archive.org/web/20230605235832/https://www.nature.com/articles/nature14539 |archive-date=5 June 2023 |access-date=19 June 2023 }}
* Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning models used in scientific research", ''[[Scientific American]]'', vol. 330, no. 6 (June 2024), pp. 80–81.
* [[Jill Lepore|Lepore, Jill]], "The Chit-Chatbot: Is talking with a machine a conversation?", ''[[The New Yorker]]'', 7 October 2024, pp. 12–16.
* {{Cite web |last=Maschafilm |date=2010 |title=Content: Plug & Pray Film – Artificial Intelligence – Robots |url=http://www.plugandpray-film.de/en/content.html |url-status=live |archive-url=https://web.archive.org/web/20160212040134/http://www.plugandpray-film.de/en/content.html |archive-date=12 February 2016 |website=plugandpray-film.de }}
* [[Marcus, Gary]], "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", ''[[Scientific American]]'', vol. 327, no. 4 (October 2022), pp.&nbsp;42–45.
* {{Cite book |last=Mitchell |first=Melanie |title=Artificial intelligence: a guide for thinking humans |date=2019 |publisher=Farrar, Straus and Giroux |isbn=978-0-3742-5783-5 |location=New York}}
* {{Cite journal |last1=Mnih |first1=Volodymyr |last2=Kavukcuoglu |first2=Koray |last3=Silver |first3=David |last4=Rusu |first4=Andrei A. |last5=Veness |first5=Joel |last6=Bellemare |first6=Marc G. |last7=Graves |first7=Alex |last8=Riedmiller |first8=Martin |last9=Fidjeland |first9=Andreas K. |last10=Ostrovski |first10=Georg |last11=Petersen |first11=Stig |last12=Beattie |first12=Charles |last13=Sadik |first13=Amir |last14=Antonoglou |first14=Ioannis |last15=King |first15=Helen |last16=Kumaran |first16=Dharshan |last17=Wierstra |first17=Daan |last18=Legg |first18=Shane |last19=Hassabis |first19=Demis |display-authors=3 |date=26 February 2015 |title=Human-level control through deep reinforcement learning |url=https://www.nature.com/articles/nature14236 |url-status=live |journal=Nature |volume=518 |issue=7540 |pages=529–533 |bibcode=2015Natur.518..529M |doi=10.1038/nature14236 |pmid=25719670 |s2cid=205242740 |archive-url=https://web.archive.org/web/20230619055525/https://www.nature.com/articles/nature14236 |archive-date=19 June 2023 |access-date=19 June 2023 }} Introduced [[Deep Q-learning|DQN]], which produced human-level performance on some Atari games.
* [[Eyal Press|Press, Eyal]], "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", ''[[The New Yorker]]'', 20 November 2023, pp.&nbsp;20–26.
* {{Cite news |date=21 December 2006 |title=Robots could demand legal rights |url=http://news.bbc.co.uk/2/hi/technology/6200005.stm |url-status=live |archive-url=https://web.archive.org/web/20191015042628/http://news.bbc.co.uk/2/hi/technology/6200005.stm |archive-date=15 October 2019 |access-date=3 February 2011 |work=BBC News }}
* Roivainen, Eka, "AI's IQ: [[ChatGPT]] aced a [standard intelligence] test but showed that [[intelligence]] cannot be measured by [[IQ]] alone", ''[[Scientific American]]'', vol. 329, no. 1 (July/August 2023), p.&nbsp;7. "Despite its high IQ, [[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."
* Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", ''[[Foreign Affairs]]'', vol. 98, no. 3 (May/June 2019), pp.&nbsp;135–144. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p.&nbsp;140.)
* {{Cite journal |last1=Schulz |first1=Hannes |last2=Behnke |first2=Sven |date=1 November 2012 |title=Deep Learning |url=https://www.researchgate.net/publication/230690795 |journal=KI – Künstliche Intelligenz |volume=26 |issue=4 |pages=357–363 |doi=10.1007/s13218-012-0198-z |issn=1610-1987 |s2cid=220523562 }}
* {{Cite journal |last1=Serenko |first1=Alexander |last2=Michael Dohan |date=2011 |title=Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence |url=http://www.aserenko.com/papers/JOI_AI_Journal_Ranking_Serenko.pdf |url-status=live |journal=Journal of Informetrics |volume=5 |issue=4 |pages=629–649 |doi=10.1016/j.joi.2011.06.002 |archive-url=https://web.archive.org/web/20131004212839/http://www.aserenko.com/papers/JOI_AI_Journal_Ranking_Serenko.pdf |archive-date=4 October 2013 |access-date=12 September 2013 }}
* {{Cite journal |last1=Silver |first1=David |last2=Huang |first2=Aja |last3=Maddison |first3=Chris J. |last4=Guez |first4=Arthur |last5=Sifre |first5=Laurent |last6=van den Driessche |first6=George |last7=Schrittwieser |first7=Julian |last8=Antonoglou |first8=Ioannis |last9=Panneershelvam |first9=Veda |last10=Lanctot |first10=Marc |last11=Dieleman |first11=Sander |last12=Grewe |first12=Dominik |last13=Nham |first13=John |last14=Kalchbrenner |first14=Nal |last15=Sutskever |first15=Ilya |last16=Lillicrap |first16=Timothy |last17=Leach |first17=Madeleine |last18=Kavukcuoglu |first18=Koray |last19=Graepel |first19=Thore |last20=Hassabis |first20=Demis |display-authors=3 |date=28 January 2016 |title=Mastering the game of Go with deep neural networks and tree search |url=https://www.nature.com/articles/nature16961 |url-status=live |journal=Nature |volume=529 |issue=7587 |pages=484–489 |bibcode=2016Natur.529..484S |doi=10.1038/nature16961 |pmid=26819042 |s2cid=515925 |archive-url=https://web.archive.org/web/20230618213059/https://www.nature.com/articles/nature16961 |archive-date=18 June 2023 |access-date=19 June 2023 }}
* [[Ashish Vaswani|Vaswani, Ashish]], Noam Shazeer, Niki Parmar et al. "[[Attention is all you need]]." Advances in neural information processing systems 30 (2017). Seminal paper on [[transformer (machine learning model)|transformer]]s.
* Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", ''[[London Review of Books]]'', vol. 46, no. 19 (10 October 2024), pp. 29–32. "[AI chatbot] programs are made possible by new technologies but rely on the timelelss human tendency to [[anthropomorphise]]." (p. 29.)
* {{Cite book |url=https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf |title=White Paper: On Artificial Intelligence – A European approach to excellence and trust |publisher=European Commission |date=2020 |location=Brussels |ref={{Harvid|European Commission|2020}} |access-date=20 February 2020 |archive-url=https://web.archive.org/web/20200220173419/https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf |archive-date=20 February 2020 |url-status=live }}
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* {{Cite SEP |url-id=logic-ai |title=Logic and Artificial Intelligence |last=Thomason |first=Richmond}}
* [https://www.bbc.co.uk/programmes/p003k9fc Artificial Intelligence]. BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (''In Our Time'', 8 December 2005).

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