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{{short description|Intelligence of machines or software}}
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<!-- DEFINITIONS -->
'''Artificial intelligence''' ('''AI''') is the intelligence of [[machine]]s or [[software]], as opposed to the intelligence of living beings, primarily [[Human intelligence|of humans]]. It is a [[field of study]] in [[computer science]] that develops and studies intelligent machines. Such machines may be called AIs.

[[Applications of artificial intelligence|AI technology]] is widely used throughout [[Artificial intelligence in industry|industry]], [[Artificial intelligence in government|government]], and science. Some high-profile applications are: advanced [[web search engine]]s (e.g., [[Google Search]]), [[recommender system|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]]), [[self-driving car]]s (e.g., [[Waymo]]), [[Generative artificial intelligence|generative]] and [[Computational creativity|creative]] tools (e.g., [[ChatGPT]] and [[AI art]]), and superhuman play and analysis in [[strategy game]]s (e.g., [[chess]] and [[Go (game)|Go]]).{{sfnp|Google|2016}}

<!-- SUMMARIZING HISTORY -->
[[Alan Turing]] was the first person to conduct substantial research in the field that he called machine intelligence.<ref name="turing"/> Artificial intelligence was founded as an academic discipline in 1956.<ref name="Dartmouth workshop"/> The field went through multiple cycles of optimism,<ref name="AI in the 60s"/><ref name="AI in the 80s"/> 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]] surpassed all previous AI techniques,<ref name="Deep learning revolution"/> and after 2017 with the [[Transformer (machine learning model)|transformer]] architecture.{{sfnp|Toews|2023}} This led to the [[AI spring]] of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant [[advances in artificial intelligence]].{{sfnp|Frank|2023}}

The growing use of artificial intelligence in the 21st century is influencing [[AI era|a societal and economic shift]] towards increased [[automation]], [[data-driven decision-making]], and the [[Artificial intelligence systems integration|integration of AI systems]] into various economic sectors and areas of life, impacting [[Workplace impact of artificial intelligence|job markets]], [[Artificial intelligence in healthcare|healthcare]], government, industry, and [[Artificial intelligence in education|education]]. This raises questions about the [[Ethics of artificial intelligence|ethical implications]] and [[AI risk|risks of AI]], prompting discussions about [[Regulation of artificial intelligence|regulatory policies]] to ensure the [[AI safety|safety and benefits of the technology]].

<!-- SUMMARIZING GOALS AND TOOLS: SCOPE OF AI  -->
The various sub-fields 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]], [[machine perception|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) is among the field's long-term goals.<ref name="AGI"/>

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving 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]], [[neuroscience]] and other fields.<ref name="AI influences">{{Harvtxt|Russell|Norvig|2021|loc=§1.2}}.</ref>

== Goals ==
The general problem of simulating (or creating) intelligence has been broken into sub-problems. 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, 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 became exponentially slower as the problems grew larger.<ref name="Intractability">
[[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 sub-symbolic reasoning">
Psychological evidence of the prevalence 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|databases]]),{{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 name="Representing categories and relations">
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 name="Representing time">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 name="Representing causation">
[[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 name="Representing knowledge about knowledge">
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 and non-monotonic logic">
[[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 sub-symbolic reasoning"/> 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 and scheduling|automated planning]], the agent has a specific goal.<ref>
[[Automated planning and scheduling|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 (economics)|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 [[intractable problem|intractably]] large, so the agents must take actions and evaluate situations while being uncertain 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 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 name ="machine learning">
[[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 theory|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|p=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|p=875–878}} and by 2023 these models were able to get human-level scores on the [[bar exam]], [[scholastic aptitude test|SAT]] test, [[Graduate Record Examinations|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}} and [[robotic sensing|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; 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 = "AGI" >
[[Artificial general intelligence]]:
* {{Harvtxt|Russell|Norvig|2021|pp=32–33, 1020–1021}}
Proposal for the modern version:
* {{Harvtxt|Pennachin|Goertzel|2007}}
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=Chpt. 3–5}}
* {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=113–163}}
* {{Harvtxt|Luger|Stubblefield|2004|pp=79–164, 193–219}}
* {{Harvtxt|Nilsson|1998|loc=chpt. 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 name="State space search">
[[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=§11.2}}

[[Brute force search|Simple exhaustive searches]]<ref name="Uninformed search">[[Uninformed search]]es ([[breadth first search]], [[depth-first search]] and general [[state space search]]):
* {{Harvtxt|Russell|Norvig|2021|loc=§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"/> "[[Heuristics]]" or "rules of thumb" can help to prioritize choices that are more likely to reach a goal.<ref name="Informed search">
[[Heuristic]] or informed searches (e.g., greedy [[Best-first search|best first]] and [[A* search algorithm|A*]]):

* {{Harvtxt|Russell|Norvig|2021|loc=s§3.5}}
* {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=132–147}}
* {{Harvtxt|Poole|Mackworth|2017|loc=§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 name="Local search2">[[Local search (optimization)|Local]] or "[[Mathematical optimization|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 |language=en-US}}</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=§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 (behavior)|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 name="Logic">
[[Logic]]:
* {{Harvtxt|Russell|Norvig|2021|loc=chpt. 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 name="Propositional logic">
[[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 name="Predicate logic">
[[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>

Logical [[inference]] (or [[Deductive reasoning|deduction]]) is the process of [[logical proof|proving]] a new statement ([[Logical consequence|conclusion]]) from other statements that are already known to be true (the [[premise]]s).<ref name="Inference">
[[Logical inference]]:
* {{Harvtxt|Russell|Norvig|2021|loc = chpt. 10}}
</ref>
A logical [[knowledge base]] also handles queries and assertions as a special case of inference.{{sfnp|Russell|Norvig|2021|loc=§8.3.1}}
An [[inference rule]] describes what is a [[validity (logic)|valid]] step in a proof. The most general inference rule is [[resolution (logic)|resolution]].<ref name="Resolution">
[[Resolution (logic)|Resolution]] and [[unification (computer science)|unification]]:
* {{Harvtxt|Russell|Norvig|2021|loc = §7.5.2, §9.2, §9.5}}
</ref>
Inference can be reduced to performing a search to find a path that leads from premises to conclusions, where each step is the application of an [[inference rule]].<ref name="Logic as search">[[Forward chaining]], [[backward chaining]], [[Horn clause]]s, and logical deduction as search:
* {{Harvtxt|Russell|Norvig|2021|loc= §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>
Inference performed this way is [[Intractable problem|intractable]] except for short proofs in restricted domains. No efficient, powerful and general method has been discovered.

[[Fuzzy logic]] assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.<ref name="Fuzzy logic">
Fuzzy logic:
* {{Harvtxt|Russell|Norvig|2021|pp=214, 255, 459}}
* {{Harvtxt|Scientific American|1999}}
</ref>
[[Non-monotonic logic]]s are designed to handle [[default reasoning]].<ref name="Default reasoning and non-monotonic logic"/>
Other specialized versions of logic have been developed to describe many complex domains (see [[#Knowledge representation|knowledge representation]] above).

=== Probabilistic methods for uncertain reasoning ===
[[File:SimpleBayesNet.svg|thumb|380x380px|A simple [[Bayesian network]], with the associated [[Conditional probability table|conditional probability tables]]]]
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="Uncertain reasoning">
Stochastic methods for uncertain reasoning:
* {{Harvtxt|Russell|Norvig|2021|loc=Chpt. 12–18 and 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>

[[Bayesian network]]s<ref name="Bayesian networks">
[[Bayesian network]]s:
* {{Harvtxt|Russell|Norvig|2021|loc=§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. 19.3–4}}
</ref>
are a very general tool that can be used for many problems, including [[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. [[Google AdSense|AdSense]] uses a Bayesian network with over 300&nbsp;million edges to learn which ads to serve.{{sfnp|Domingos|2015|loc=chapter 6}}
}}<ref name="Bayesian inference">
[[Bayesian inference]] algorithm:
* {{Harvtxt|Russell|Norvig|2021|loc=§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 name="Bayesian learning">
[[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 name="Bayesian decision networks">[[Bayesian decision theory]] and Bayesian [[decision network]]s:
* {{Harvtxt|Russell|Norvig|2021|loc=§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, helping [[Machine perception|perception]] systems to 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=§14.3}}
[[Kalman filter]]s:
* {{Harvtxt|Russell|Norvig|2021|loc=§14.4}}
[[Dynamic Bayesian network]]s:
* {{Harvtxt|Russell|Norvig|2021|loc=§14.5}}
</ref>

Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[decision analysis]],<ref name="Decisions theory and analysis">
[[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 name="Information value theory">
[[Information value theory]]:
* {{Harvtxt|Russell|Norvig|2021|loc=§16.6}}
</ref> These tools include models such as [[Markov decision process]]es,<ref name="Markov decision process">[[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 name="Game theory and mechanism design">[[Game theory]] and [[mechanism design]]:
*{{Harvtxt|Russell|Norvig|2021|loc=chpt. 18}}
</ref>[[File:EM_Clustering_of_Old_Faithful_data.gif|thumb|upright=1.2|[[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 name="Statistical classifiers">
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. 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=§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=§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=§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 name="Backpropagation">
Gradient calculation in computational graphs, [[backpropagation]], [[automatic differentiation]]:
* {{Harvtxt|Russell|Norvig|2021|loc=§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=§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=§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=§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 or 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:
[[Alan Turing]] (1948);{{sfnp|Russell|Norvig|2021|p=785}}
[[Frank Rosenblatt]](1957);{{sfnp|Russell|Norvig|2021|p=785}}
[[Karl Steinbuch]] and [[Roger David Joseph]] (1961).{{sfnp|Schmidhuber|2022|loc=§5}}
Deep or recurrent networks that learned (or used gradient descent) were developed by:
[[Ernst Ising]] and [[Wilhelm Lenz]] (1925);{{sfnp|Schmidhuber|2022|loc=§6}}
[[Oliver Selfridge]] (1959);{{sfnp|Schmidhuber|2022|loc=§5}}
[[Alexey Ivakhnenko]] and [[Valentin Lapa]] (1965);{{sfnp|Schmidhuber|2022|loc=§6}}
[[Kaoru Nakano]] (1977);{{sfnp|Schmidhuber|2022|loc=§7}}
[[Shun-Ichi Amari]] (1972);{{sfnp|Schmidhuber|2022|loc=§7}}
[[John Joseph Hopfield]] (1982).{{sfnp|Schmidhuber|2022|loc=§7}}
Backpropagation was independently discovered 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}}
[[Seppo Linnainmaa]] (1970);{{sfnp|Schmidhuber|2022|loc=§8}}
[[Paul Werbos]] (1974).{{sfnp|Russell|Norvig|2021|p=785}}
In fact, backpropagation and gradient descent are straight forward applications of [[Gottfried Leibniz]]' [[chain rule]] in calculus (1676),{{sfnp|Schmidhuber|2022|loc=§2}} and is essentially identical (for one layer) to the [[method of least squares]], developed independently by [[Johann Carl Friedrich Gauss]] (1795) and [[Adrien-Marie Legendre]] (1805).{{sfnp|Schmidhuber|2022|loc=§3}} There are probably many others, yet to be discovered by historians of science.
}}
but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to [[Graphics processing units|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|Generative pre-trained transformers]] (GPT) are [[Large language model|large language models]] that are based on the semantic relationships between words in sentences ([[natural language processing]]). Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next [[Lexical analysis|token]] (a token being usually a word, subword, or punctuation). Throughout this pre-training, GPT models accumulate knowledge about the world, and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful and harmless, usually with a technique called [[reinforcement learning from human feedback]] (RLHF). Current GPT models are still prone to generating falsehoods called "[[Hallucination (artificial intelligence)|hallucinations]]", although this can be reduced with RLHF and quality data. They are used in [[chatbot|chatbots]], which allow you to ask a question or request a task in simple text.{{sfnp|Smith|2023}}<ref>{{Cite web|url=https://news.mit.edu/2023/explained-generative-ai-1109 |date=9 November 2023|title=Explained: Generative AI}}</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|url=https://mitsloanedtech.mit.edu/ai/tools/writing/ |access-date=25 December 2023|title=AI Writing and Content Creation Tools|publisher= MIT Sloan Teaching & Learning Technologies}}</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}}

===Specialized 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}} Historically, specialized languages, such as [[Lisp (programming language)|Lisp]], [[Prolog]], [[Python (programming language)|Python]] and others, had been used.

== 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]], [[Recommender system|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]]), [[Machine translation|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 [[Automatic image annotation|image labeling]] (used by [[Facebook]], [[Apple Computer|Apple]]'s [[iPhoto]] and [[TikTok]]).

===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 |title=The potential for artificial intelligence in healthcare |journal=Future Healthc J. |date=June 2019 |volume=6 |issue=2 |pages=94–98 |doi=10.7861/futurehosp.6-2-94  |pmid= 31363513 |pmc=6616181|bibcode=|language=en |issn=}}</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.

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="The future of personalized cardiova">{{cite journal |last1=Bax|first1=Monique |last2=Thorpe |first2=Jordan|last3=Romanov |first3=Valentin |title=The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence |journal=Frontiers in Sensors |date=December 2023 |volume=4 |issue= |pages= |doi=10.3389/fsens.2023.1294721  |pmid= |pmc=|bibcode=|language=en |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="The future of personalized cardiova"/> New AI tools can deepen our understanding of biomedically relevant pathways. For example, [[AlphaFold 2]] (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein.<ref>{{cite journal |last1=Jumper|first1=J |last2=Evans |first2=R|last3=Pritzel |first3=A |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |date=2021 |volume=596 |issue= 7873|pages=583–589 |doi=10.1038/s41586-021-03819-2  |pmid= 34265844|pmc=8371605|bibcode=2021Natur.596..583J|language=en |issn=}}</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|url=https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/|date=2023-12-20|title=AI discovers new class of antibiotics to kill drug-resistant bacteria }}</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 news |last=Grant |first=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 |work=The New Yorker |language=en-US |issn=0028-792X}}</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 |language=en-US}}</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 |language=en-US |issn=0362-4331}}</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 |language=en}}</ref> Other programs handle [[Imperfect information|imperfect-information]] games, such as the [[poker]]-playing program [[Pluribus (poker bot)|Pluribus]].<ref>{{Cite journal |last=Brown |first=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 |language=en |volume=365 |issue=6456 |pages=885–890 |doi=10.1126/science.aay2400 |issn=0036-8075}}</ref> [[DeepMind]] developed increasingly generalistic [[Reinforcement learning|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 |language=en}}</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 |language=en-GB |issn=0261-3077}}</ref> In 2021 an AI agent competed in a Playstation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.<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 602 |volume=602 |issue=7896 |pages=223–228 |doi=10.1038/s41586-021-04357-7}}</ref>

=== Military ===
{{Main|Military artificial intelligence}}
Various countries are deploying AI military applications.<ref name=":22">{{Cite book |last=Congressional Research Service |url=https://fas.org/sgp/crs/natsec/R45178.pdf |title=Artificial Intelligence and National Security |publisher=Congressional Research Service |year=2019 |location=Washington, DC}}[[Template:PD-notice|PD-notice]]</ref> The main applications enhance [[command and control]], communications, sensors, integration and interoperability.<ref name="AI">{{cite journal |last1=Slyusar |first1=Vadym |year=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}}</ref> Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and [[Vehicular automation|autonomous vehicles]].<ref name=":22" /> 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="AI" /> AI was incorporated into military operations in Iraq and Syria.<ref name=":22" />

In November 2023, US Vice President [[Kamala Harris]] disclosed a declaration signed by 31 nations to set guardrails for the military use of IA. 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 news |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 |work=Wired |language=en-US |issn=1059-1028}}</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. In March 2023, 58% of US 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 |language=en}}</ref> The increasing realism and ease-of-use of AI-based [[Text-to-image model|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 |language=en-GB |issn=0261-3077}}</ref><ref>{{Cite web |date=2023-05-23 |title=How a fake image of a Pentagon explosion shared on Twitter caused a real dip on Wall Street |first1=Luke |last1=Hurst |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 |language=en}}</ref>

===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 they had incorporated "AI" in some offerings or processes.<ref>{{Cite journal |last=Ransbotham |first=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/ |journal=MIT Sloan Management Review |language=en-US |url-status=live |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.

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

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

==Ethics==
AI, like any powerful technology, has potential benefits and potential risks. 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}}

Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that 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 -->
Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio.{{sfnp|GAO|2022}}
For example, in order to build [[speech recognition]] algorithms, [[Amazon (company)|Amazon]] have 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|p=991–992}} Since 2016, some privacy experts, such as [[Cynthia Dwork]], began 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 a rationale of "[[fair use]]". Also website owners who do not wish to have their copyrighted content be AI indexed or 'scraped' can add code to their site, as you would, if you did not want your website to be indexed by a search engine which is currently available to certain services such as [[OpenAI]]. 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}} 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}}

==== 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 theory|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 |date=July 22, 2019 |title=Trust and Distrust in America |first1=Lee |last1=Rainie |first2=Scott |last2=Keeter |first3=Andrew |last3=Perrin |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.

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 biased 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>
[[Fairness (machine learning)|Fairness]] in machine learning is the study of how to prevent the harm caused by algorithmic bias. It has become serious area of academic study within AI. Researchers have discovered it is not always possible to define "fairness" in a way that satisfies all stakeholders.<ref>
[[Algorithmic bias]] and [[Fairness (machine learning)]]:
* {{Harvtxt|Russell|Norvig|2021|loc=section 27.3.3}}
* {{Harvtxt|Christian|2020|loc=Fairness}}
</ref>

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]]), 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 a deeper problem with the misuse of AI. 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. Unfortunately, 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 necessarily descriptive and not proscriptive.{{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}}

At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the [[Association for Computing Machinery]], in Seoul, South Korea, presented and published findings recommending 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.{{sfnp|Dockrill|2022}}

==== Lack of transparency ====
{{See also|Explainable AI|Algorithmic transparency|Right to explanation}}
[[File:HiPhi Z, IAA Summit 2023, Munich (P1120237).jpg|upright=1.2|thumb|[[Lidar]] testing vehicle for autonomous driving]]
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 | url=https://www.techopedia.com/definition/34940/black-box-ai | title=Black Box AI | date=16 June 2023 }}</ref>

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 explanation|right to an explanation]]. Doctors, for example, are required to clearly and completely explain the reasoning behind any decision they make.{{Clarify|date=November 2023|reason=Everywhere in the world? If not so, precise in which countries or according to which law. (also, not sure the reference Christian 2020 p. 83 contains the given information)}}<ref>{{Harvtxt|Christian|2020|p=83}}; {{Harvtxt|Russell|Norvig|2021|p=997}}</ref> 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 and solve these problems.{{sfnp|Christian|2020|p=83}}

There are several potential solutions to the transparency problem. SHAP helps visualise the contribution of each feature to the output.{{sfnp|Verma|2021}} LIME can locally approximate a model 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|p=105-108}} [[Deconvolution]], [[DeepDream]] and other [[generative AI|generative]] methods can allow developers to see what different layers of a deep network have learned and produce output that can suggest what the network is learning.{{sfnp|Christian|2020|pp=108-112}}

==== Conflict, surveillance and weaponized AI ====
{{Main|Lethal autonomous weapon|Artificial intelligence arms race|AI safety}}

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}}}} By 2015, over fifty countries were reported to be researching battlefield robots.<ref>{{harvtxt|Robitzski|2018}}; {{harvtxt|Sainato|2015}}</ref> These weapons are considered especially dangerous for several reasons: if they [[murder|kill an innocent person]] it is not clear who should be held [[accountability|accountable]], it is unlikely they will reliably choose targets, and, if produced at scale, they are potentially [[weapons of mass destruction]].{{sfnp|Russell|Norvig|2021|p=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}}

AI provides a number of tools that are particularly useful for [[authoritarian]] governments: smart [[spyware]], [[Facial recognition system|face recognition]] and [[Speaker recognition|voice recognition]] allow widespread [[surveillance]]; such surveillance allows [[machine learning]] to [[classifier (machine learning)|classify]] potential enemies of the state and can prevent them from hiding; [[recommender system|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 with liberal and decentralized systems such as [[market (economics)|market]]s.{{sfnp|Harari|2018}}

AI [[facial recognition system]]s are used for [[mass surveillance]], notably 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 |work=The New York Times |url=https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html}}</ref><ref>{{Cite web |date=3 May 2019 |title=Security lapse exposed a Chinese smart city surveillance system |url=https://social.techcrunch.com/2019/05/03/china-smart-city-exposed/ |url-status=dead |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> In 2019, [[Bangalore|Bengaluru, India]] deployed AI-managed traffic signals. This system uses cameras to monitor traffic density and adjust signal timing based on the interval needed to clear traffic.<ref>{{Cite web |date=24 September 2019 |title=AI traffic signals to be installed in Bengaluru soon |url=https://nextbigwhat.com/ai-traffic-signals-to-be-installed-in-bengaluru-soon/ |access-date=1 October 2019 |website=NextBigWhat |language=en-US}}</ref> Terrorists, criminals and rogue states can use weaponized AI such as advanced [[digital warfare]] and [[lethal autonomous weapon]]s. Machine-learning AI is also 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}}
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 |pages=34–9 |work=[[The Guardian Weekly]]}}</ref>

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="auto1">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 US 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="auto1"/>

<!-- 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>

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 |language=en-US}}</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 |language=en}}</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 [[ideology|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}}

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]] have expressed concern about existential risk from AI.<ref>
Leaders' concerns about the existential risks of AI around 2015:
*{{harvtxt|Rawlinson|2015}}
*{{Harvtxt|Holley|2015}}
*{{Harvtxt|Gibbs|2014}}
*{{harvtxt|Sainato|2015}}
</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}}

AI pioneers including [[Fei-Fei Li]], [[Geoffrey Hinton]], [[Yoshua Bengio]], [[Cynthia Breazeal]], [[Rana el Kaliouby]], [[Demis Hassabis]], [[Joy Buolamwini]], and [[Sam Altman]] have expressed concerns about the risks of AI. In 2023, many leading AI experts issued the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".{{sfnp|Valance|2023}}

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 name="guardian2023">{{cite news|url=https://www.theguardian.com/technology/2023/may/07/rise-of-artificial-intelligence-is-inevitable-but-should-not-be-feared-father-of-ai-says|title=Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says|last1=Taylor|first1=Josh|date=7 May 2023|work=The Guardian|access-date=26 May 2023|language=en}}</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 name="foxnews2023">{{cite news|url=https://www.foxnews.com/tech/father-ai-jurgen-schmidhuber-says-tech-fears-misplaced-cannot-stop|title='Father of AI' says tech fears misplaced: 'You cannot stop it'|last1=Colton|first1=Emma|date=7 May 2023|work=Fox News|access-date=26 May 2023|language=en}}</ref><ref name="forbes2023">{{cite news|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/|title=Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life's Work Won't Lead To Dystopia|last1=Jones|first1=Hessie|date=23 May 2023|work=Forbes|access-date=26 May 2023|language=en}}</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 name="andrewng2023">{{cite news|url=https://www.ft.com/content/2dc07f9e-d2a9-4d98-b746-b051f9352be3|title=Andrew Ng: 'Do we think the world is better off with more or less intelligence?'|last1=McMorrow|first1=Ryan |date=19 Dec 2023|work=Financial Times|access-date=30 Dec 2023|language=en}}</ref> [[Yann LeCun]] "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."<ref name="lecun2023">{{cite magazine|url=https://www.wired.com/story/artificial-intelligence-meta-yann-lecun-interview/|title=How Not to Be Stupid About AI, With Yann LeCun|last1=Levy|first1=Steven |date=22 Dec 2023|magazine=Wired|access-date=30 Dec 2023|language=en}}</ref>

==== Limiting AI ====
Possible options for limiting AI include: using Embedded Ethics or Constitutional AI where companies or governments can add a policy, restricting high levels of compute power in training, restricting the ability to rewrite its own code base, restrict certain AI techniques but not in the training phase, open-source (transparency) vs proprietary (could be more restricted), backup model with redundancy, restricting security, privacy and copyright, restricting or controlling the memory, real-time monitoring, risk analysis, emergency shut-off, rigorous simulation and testing, model certification, assess known vulnerabilities, restrict the training material, restrict access to the internet, issue terms of use.

=== 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}}

=== 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 |date=2019 |title=Understanding artificial intelligence ethics and safety |author=Alan Turing Institute|url=https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf}}</ref><ref>{{cite web |date=2023 |title=AI Ethics and Governance in Practice|author=Alan Turing Institute|url=https://www.turing.ac.uk/sites/default/files/2023-12/aieg-ati-ai-ethics-an-intro_1.pdf}}</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 |language=en |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 |language=en |volume=23 |issue=3 |pages=387–399 |doi=10.1007/s11019-020-09948-1 |pmid=32236794 |s2cid=214766800 |issn=1572-8633}}</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 |s2cid=259614124 |issn=2514-9369}}</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 artificial intelligence (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, US 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 P. Huttenlocher|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}}</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 [[2023 AI Safety Summit|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 |pages=10–12 |work=[[The Guardian Weekly]]}}</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 name="2023-11-01-bletchley-declaration-full">{{cite web |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 |website=GOV.UK |access-date=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  |date=1 November 2023}}</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 |website=GOV.UK |archive-date=1 November 2023 |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 |url-status=live }}</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, NEUROBIOLOGY: 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 both mathematical deduction and formal reasoning, which is known as the [[Church–Turing thesis]].{{sfnp|Berlinski|2000}} This, along with concurrent discoveries in [[cybernetics]] and [[information theory]], led researchers to consider the possibility of building an "electronic brain".{{efn|"Electronic brain" was the term used by the press around this time.<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}}</ref>}}<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>

Alan Turing was thinking about machine intelligence at least as early as 1941, when he circulated a paper on machine intelligence which could be the earliest paper in the field of AI – though it is now lost.<ref name="turing">{{Cite book |title=The Essential Turing: the ideas that gave birth to the computer age |publisher=Clarendon Press |year=2004 |isbn=0-19-825079-7 |editor-last=Copeland |editor-first=J. |location=Oxford, England |language=en}}</ref> The first available paper generally recognized as "AI" was [[Warren McCullouch|McCullouch]] and [[Walter Pitts|Pitts]] design for [[Turing-complete]] "artificial neurons" in 1943 – the first mathematical model of a neural network.{{sfnp|Russell|Norvig|2021|p=17}} The paper was influenced by Turing's earlier paper '[[Turing's proof|On Computable Numbers]]' from 1936 using similar two-state boolean 'neurons', but was the first to apply it to neuronal function.<ref name="turing" />

The term 'machine intelligence' was used by Alan Turing during his life which was later often referred to as 'artificial intelligence' after his death in 1954. In 1950 Turing published the best known of his papers '[[Computing Machinery and Intelligence]]', the paper introduced his concept of what is now known as the [[Turing test]] to the general public. Then followed three radio broadcasts on AI by Turing, the lectures: 'Intelligent Machinery, A Heretical Theory', 'Can Digital Computers Think'? and the panel discussion 'Can Automatic Calculating Machines be Said to Think'. By 1956 computer intelligence had been actively pursued for more than a decade in Britain; the earliest AI programmes were written there in 1951–1952.<ref name="turing" />

<!-- 1951–1974 -->
In 1951, using a [[Ferranti Mark 1]] computer of the [[University of Manchester]], checkers and chess programs were written where you could play against the computer.<ref>See [http://www.alanturing.net/turing_archive/pages/Reference%20Articles/BriefHistofComp.html "A Brief History of Computing"] at AlanTuring.net.</ref> The field of American 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 [[Peter Norvig|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}}
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 [[Peter Norvig|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 [[Peter Norvig|Norvig]] wrote "it was astonishing whenever a computer did anything kind of smartish".{{sfnp|Russell|Norvig|2003|p=18}}
}} computers were learning [[draughts|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 (computer program)|STUDENT]], [[Allen Newell|Newell]] and [[Herbert A. Simon|Simon]]'s [[Logic Theorist]] and [[Terry Winograd]]'s [[SHRDLU]].
}}<ref name="AI in the 60s">
Successful programs 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 US Universities in the latter 1950s and early 1960s.<ref name="turing" />

<!-- First AI "winter": 1974 -->
They had, however, underestimated the difficulty of the problem.{{efn|[[Stuart J. Russell|Russell]] and [[Peter Norvig|Norvig]] write: "in almost all cases, these early systems failed on more difficult problems"{{sfnp|Russell|Norvig|2021|p=21}}}} 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]]. [[Marvin Minsky|Minsky]]'s and [[Seymour Papert|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 -->
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=145–62, 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="AI in the 80s">
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 and Uncertain reasoning in the 1980s -->
Many researchers began to doubt that the current practices 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}} A number of researchers began to look into "sub-symbolic" approaches.{{sfnp|Nilsson|1998|p=7}} [[Robotics]] researchers, such as [[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 name = "Developmental robotics">
[[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 = "Uncertain reasoning"/>{{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}}

<!-- FORMAL METHODS RISING IN THE 1990s: "Statistical AI"  -->
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="AI 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".<ref name="AI widely used 1990s">
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>

<!-- AGI, 2002-present -->
Several academic researchers became concerned that AI was no longer pursuing the 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="AGI"/>

<!--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 name="Moore's Law">
[[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 name="Big data">
[[Big data]]:
* {{Harvtxt|Russell|Norvig|2021|p=26}}
</ref> (including curated datasets,{{sfnp|Clark|2015b}} such as [[ImageNet]]).

<!-- AI BOOM 2012–present. Four metrics: publications, patents, investment, graduates, jobs. That's enough I think, but more up-to-date numbers on these. -->
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}}
and [[WIPO]] reported that AI was the most prolific [[emerging technologies|emerging technology]] in terms of the number of [[patent]] applications and granted patents.<ref>{{Cite web |title=Intellectual Property and Frontier Technologies |url=https://www.wipo.int/about-ip/en/frontier_technologies/ |website=WIPO |access-date=30 March 2022 |archive-date=2 April 2022 |archive-url=https://web.archive.org/web/20220402064804/https://www.wipo.int/about-ip/en/frontier_technologies/ |url-status=live }}</ref>
According to 'AI Impacts', about $50 billion annually was invested in "AI" around 2022 in the US alone and about 20% of new US Computer Science PhD graduates have specialized in "AI";{{sfnp|DiFeliciantonio|2023}}
about 800,000 "AI"-related US job openings existed in 2022.{{sfnp|Goswami|2023}} The large majority of the advances have occurred within the [[United States]], with its companies, universities, and research labs leading artificial intelligence research.{{sfnp|Frank|2023}}

<!-- ALIGNMENT PROBLEM  -->
In 2016, issues of 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}}

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

=== Defining artificial intelligence ===
{{Main|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 test">
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 and 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 we can not determine these things about other people{{efn|See [[Problem of other minds]]}} but "it is usual to have a polite convention that everyone thinks"{{sfnp|Turing|1950|loc=Under "The Argument from Consciousness"}}

[[Stuart J. Russell|Russell]] and [[Peter Norvig|Norvig]] agree with Turing that AI must be defined in terms of "acting" and not "thinking".{{sfnp|Russell|Norvig|2021|loc=chpt. 2}} However, they are critical that the test compares machines to ''people''. "[[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 defines it as "the ability to solve hard problems".{{sfnp|Minsky|1986}} 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.

=== 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]] (see below). 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 name="Physical symbol system hypothesis">
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=190–91}}
</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 name="Dreyfus' critique">
[[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 sub-symbolic reasoning"/>

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 (mathematics)|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 name="Neats vs. scruffies">
[[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"/> 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|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 ([[Weak artificial intelligence|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 experimental 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 [[Peter Norvig|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). Human [[Information processing (psychology)|information processing]] is easy to explain, however, 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 [[Peter Norvig|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 name="Chinese room">
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 AI is [[Sentience|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 |language=en-US}}</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=":02">{{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 |language=en-US}}</ref><ref name=":12">{{Cite web |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 |website=TIME |language=en}}</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=":02" /> [[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 |language=en-GB |issn=0261-3077}}</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 |language=en}}</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 |language=en}}</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=":12" /><ref name=":02" />

== 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 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 name = "Singularity">
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 ===
<!-- TRANSHUMANISM -->
Robot designer [[Hans Moravec]], cyberneticist [[Kevin Warwick]], and inventor [[Ray Kurzweil]] have predicted that humans and machines will merge in the future into [[cyborg]]s that are more capable and powerful than either. This idea, called transhumanism, has roots in [[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 stage 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 book of the same name in 1998.<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 books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;{{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 ==
* [[AI effect]]
* {{Annotated link |Artificial intelligence detection software}}
* {{Annotated link |Behavior selection algorithm}}
* {{Annotated link |Business process automation}}
* {{Annotated link |Case-based reasoning}}
* {{Annotated link |Emergent algorithm}}
* {{Annotated link |Female gendering of AI technologies}}
* {{Annotated link |Glossary of artificial intelligence}}
* {{Annotated link |Robotic process automation}}
* {{Annotated link |Weak artificial intelligence}}
* {{Annotated link|Wetware computer}}
* {{anl|Intelligence amplification}}

== Explanatory notes ==
{{Notelist}}

== References ==
{{reflist|22em}}

=== 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
| first1 = Stuart J. | last1 = Russell | author1-link = Stuart J. Russell
| first2 = Peter.    | last2 = Norvig  | author2-link = Peter Norvig
| title=[[Artificial Intelligence: A Modern Approach]]
| year = 2021
| edition = 4th
| isbn = 978-0134610993
| lccn = 20190474
| publisher = Pearson | location = Hoboken
}}
* {{Cite book
| first1 = Elaine | last1 = Rich | author1-link = Elaine Rich
| first2 = Kevin  | last2 = Knight
| first3 = Shivashankar B | last3 = Nair
| title = Artificial Intelligence
| date = 2010
| isbn = 978-0070087705
| language = en
| publisher = Tata McGraw Hill India | location = New Delhi
| edition = 3rd
}}

These were the four of the most widely used AI textbooks in 2008:
{{refbegin|30em}}
* {{cite book|last1=Luger|first1=George|author-link=George Luger|last2=Stubblefield|first2=William|author2-link=William Stubblefield|year=2004|title=Artificial Intelligence: Structures and Strategies for Complex Problem Solving|publisher=Benjamin/Cummings|edition=5th|isbn=978-0-8053-4780-7|url=https://archive.org/details/artificialintell0000luge|url-access=registration|access-date=17 December 2019|archive-date=26 July 2020|archive-url=https://web.archive.org/web/20200726220613/https://archive.org/details/artificialintell0000luge|url-status=live}}
* {{cite book|last=Nilsson|first=Nils|author-link=Nils Nilsson (researcher)|year=1998|title=Artificial Intelligence: A New Synthesis|url=https://archive.org/details/artificialintell0000nils|url-access=registration|publisher=Morgan Kaufmann|isbn=978-1-55860-467-4|access-date=18 November 2019|archive-date=26 July 2020|archive-url=https://web.archive.org/web/20200726131654/https://archive.org/details/artificialintell0000nils|url-status=live}}
* {{Russell Norvig 2003}}.
* {{cite book|first1=David|last1=Poole|author-link=David Poole (researcher)|first2=Alan|last2=Mackworth|author2-link=Alan Mackworth|first3=Randy|last3=Goebel|author3-link=Randy Goebel|year=1998|title=Computational Intelligence: A Logical Approach|publisher=Oxford University Press|location=New York|isbn=978-0-19-510270-3|url=https://archive.org/details/computationalint00pool|access-date=22 August 2020|archive-date=26 July 2020|archive-url=https://web.archive.org/web/20200726131436/https://archive.org/details/computationalint00pool|url-status=live}}

Later editions.
* {{cite book|last1=Poole|first1=David|url=http://artint.info/index.html|title=Artificial Intelligence: Foundations of Computational Agents|last2=Mackworth|first2=Alan|publisher=Cambridge University Press|year=2017|isbn=978-1-107-19539-4|edition=2nd|author2-link=Alan Mackworth|access-date=6 December 2017|archive-date=7 December 2017|archive-url=https://web.archive.org/web/20171207013855/http://artint.info/index.html|url-status=live}}

{{refend}}

=== History of AI ===
{{refbegin}}
* {{Crevier 1993}}.
* {{McCorduck 2004}}.
* {{cite book |last=Newquist |first=H. P. |author-link=HP Newquist |year=1994 |title=The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think |publisher=Macmillan/SAMS |location=New York |isbn=978-0-672-30412-5}}
{{refend}}

=== Other sources ===
<!-- TO BE ALPHABETIZED -->
{{Refbegin|30em}}
* [https://suli.pppl.gov/2023/course/Rea-PPPL-SULI2023.pdf AI & ML in Fusion]
* [https://drive.google.com/file/d/1npCTrJ8XJn20ZGDA_DfMpANuQZFMzKPh/view?usp=drive_link AI & ML in Fusion, video lecture] {{Webarchive|url=https://web.archive.org/web/20230702164332/https://drive.google.com/file/d/1npCTrJ8XJn20ZGDA_DfMpANuQZFMzKPh/view?usp=drive_link |date=2 July 2023 }}
* {{cite web
| ref = {{Harvid|Google|2016}}
| title = AlphaGo – Google DeepMind
| url = https://wildoftech.com/alphago-google-deepmind
| url-status = live
| archive-url = https://web.archive.org/web/20160310191926/https://wildoftech.com/alphago-google-deepmind/
| archive-date = 10 March 2016
}}
* {{citation
| last1 = Alter | first1 = Alexandra
| last2 = Harris | first2 = Elizabeth A.
| title = Franzen, Grisham and Other Prominent Authors Sue OpenAI
| date = September 20, 2023
| newspaper = The New York Times
| url = https://www.nytimes.com/2023/09/20/books/authors-openai-lawsuit-chatgpt-copyright.html?campaign_id=2&emc=edit_th_20230921&instance_id=103259&nl=todaysheadlines&regi_id=62816440&segment_id=145288&user_id=ad24f3545dae0ec44284a38bb4a88f1d
}}
* {{Cite web
|title=Governance of Superintelligence
| last1 = Altman | first1 = Sam | author1-link = Sam Altman
| last2 = Brockman | first2 = Greg | author2-link = Greg Brockman
| last3 = Sutskever | first3 = Ilya | author3-link = Ilya Sutskever
|date=22 May 2023
|url=https://openai.com/blog/governance-of-superintelligence
|access-date=27 May 2023
|website=openai.com
|language=en-US
|archive-date=27 May 2023
|archive-url=https://web.archive.org/web/20230527061619/https://openai.com/blog/governance-of-superintelligence
|url-status=live
}}
* {{cite journal
| last = Anderson | first = Susan Leigh
| title = Asimov's "three laws of robotics" and machine metaethics.
| year = 2008
| journal = AI & Society | volume = 22 | issue = 4 | pages = 477–493| doi = 10.1007/s00146-007-0094-5
| s2cid = 1809459
}}
* {{cite book
| first1 = Michael | last1 = Anderson
| first2 = Susan Leigh | last2 = Anderson
| year = 2011
| title = Machine Ethics
| publisher = Cambridge University Press.
}}
* {{citation
| last1 = Arntz | first1 = Melanie
| last2 = Gregory | first2 = Terry
| last3 = Zierahn | first3 = Ulrich
| title = The risk of automation for jobs in OECD countries: A comparative analysis
| year = 2016
| work = OECD Social, Employment, and Migration Working Papers 189
}}
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* {{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]] |language=en-US |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.}}
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* {{cite web|first=Edward Moore|last=Geist|title=Is artificial intelligence really an existential threat to humanity?|date=9 August 2015|url=http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|url-status=live|archive-url=https://web.archive.org/web/20151030054330/http://thebulletin.org/artificial-intelligence-really-existential-threat-humanity8577|archive-date=30 October 2015|access-date=30 October 2015|website=Bulletin of the Atomic Scientists}}
* {{cite news |last=Gertner |first=Jon |author-link=Jon Gartner |title=Wikipedia's Moment of Truth – Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process? + comment
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* {{citation
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* {{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|volume=2|issue=2|pages=14–23|date=2016|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|access-date=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}
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{{Refend}}

== Further reading ==
{{refbegin|30em}}
* Ashish Vaswani, 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.
* [[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.
* [[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;192–98. [[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.)
* [[Pedro Domingos|Domingos, Pedro]], "Our Digital Doubles: AI will serve our species, not control it", ''[[Scientific American]]'', vol. 319, no. 3 (September 2018), pp.&nbsp;88–93. "AIs are like [[autistic savant]]s and will remain so for the foreseeable future.... AIs lack [[common sense]] and can easily make errors that a human never would... They are also liable to take our instructions too literally, giving us precisely what we asked for instead of what we actually wanted." (p.&nbsp;93.)
* 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]
* [[James Gleick|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.) 
* Hanna, Alex, and [[Emily M. Bender]], "Theoretical AI Harms Are a Distraction: Fearmongering about artificial intelligence's potential to end humanity shrouds the real harm it already causes", ''[[Scientific American]]'', vol 330, no. 2 (February 2024), pp. 69–70. "[H]ype [about "[[existential risks]]"] surrounds many AI firms, but their technology already enables myriad harms, including... [[discrimination]] in housing, criminal justice, and health care, as well as the spread of [[hate speech]] and [[misinformation]]... [[Large language models]] extrude... fluent... coherent-seeming text but have no [[understanding]] of what the text means, let alone the ability to [[reason]].... (p. 69.) [T]hat output... becomes a noxious... insidious pollutant of our [[information ecosystem]].... [T]oo many... publications [about] AI come from corporate labs or... academic groups that receive... industry funding. Many of these publications are based on [[junk science]] [that] is nonreproducible... is full of [[Promotion (marketing)|hype]], and uses evaluation methods that do not measure what they purport to... Meanwhile 'AI doomers' cite this junk science... to [misdirect] attention [to] the fantasy of all-powerful machines possibly going rogue and destroying humanity." (p. 70.)  
* [[Kenna Hughes-Castleberry|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.)
* [[Daniel Immerwahr|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 |title=Deep learning |journal=Nature |date=28 May 2015 |volume=521 |issue=7553 |pages=436–444 |doi=10.1038/nature14539 |pmid=26017442 |bibcode=2015Natur.521..436L |s2cid=3074096 |url=https://www.nature.com/articles/nature14539 |access-date=19 June 2023 |archive-date=5 June 2023 |archive-url=https://web.archive.org/web/20230605235832/https://www.nature.com/articles/nature14539 |url-status=live }}
* [[Gary Marcus|Marcus, Gary]], "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", ''[[Scientific American]]'', vol. 316, no. 3 (March 2017), pp.&nbsp;61–63. Marcus points out a so far insuperable stumbling block to artificial intelligence: an incapacity for reliable [[disambiguation]]. "[V]irtually every sentence [that people generate] is [[ambiguity|ambiguous]], often in multiple ways. Our brain is so good at comprehending [[language]] that we do not usually notice." A prominent example is the "pronoun disambiguation problem" ("PDP"): a machine has no way of determining to whom or what a [[pronoun]] in a sentence—such as "he", "she" or "it"—refers.
* [[Gary Marcus|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 |last1=Mitchell |first1=Melanie |title=Artificial intelligence: a guide for thinking humans |date=2019 |publisher=Farrar, Straus and Giroux |location=New York |isbn=9780374257835}}
* {{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 |title=Human-level control through deep reinforcement learning |journal=Nature |date=26 February 2015 |volume=518 |issue=7540 |pages=529–533 |doi=10.1038/nature14236 |pmid=25719670 |bibcode=2015Natur.518..529M |s2cid=205242740 |url=https://www.nature.com/articles/nature14236/ |access-date=19 June 2023 |archive-date=19 June 2023 |archive-url=https://web.archive.org/web/20230619055525/https://www.nature.com/articles/nature14236/ |url-status=live }} 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.
* [[Eka Roivainen|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."
* {{cite journal | last1 = Serenko | first1 = Alexander | author2 = Michael Dohan | year = 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 | journal = Journal of Informetrics | volume = 5 | issue = 4 | pages = 629–49 | doi = 10.1016/j.joi.2011.06.002 | access-date = 12 September 2013 | archive-date = 4 October 2013 | archive-url = https://web.archive.org/web/20131004212839/http://www.aserenko.com/papers/JOI_AI_Journal_Ranking_Serenko.pdf | url-status = live }}
* {{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 |title=Mastering the game of Go with deep neural networks and tree search |journal=Nature |date=28 January 2016 |volume=529 |issue=7587 |pages=484–489 |doi=10.1038/nature16961 |pmid=26819042 |bibcode=2016Natur.529..484S |s2cid=515925 |url=https://www.nature.com/articles/nature16961 |access-date=19 June 2023 |archive-date=18 June 2023 |archive-url=https://web.archive.org/web/20230618213059/https://www.nature.com/articles/nature16961 |url-status=live }}
* {{Cite book
| ref={{harvid|European Commission|2020}}
| 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
| year=2020
| location=Brussels
| access-date=20 February 2020
| archive-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
| url-status=live
}}
{{refend}}

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{{Sister project links|voy=no|species=no|d=Q11660|v=Portal:Artificial intelligence|n=no|s=no|c=Category:Artificial intelligence|wikt=artificial intelligence}}
* {{IEP|art-inte|Artificial Intelligence}}
* {{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).
* [https://datascience.cancer.gov/news-events/blog/theranostics-and-ai-next-advance-cancer-precision-medicine Theranostics and AI—The Next Advance in Cancer Precision Medicine].

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