Provide a brief summary for this article:
{{Short description|Intelligence of machines}}
{{Redirect|AI|other uses|AI (disambiguation)|and|Artificial intelligence (disambiguation)}}
{{Use dmy dates|date=July 2023}}{{Pp|small=yes}}
{{Artificial intelligence}}
'''Artificial intelligence''' ('''AI''') refers to the capability of [[computer|computational systems]] to perform tasks typically associated with [[human intelligence]], such as learning, reasoning, problem-solving, perception, and decision-making. It is a [[field of research]] in [[computer science]] that develops and studies methods and [[software]] that enable machines to [[machine perception|perceive their environment]] and use [[machine learning|learning]] and [[intelligence]] to take actions that maximize their chances of achieving defined goals.{{Sfnp|Russell|Norvig|2021|pp=1–4}} Such machines may be called AIs.
High-profile [[applications of AI]] include advanced [[web search engine]]s (e.g., [[Google Search]]); [[recommendation systems]] (used by [[YouTube]], [[Amazon (company)|Amazon]], and [[Netflix]]); [[virtual assistant]]s (e.g., [[Google Assistant]], [[Siri]], and [[Amazon Alexa|Alexa]]); [[autonomous vehicles]] (e.g., [[Waymo]]); [[Generative artificial intelligence|generative]] and [[Computational creativity|creative]] tools (e.g., [[ChatGPT]] and [[AI art]]); and [[Superintelligence|superhuman]] play and analysis in [[strategy game]]s (e.g., [[chess]] and [[Go (game)|Go]]). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's [[AI effect|not labeled AI anymore]]."[[http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/ AI set to exceed human brain power] {{Webarchive|url=https://web.archive.org/web/20080219001624/http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/|date=2008-02-19}} CNN.com (July 26, 2006)][{{Cite journal |last1=Kaplan |first1=Andreas |last2=Haenlein |first2=Michael |date=2019 |title=Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence |journal=Business Horizons |volume=62 |pages=15–25 |doi=10.1016/j.bushor.2018.08.004 |issn=0007-6813 |s2cid=158433736}}]
Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include learning, [[automated reasoning|reasoning]], [[knowledge representation]], [[Automated planning and scheduling|planning]], [[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 performed by a human on an at least equal level—is among the field's long-term goals.[{{Harvtxt|Russell|Norvig|2021|loc=§1.2}}.]
Artificial intelligence was founded as an academic discipline in 1956,[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}}] By the late 1980s and 1990s, methods were developed for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].[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}}]
Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow.[[[Intractably|Intractability and efficiency]] and the [[combinatorial explosion]]: {{Harvtxt|Russell|Norvig|2021|p=21}}] 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.[Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: {{Harvtxt|Kahneman|2011}}, {{Harvtxt|Dreyfus|Dreyfus|1986}}, {{Harvtxt|Wason|Shapiro|1966}}, {{Harvtxt|Kahneman|Slovic|Tversky|1982}}] 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]][[[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}}] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,{{Sfnp|Smoliar|Zhang|1994}} scene interpretation,{{Sfnp|Neumann|Möller|2008}} clinical decision support,{{Sfnp|Kuperman|Reichley|Bailey|2006}} knowledge discovery (mining "interesting" and actionable inferences from large [[database]]s),{{Sfnp|McGarry|2005}} and other areas.{{Sfnp|Bertini|Del Bimbo|Torniai|2006}}
A [[knowledge base]] is a body of knowledge represented in a form that can be used by a program. An [[ontology (information science)|ontology]] is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.{{Sfnp|Russell|Norvig|2021|pp=272}} Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;[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}}] situations, events, states, and 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}}] causes and effects;[[[Causality#Causal calculus|Causal calculus]]: {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=335–337}}] knowledge about knowledge (what we know about what other people know);[Representing knowledge about knowledge: Belief calculus, [[modal logic]]s: {{Harvtxt|Russell|Norvig|2021|loc=§10.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=275–277}}] [[default reasoning]] (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[[[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").] 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);[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]])] 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).[[[Automated planning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 11}}.] In [[automated decision-making]], the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "[[utility]]") that measures how much the agent prefers it. For each possible action, it can calculate the "[[expected utility]]": the [[utility]] of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.[[[Automated decision making]], [[Decision theory]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 16–18}}.]
In [[Automated planning and scheduling#classical planning|classical planning]], the agent knows exactly what the effect of any action will be.[[[Automated planning and scheduling#classical planning|Classical planning]]: {{Harvtxt|Russell|Norvig|2021|loc=Section 11.2}}.] 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.[Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning): {{Harvtxt|Russell|Norvig|2021|loc=Section 11.5}}.]
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.[Uncertain preferences: {{Harvtxt|Russell|Norvig|2021|loc=Section 16.7}}
[[Inverse reinforcement learning]]: {{Harvtxt|Russell|Norvig|2021|loc=Section 22.6}}] [[Information value theory]] can be used to weigh the value of exploratory or experimental actions.[[[Information value theory]]: {{Harvtxt|Russell|Norvig|2021|loc=Section 16.6}}.] The space of possible future actions and situations is typically [[intractably]] large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.
A [[Markov decision process]] has a [[Finite-state machine|transition model]] that describes the probability that a particular action will change the state in a particular way and a [[reward function]] that supplies the utility of each state and the cost of each action. A [[Reinforcement learning#Policy|policy]] associates a decision with each possible state. The policy could be calculated (e.g., by [[policy iteration|iteration]]), be [[heuristic]], or it can be learned.[[[Markov decision process]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 17}}.]
[[Game theory]] describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.[[[Game theory]] and multi-agent decision theory: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 18}}.]
=== Learning ===
[[Machine learning]] is the study of programs that can improve their performance on a given task automatically.[[[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}}] 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}}
}}
[[File:Supervised and unsupervised learning.png|right|upright=1.4|frameless]]
There are several kinds of machine learning. [[Unsupervised learning]] analyzes a stream of data and finds patterns and makes predictions without any other guidance.[[[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]])] [[Supervised learning]] requires labeling the training data with the expected answers, 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).[[[Supervised learning]]: {{Harvtxt|Russell|Norvig|2021|loc=§19.2}} (Definition), {{Harvtxt|Russell|Norvig|2021|loc=Chpt. 19–20}} (Techniques)]
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".[[[Reinforcement learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 22}}, {{Harvtxt|Luger|Stubblefield|2004|pp=442–449}}] [[Transfer learning]] is when the knowledge gained from one problem is applied to a new problem.[[[Transfer learning]]: {{Harvtxt|Russell|Norvig|2021|pp=281}}, {{Harvtxt|The Economist|2016}}] [[Deep learning]] is a type of machine learning that runs inputs through biologically inspired [[artificial neural networks]] for all of these types of learning.[{{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}}]
[[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization]].[[[Computational learning theory]]: {{Harvtxt|Russell|Norvig|2021|pp=672–674}}, {{Harvtxt|Jordan|Mitchell|2015}}]
{{Clear}}
=== Natural language processing ===
[[Natural language processing]] (NLP)[[[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}}] 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]].[Subproblems of [[Natural language processing|NLP]]: {{Harvtxt|Russell|Norvig|2021|pp=849–850}}]
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[Modern statistical and deep learning approaches to [[Natural language processing|NLP]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 24}}, {{Harvtxt|Cambria|White|2014}}] In 2019, [[generative pre-trained transformer]] (or "GPT") language models began to generate coherent text,{{Sfnp|Vincent|2019}}{{Sfnp|Russell|Norvig|2021|pp=875–878}} and by 2023, these models were able to get human-level scores on the [[bar exam]], [[SAT]] test, [[GRE]] test, and many other real-world applications.{{Sfnp|Bushwick|2023}}
=== Perception ===
[[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.[[[Computer vision]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 25}}, {{Harvtxt|Nilsson|1998|loc=chpt. 6}}]
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}}[[motion capture|object tracking]],{{Sfnp|Challa|Moreland|Mušicki|Evans|2011}} and [[robotic perception]].{{Sfnp|Russell|Norvig|2021|pp=931–938}}
=== Social intelligence ===
[[File:Kismet-IMG 6007-gradient.jpg|thumb|[[Kismet (robot)|Kismet]], a robot head which was made in the 1990s; it is a machine that can recognize and simulate emotions.{{Sfnp|MIT AIL|2014}}]]
[[Affective computing]] is a field that comprises systems that recognize, interpret, process, or simulate human [[Affect (psychology)|feeling, emotion, and mood]].[[[Affective computing]]: {{Harvtxt|Thro|1993}}, {{Harvtxt|Edelson|1991}}, {{Harvtxt|Tao|Tan|2005}}, {{Harvtxt|Scassellati|2002}}] 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 effects 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]].[
[[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}}
== 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.[[[Search algorithm]]s: {{Harvtxt|Russell|Norvig|2021|loc=chpts. 3–5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=113–163}}, {{Harvtxt|Luger|Stubblefield|2004|pp=79–164, 193–219}}, {{Harvtxt|Nilsson|1998|loc=chpts. 7–12}}] 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.[[[State space search]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 3}}] For example, [[Automated planning and scheduling|planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [[means-ends analysis]].{{Sfnp|Russell|Norvig|2021|loc=sect. 11.2}}
[[Brute force search|Simple exhaustive searches]][[[Uninformed search]]es ([[breadth first search]], [[depth-first search]] and general [[state space search]]): {{Harvtxt|Russell|Norvig|2021|loc=sect. 3.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=113–132}}, {{Harvtxt|Luger|Stubblefield|2004|pp=79–121}}, {{Harvtxt|Nilsson|1998|loc=chpt. 8}}] 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.[[[Heuristic]] or informed searches (e.g., greedy [[Best-first search|best first]] and [[A* search algorithm|A*]]): {{Harvtxt|Russell|Norvig|2021|loc=sect. 3.5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=132–147}}, {{Harvtxt|Poole|Mackworth|2017|loc=sect. 3.6}}, {{Harvtxt|Luger|Stubblefield|2004|pp=133–150}}]
[[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 countermoves, looking for a winning position.[[[Adversarial search]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 5}}]
==== Local search ====
[[File:Gradient descent.gif|class=skin-invert-image|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.[[[Local search (optimization)|Local]] or "[[optimization]]" search: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 4}}]
[[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 [[Artificial neural network|neural networks]],[{{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}}] through the [[backpropagation]] algorithm.
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.[[[Evolutionary computation]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 4.1.2}}]
Distributed search processes can coordinate via [[swarm intelligence]] algorithms. Two popular swarm algorithms used in search are [[particle swarm optimization]] (inspired by bird [[flocking]]) and [[ant colony optimization]] (inspired by [[ant trail]]s).{{Sfnp|Merkle|Middendorf|2013}}
=== Logic ===
Formal [[logic]] is used for [[automatic reasoning|reasoning]] and [[knowledge representation]].[[[Logic]]: {{Harvtxt|Russell|Norvig|2021|loc=chpts. 6–9}}, {{Harvtxt|Luger|Stubblefield|2004|pp=35–77}}, {{Harvtxt|Nilsson|1998|loc=chpt. 13–16}}]
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")[[[Propositional logic]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 6}}, {{Harvtxt|Luger|Stubblefield|2004|pp=45–50}}, {{Harvtxt|Nilsson|1998|loc=chpt. 13}}] 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").[[[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}}]
[[Deductive reasoning]] in logic is the process of [[logical proof|proving]] a new statement ([[Logical consequence|conclusion]]) from other statements that are given and assumed to be true (the [[premise]]s).[[[Logical inference]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 10}}] Proofs can be structured as proof [[tree structure|trees]], in which nodes are labelled by sentences, and children nodes are connected to parent nodes by [[inference rule]]s.
Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose [[leaf nodes]] are labelled by premises or [[axiom]]s. In the case of [[Horn clause]]s, problem-solving search can be performed by reasoning [[Forward chaining|forwards]] from the premises or [[backward chaining|backwards]] from the problem.[logical deduction as search: {{Harvtxt|Russell|Norvig|2021|loc=sects. 9.3, 9.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=~46–52}}, {{Harvtxt|Luger|Stubblefield|2004|pp=62–73}}, {{Harvtxt|Nilsson|1998|loc=chpt. 4.2, 7.2}}] In the more general case of the clausal form of [[first-order logic]], [[resolution (logic)|resolution]] is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.[[[Resolution (logic)|Resolution]] and [[unification (computer science)|unification]]: {{Harvtxt|Russell|Norvig|2021|loc= sections 7.5.2, 9.2, 9.5}}]
Inference in both Horn clause logic and first-order logic is [[Undecidable problem|undecidable]], and therefore [[Intractable problem|intractable]]. However, backward reasoning with Horn clauses, which underpins computation in the [[logic programming]] language [[Prolog]], is [[Turing complete]]. Moreover, its efficiency is competitive with computation in other [[symbolic programming]] languages.[{{Cite journal |last1=Warren |first1=D.H. |last2=Pereira |first2=L.M. |last3=Pereira |first3=F. |date=1977 |title=Prolog-the language and its implementation compared with Lisp |journal=[[ACM SIGPLAN Notices]] |volume=12 |issue=8 |pages=109–115 |doi=10.1145/872734.806939}}]
[[Fuzzy logic]] assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.[Fuzzy logic: {{Harvtxt|Russell|Norvig|2021|pp=214, 255, 459}}, {{Harvtxt|Scientific American|1999}}]
[[Non-monotonic logic]]s, including logic programming with [[negation as failure]], are designed to handle [[default reasoning]].[Stochastic methods for uncertain reasoning: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 12–18, 20}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=345–395}}, {{Harvtxt|Luger|Stubblefield|2004|pp=165–191, 333–381}}, {{Harvtxt|Nilsson|1998|loc=chpt. 19}}] Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using [[decision theory]], [[decision analysis]],[[[decision theory]] and [[decision analysis]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 16–18}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=381–394}}] and [[information value theory]].[[[Information value theory]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 16.6}}] These tools include models such as [[Markov decision process]]es,[[[Markov decision process]]es and dynamic [[decision network]]s: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 17}}] dynamic [[decision network]]s,[[[Game theory]] and [[mechanism design]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 18}}]
[[Bayesian network]]s[[[Bayesian network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sects. 12.5–12.6, 13.4–13.5, 14.3–14.5, 16.5, 20.2–20.3}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=361–381}}, {{Harvtxt|Luger|Stubblefield|2004|pp=~182–190, ≈363–379}}, {{Harvtxt|Nilsson|1998|loc=chpt. 19.3–19.4}}] are a tool that can be used for [[automated reasoning|reasoning]] (using the [[Bayesian inference]] algorithm),{{Efn|
Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be [[conditionally independent]] of one another. [[AdSense]] uses a Bayesian network with over 300 million edges to learn which ads to serve.{{Sfnp|Domingos|2015|loc=chpt. 6}}
}}[[[Bayesian inference]] algorithm: {{Harvtxt|Russell|Norvig|2021|loc=sect. 13.3–13.5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=361–381}}, {{Harvtxt|Luger|Stubblefield|2004|pp=~363–379}}, {{Harvtxt|Nilsson|1998|loc=chpt. 19.4 & 7}}] [[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}}}}[[[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}}] [[Automated planning and scheduling|planning]] (using [[decision network]]s)[[[Bayesian decision theory]] and Bayesian [[decision network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 16.5}}] and [[Machine perception|perception]] (using [[dynamic Bayesian network]]s).[Stochastic temporal models: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 14}}
[[Hidden Markov model]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 14.3}}
[[Kalman filter]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 14.4}}
[[Dynamic Bayesian network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 14.5}}]
[[File:EM_Clustering_of_Old_Faithful_data.gif|thumb|upright=1.2|[[Expectation–maximization algorithm|Expectation–maximization]] [[cluster analysis|clustering]] of [[Old Faithful]] eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.]]
=== Classifiers and statistical learning methods ===
The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. [[Classifier (mathematics)|Classifiers]][Statistical learning methods and [[Classifier (mathematics)|classifiers]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 20}},] 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.[{{Cite book |last1=Ciaramella |first1=Alberto |author-link=Alberto Ciaramella |title=Introduction to Artificial Intelligence: from data analysis to generative AI |last2=Ciaramella |first2=Marco |date=2024 |publisher=Intellisemantic Editions |isbn=978-8-8947-8760-3}}] The [[decision tree]] is the simplest and most widely used symbolic machine learning algorithm.[[[Alternating decision tree|Decision tree]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 19.3}}, {{Harvtxt|Domingos|2015|p=88}}] [[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.[[[Nonparametric statistics|Non-parameteric]] learning models such as [[K-nearest neighbor]] and [[support vector machines]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 19.7}}, {{Harvtxt|Domingos|2015|p=187}} (k-nearest neighbor)
* {{Harvtxt|Domingos|2015|p=88}} (kernel methods)]
The [[naive Bayes classifier]] is reportedly the "most widely used learner"{{Sfnp|Domingos|2015|p=152}} at Google, due in part to its scalability.[[[Naive Bayes classifier]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 12.6}}, {{Harvtxt|Domingos|2015|p=152}}]
[[Artificial neural network|Neural networks]] are also used as classifiers.[Neural networks: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 21}}, {{Harvtxt|Domingos|2015|loc=Chapter 4}}]
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.[Gradient calculation in computational graphs, [[backpropagation]], [[automatic differentiation]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.2}}, {{Harvtxt|Luger|Stubblefield|2004|pp=467–474}}, {{Harvtxt|Nilsson|1998|loc=chpt. 3.3}}] 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.[[[Universal approximation theorem]]: {{Harvtxt|Russell|Norvig|2021|p=752}}
The theorem: {{Harvtxt|Cybenko|1988}}, {{Harvtxt|Hornik|Stinchcombe|White|1989}}]
In [[feedforward neural network]]s the signal passes in only one direction.[[[Feedforward neural network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.1}}] [[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.[[[Recurrent neural network]]s: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.6}}] [[Perceptron]]s[[[Perceptron]]s: {{Harvtxt|Russell|Norvig|2021|pp=21, 22, 683, 22}}] use only a single layer of neurons; deep learning[[[Convolutional neural networks]]: {{Harvtxt|Russell|Norvig|2021|loc=sect. 21.3}}]
{{Clear}}
=== Deep learning ===
[[File:AI hierarchy.svg|thumb|upright]]
[[Deep learning]][[[Deep learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 21}}, {{Harvtxt|Goodfellow|Bengio|Courville|2016}}, {{Harvtxt|Hinton ''et al.''|2016}}, {{Harvtxt|Schmidhuber|2015}}] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in [[image processing]], lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.{{Sfnp|Deng|Yu|2014|pp=199–200}}
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including [[computer vision]], [[speech recognition]], [[natural language processing]], [[image classification]],{{Sfnp|Ciresan|Meier|Schmidhuber|2012}} and others. The reason that deep learning performs so well in so many applications is not known as of 2021.{{Sfnp|Russell|Norvig|2021|p=750}} The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s){{Efn|
Some form of deep neural networks (without a specific learning algorithm) were described by:
[[Warren S. McCulloch]] and [[Walter Pitts]] (1943){{Sfnp|Russell|Norvig|2021|p=17}}
[[Alan Turing]] (1948);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Karl Steinbuch]] and [[Roger David Joseph]] (1961).{{Sfnp|Schmidhuber|2022|loc=sect. 5}}
Deep or recurrent networks that learned (or used gradient descent) were developed by:
[[Frank Rosenblatt]](1957);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Oliver Selfridge]] (1959);{{Sfnp|Schmidhuber|2022|loc=sect. 5}}
[[Alexey Ivakhnenko]] and [[Valentin Lapa]] (1965);{{Sfnp|Schmidhuber|2022|loc=sect. 6}}
[[Kaoru Nakano]] (1971);{{Sfnp|Schmidhuber|2022|loc=sect. 7}}
[[Shun-Ichi Amari]] (1972);{{Sfnp|Schmidhuber|2022|loc=sect. 7}}
[[John Joseph Hopfield]] (1982).{{Sfnp|Schmidhuber|2022|loc=sect. 7}}
Precursors to backpropagation were developed by:
[[Henry J. Kelley]] (1960);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Arthur E. Bryson]] (1962);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Stuart Dreyfus]] (1962);{{Sfnp|Russell|Norvig|2021|p=785}}
[[Arthur E. Bryson]] and [[Yu-Chi Ho]] (1969);{{Sfnp|Russell|Norvig|2021|p=785}}
Backpropagation was independently developed by:
[[Seppo Linnainmaa]] (1970);{{Sfnp|Schmidhuber|2022|loc=sect. 8}}
[[Paul Werbos]] (1974).{{Sfnp|Russell|Norvig|2021|p=785}}
}} but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to [[GPU]]s) and the availability of vast amounts of training data, especially the giant [[List of datasets for machine-learning research|curated datasets]] used for benchmark testing, such as [[ImageNet]].{{Efn|[[Geoffrey Hinton]] said, of his work on neural networks in the 1990s, "our labeled datasets were thousands of times too small. [And] our computers were millions of times too slow."[Quoted in {{Harvtxt|Christian|2020|p=22}}]}}
===GPT===
[[Generative pre-trained transformer]]s (GPT) are [[large language model]]s (LLMs) that generate text based on the semantic relationships between words in sentences. Text-based GPT models are pretrained on a large [[corpus of text]] that can be from the Internet. The pretraining consists of predicting the next [[Lexical analysis|token]] (a token being usually a word, subword, or punctuation). Throughout this pretraining, GPT models accumulate knowledge about the world and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful, and harmless, usually with a technique called [[reinforcement learning from human feedback]] (RLHF). Current GPT models are prone to generating falsehoods called "[[Hallucination (artificial intelligence)|hallucinations]]", although this can be reduced with RLHF and quality data. They are used in [[chatbot]]s, which allow people to ask a question or request a task in simple text.{{Sfnp|Smith|2023}}[{{Cite web |date=9 November 2023 |title=Explained: Generative AI |url=https://news.mit.edu/2023/explained-generative-ai-1109}}]
Current models and services include [[Gemini (chatbot)|Gemini]] (formerly Bard), [[ChatGPT]], [[Grok (chatbot)|Grok]], [[Anthropic#Claude|Claude]], [[Microsoft Copilot|Copilot]], and [[LLaMA]].[{{Cite web |title=AI Writing and Content Creation Tools |url=https://mitsloanedtech.mit.edu/ai/tools/writing |access-date=25 December 2023 |publisher=MIT Sloan Teaching & Learning Technologies |archive-date=25 December 2023 |archive-url=https://web.archive.org/web/20231225232503/https://mitsloanedtech.mit.edu/ai/tools/writing/ |url-status=live }}] [[Multimodal learning|Multimodal]] GPT models can process different types of data ([[Modality (human–computer interaction)|modalities]]) such as images, videos, sound, and text.{{Sfnp|Marmouyet|2023}}
===Hardware and software===
{{Main|Programming languages for artificial intelligence|Hardware for artificial intelligence}}
In the late 2010s, [[graphics processing unit]]s (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized [[TensorFlow]] software had replaced previously used [[central processing unit]] (CPUs) as the dominant means for large-scale (commercial and academic) [[machine learning]] models' training.{{Sfnp|Kobielus|2019}} Specialized [[programming language]]s such as [[Prolog]] were used in early AI research,[{{Cite web |last=Thomason |first=James |date=2024-05-21 |title=Mojo Rising: The resurgence of AI-first programming languages |url=https://venturebeat.com/ai/mojo-rising-the-resurgence-of-ai-first-programming-languages |access-date=2024-05-26 |website=VentureBeat |archive-date=27 June 2024 |archive-url=https://web.archive.org/web/20240627143853/https://venturebeat.com/ai/mojo-rising-the-resurgence-of-ai-first-programming-languages/ |url-status=live }}] but [[general-purpose programming language]]s like [[Python (programming language)|Python]] have become predominant.[{{Cite news |last=Wodecki |first=Ben |date=May 5, 2023 |title=7 AI Programming Languages You Need to Know |url=https://aibusiness.com/verticals/7-ai-programming-languages-you-need-to-know |work=AI Business |access-date=5 October 2024 |archive-date=25 July 2024 |archive-url=https://web.archive.org/web/20240725164443/https://aibusiness.com/verticals/7-ai-programming-languages-you-need-to-know |url-status=live }}]
The transistor density in [[integrated circuit]]s has been observed to roughly double every 18 months—a trend known as [[Moore's law]], named after the [[Intel]] co-founder [[Gordon Moore]], who first identified it. Improvements in [[GPUs]] have been even faster,[{{Cite web |last=Plumb |first=Taryn |date=2024-09-18 |title=Why Jensen Huang and Marc Benioff see 'gigantic' opportunity for agentic AI |url=https://venturebeat.com/ai/why-jensen-huang-and-marc-benioff-see-gigantic-opportunity-for-agentic-ai/ |access-date=2024-10-04 |website=VentureBeat |language=en-US |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165649/https://venturebeat.com/ai/why-jensen-huang-and-marc-benioff-see-gigantic-opportunity-for-agentic-ai/ |url-status=live }}] a trend sometimes called [[Huang's law]],[{{Cite news |last=Mims |first=Christopher |date=2020-09-19 |title=Huang's Law Is the New Moore's Law, and Explains Why Nvidia Wants Arm |url=https://www.wsj.com/articles/huangs-law-is-the-new-moores-law-and-explains-why-nvidia-wants-arm-11600488001 |access-date=2025-01-19 |work=Wall Street Journal |language=en-US |issn=0099-9660 |archive-date=2 October 2023 |archive-url=https://web.archive.org/web/20231002080608/https://www.wsj.com/articles/huangs-law-is-the-new-moores-law-and-explains-why-nvidia-wants-arm-11600488001 |url-status=live }}] named after [[Nvidia]] co-founder and CEO [[Jensen Huang]].
== Applications ==
{{Main|Applications of artificial intelligence}}AI and machine learning technology is used in most of the essential applications of the 2020s, including: [[search engines]] (such as [[Google Search]]), [[Targeted advertising|targeting online advertisements]], [[recommendation systems]] (offered by [[Netflix]], [[YouTube]] or [[Amazon (company)|Amazon]]), driving [[internet traffic]], [[Marketing and artificial intelligence|targeted advertising]] ([[AdSense]], [[Facebook]]), [[virtual assistant]]s (such as [[Siri]] or [[Amazon Alexa|Alexa]]), [[autonomous vehicles]] (including [[Unmanned aerial vehicle|drones]], [[Advanced driver-assistance system|ADAS]] and [[self-driving cars]]), [[automatic language translation]] ([[Microsoft Translator]], [[Google Translate]]), [[Facial recognition system|facial recognition]] ([[Apple Computer|Apple]]'s [[Face ID]] or [[Microsoft]]'s [[DeepFace]] and [[Google]]'s [[FaceNet]]) and [[image labeling]] (used by [[Facebook]], Apple's [[iPhoto]] and [[TikTok]]). The deployment of AI may be overseen by a [[Chief automation officer]] (CAO).
===Health and medicine===
{{Main|Artificial intelligence in healthcare}}
The application of AI in [[medicine]] and [[medical research]] has the potential to increase patient care and quality of life.[{{Cite journal |last1=Davenport |first1=T |last2=Kalakota |first2=R |date=June 2019 |title=The potential for artificial intelligence in healthcare |journal=Future Healthc J. |volume=6 |issue=2 |pages=94–98 |doi=10.7861/futurehosp.6-2-94 |pmc=6616181 |pmid=31363513}}] Through the lens of the [[Hippocratic Oath]], medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.[{{Cite journal |last1=Lyakhova |first1=U.A. |last2=Lyakhov |first2=P.A. |date=2024 |title=Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects |url=https://linkinghub.elsevier.com/retrieve/pii/S0010482524008278 |journal=Computers in Biology and Medicine |language=en |volume=178 |pages=108742 |doi=10.1016/j.compbiomed.2024.108742 |pmid=38875908 |archive-date=3 December 2024 |access-date=10 October 2024 |archive-url=https://web.archive.org/web/20241203172502/https://linkinghub.elsevier.com/retrieve/pii/S0010482524008278 |url-status=live }}][{{Cite journal |last1=Alqudaihi |first1=Kawther S. |last2=Aslam |first2=Nida |last3=Khan |first3=Irfan Ullah |last4=Almuhaideb |first4=Abdullah M. |last5=Alsunaidi |first5=Shikah J. |last6=Ibrahim |first6=Nehad M. Abdel Rahman |last7=Alhaidari |first7=Fahd A. |last8=Shaikh |first8=Fatema S. |last9=Alsenbel |first9=Yasmine M. |last10=Alalharith |first10=Dima M. |last11=Alharthi |first11=Hajar M. |last12=Alghamdi |first12=Wejdan M. |last13=Alshahrani |first13=Mohammed S. |date=2021 |title=Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities |journal=IEEE Access |volume=9 |pages=102327–102344 |doi=10.1109/ACCESS.2021.3097559 |issn=2169-3536 |pmc=8545201 |pmid=34786317|bibcode=2021IEEEA...9j2327A }}]
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.[{{Cite journal |last1=Bax |first1=Monique |last2=Thorpe |first2=Jordan |last3=Romanov |first3=Valentin |date=December 2023 |title=The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence |journal=Frontiers in Sensors |volume=4 |doi=10.3389/fsens.2023.1294721 |issn=2673-5067 |doi-access=free}}] It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.[{{Cite journal |last=Dankwa-Mullan |first=Irene |date=2024 |title=Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine |url=https://www.cdc.gov/pcd/issues/2024/24_0245.htm |journal=Preventing Chronic Disease |language=en-us |volume=21 |pages=E64 |doi=10.5888/pcd21.240245 |pmid=39173183 |issn=1545-1151|pmc=11364282 }}] New AI tools can deepen the understanding of biomedically relevant pathways. For example, [[AlphaFold 2]] (2021) demonstrated the ability to approximate, in hours rather than months, the 3D [[Protein structure|structure of a protein]].[{{Cite journal |last1=Jumper |first1=J |last2=Evans |first2=R |last3=Pritzel |first3=A |date=2021 |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |volume=596 |issue=7873 |pages=583–589 |bibcode=2021Natur.596..583J |doi=10.1038/s41586-021-03819-2 |pmc=8371605 |pmid=34265844}}] 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.[{{Cite web |date=2023-12-20 |title=AI discovers new class of antibiotics to kill drug-resistant bacteria |url=https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/ |access-date=5 October 2024 |archive-date=16 September 2024 |archive-url=https://web.archive.org/web/20240916014421/https://www.newscientist.com/article/2409706-ai-discovers-new-class-of-antibiotics-to-kill-drug-resistant-bacteria/ |url-status=live }}] In 2024, researchers used machine learning to accelerate the search for [[Parkinson's disease]] drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of [[alpha-synuclein]] (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold.[{{Cite web |date=2024-04-17 |title=AI speeds up drug design for Parkinson's ten-fold |url=https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold |publisher=Cambridge University |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165755/https://www.cam.ac.uk/research/news/ai-speeds-up-drug-design-for-parkinsons-ten-fold |url-status=live }}][{{Cite journal |last1=Horne |first1=Robert I. |last2=Andrzejewska |first2=Ewa A. |last3=Alam |first3=Parvez |last4=Brotzakis |first4=Z. Faidon |last5=Srivastava |first5=Ankit |last6=Aubert |first6=Alice |last7=Nowinska |first7=Magdalena |last8=Gregory |first8=Rebecca C. |last9=Staats |first9=Roxine |last10=Possenti |first10=Andrea |last11=Chia |first11=Sean |last12=Sormanni |first12=Pietro |last13=Ghetti |first13=Bernardino |last14=Caughey |first14=Byron |last15=Knowles |first15=Tuomas P. J. |last16=Vendruscolo |first16=Michele |date=2024-04-17 |title=Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning |journal=Nature Chemical Biology |publisher=Nature |volume=20 |issue=5 |pages=634–645 |doi=10.1038/s41589-024-01580-x |pmc=11062903 |pmid=38632492}}]
=== 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.[{{Cite magazine |last1=Grant |first1=Eugene F. |last2=Lardner |first2=Rex |date=1952-07-25 |title=The Talk of the Town – It |url=https://www.newyorker.com/magazine/1952/08/02/it |access-date=2024-01-28 |magazine=The New Yorker |issn=0028-792X |archive-date=16 February 2020 |archive-url=https://web.archive.org/web/20200216034025/https://www.newyorker.com/magazine/1952/08/02/it |url-status=live }}] [[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.[{{Cite web |last=Anderson |first=Mark Robert |date=2017-05-11 |title=Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution |url=http://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |access-date=2024-01-28 |website=The Conversation |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917000827/https://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |url-status=live }}] 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.[{{Cite news |last=Markoff |first=John |date=2011-02-16 |title=Computer Wins on 'Jeopardy!': Trivial, It's Not |url=https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-access=subscription |access-date=2024-01-28 |work=The New York Times |issn=0362-4331 |archive-date=22 October 2014 |archive-url=https://web.archive.org/web/20141022023202/http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-status=live }}] 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.[{{Cite web |last=Byford |first=Sam |date=2017-05-27 |title=AlphaGo retires from competitive Go after defeating world number one 3–0 |url=https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |access-date=2024-01-28 |website=The Verge |archive-date=7 June 2017 |archive-url=https://web.archive.org/web/20170607184301/https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |url-status=live }}] Other programs handle [[Imperfect information|imperfect-information]] games, such as the [[poker]]-playing program [[Pluribus (poker bot)|Pluribus]].[{{Cite journal |last1=Brown |first1=Noam |last2=Sandholm |first2=Tuomas |date=2019-08-30 |title=Superhuman AI for multiplayer poker |url=https://www.science.org/doi/10.1126/science.aay2400 |journal=Science |volume=365 |issue=6456 |pages=885–890 |bibcode=2019Sci...365..885B |doi=10.1126/science.aay2400 |issn=0036-8075 |pmid=31296650}}] [[DeepMind]] developed increasingly generalistic [[reinforcement learning]] models, such as with [[MuZero]], which could be trained to play chess, Go, or [[Atari]] games.[{{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}}] 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.[{{Cite news |last=Sample |first=Ian |date=2019-10-30 |title=AI becomes grandmaster in 'fiendishly complex' StarCraft II |url=https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |access-date=2024-01-28 |work=The Guardian |issn=0261-3077 |archive-date=29 December 2020 |archive-url=https://web.archive.org/web/20201229185547/https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |url-status=live }}] In 2021, an AI agent competed in a PlayStation [[Gran Turismo (series)|Gran Turismo]] competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.[{{Cite journal |last1=Wurman |first1=P. R. |last2=Barrett |first2=S. |last3=Kawamoto |first3=K. |date=2022 |title=Outracing champion Gran Turismo drivers with deep reinforcement learning |journal=Nature |volume=602 |issue=7896 |pages=223–228 |bibcode=2022Natur.602..223W |doi=10.1038/s41586-021-04357-7 |pmid=35140384|url=https://www.researchsquare.com/article/rs-795954/latest.pdf }}] In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen [[open-world]] video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.[{{Cite web |last=Wilkins |first=Alex |date=13 March 2024 |title=Google AI learns to play open-world video games by watching them |url=https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them |access-date=2024-07-21 |website=New Scientist |archive-date=26 July 2024 |archive-url=https://web.archive.org/web/20240726182946/https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them/ |url-status=live }}]
=== Mathematics ===
Large language models, such as [[GPT-4]], [[Gemini (chatbot)|Gemini]], [[Claude (language model)|Claude]], [[Llama (language model)|LLaMa]] or [[Mistral AI|Mistral]], are increasingly used in mathematics. These probabilistic models are versatile, but can also produce wrong answers in the form of [[Hallucination (artificial intelligence)|hallucinations]]. They sometimes need a large database of mathematical problems to learn from, but also methods such as [[Supervised learning|supervised]] [[Fine-tuning (deep learning)|fine-tuning]][{{Cite journal |date=2024 |title=ReFT: Representation Finetuning for Language Models |journal=NeurIPS |arxiv=2404.03592 |last1=Wu |first1=Zhengxuan |last2=Arora |first2=Aryaman |last3=Wang |first3=Zheng |last4=Geiger |first4=Atticus |last5=Jurafsky |first5=Dan |last6=Manning |first6=Christopher D. |last7=Potts |first7=Christopher }}] or trained [[Statistical classification|classifiers]] with human-annotated data to improve answers for new problems and learn from corrections.[{{Cite web |date=2023-05-31 |title=Improving mathematical reasoning with process supervision |url=https://openai.com/index/improving-mathematical-reasoning-with-process-supervision/ |access-date=2025-01-26 |website=OpenAI |language=en-US}}] A February 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low, even for problems with only minor deviations from trained data.[{{Cite arXiv |eprint=2402.19450 |class=cs.AI |first=Saurabh |last=Srivastava |title=Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap |date=2024-02-29}}] One technique to improve their performance involves training the models to produce correct [[Automated reasoning|reasoning]] steps, rather than just the correct result.[{{cite arXiv |eprint=2305.20050v1 |class=cs.LG |first1=Hunter |last1=Lightman |first2=Vineet |last2=Kosaraju |title=Let's Verify Step by Step |date=2023 |last3=Burda |first3=Yura |last4=Edwards |first4=Harri |last5=Baker |first5=Bowen |last6=Lee |first6=Teddy |last7=Leike |first7=Jan |last8=Schulman |first8=John |last9=Sutskever |first9=Ilya |last10=Cobbe |first10=Karl}}] The [[Alibaba Group]] developed a version of its ''[[Qwen]]'' models called ''Qwen2-Math'', that achieved state-of-the-art performance on several mathematical benchmarks, including 84% accuracy on the MATH dataset of competition mathematics problems.[{{cite web |last1=Franzen |first1=Carl |title=Alibaba claims no. 1 spot in AI math models with Qwen2-Math |url=https://venturebeat.com/ai/alibaba-claims-no-1-spot-in-ai-math-models-with-qwen2-math/ |website=VentureBeat |date=2024-08-08|access-date=2025-02-16}}] In January 2025, Microsoft proposed the technique ''rStar-Math'' that leverages [[Monte Carlo tree search]] and step-by-step reasoning, enabling a relatively small language model like ''Qwen-7B'' to solve 53% of the [[American Invitational Mathematics Examination|AIME]] 2024 and 90% of the MATH benchmark problems.[{{Cite web |last=Franzen |first=Carl |date=2025-01-09 |title=Microsoft's new rStar-Math technique upgrades small models to outperform OpenAI's o1-preview at math problems |url=https://venturebeat.com/ai/microsofts-new-rstar-math-technique-upgrades-small-models-to-outperform-openais-o1-preview-at-math-problems/ |access-date=2025-01-26 |website=VentureBeat |language=en-US}}]
Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as ''AlphaTensor'', ''[[AlphaGeometry]]'' and ''AlphaProof'' all from [[Google DeepMind]],[{{Cite web |last=Roberts |first=Siobhan |date=July 25, 2024 |title=AI achieves silver-medal standard solving International Mathematical Olympiad problems |url=https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html |access-date=2024-08-07 |website=[[The New York Times]] |archive-date=26 September 2024 |archive-url=https://web.archive.org/web/20240926131402/https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html |url-status=live }}] ''Llemma'' from [[EleutherAI]][{{Cite web |last1=Azerbayev |first1=Zhangir |last2=Schoelkopf |first2=Hailey |last3=Paster |first3=Keiran |last4=Santos |first4=Marco Dos |last5=McAleer' |first5=Stephen |last6=Jiang |first6=Albert Q. |last7=Deng |first7=Jia |last8=Biderman |first8=Stella |last9=Welleck |first9=Sean |date=2023-10-16 |title=Llemma: An Open Language Model For Mathematics |url=https://blog.eleuther.ai/llemma/ |access-date=2025-01-26 |website=EleutherAI Blog |language=en}}] or ''Julius''.[{{Cite web |title=Julius AI |url=https://julius.ai/home/ai-math |access-date= |website=julius.ai |language=en}}]
When natural language is used to describe mathematical problems, converters can transform such prompts into a formal language such as [[Lean (proof assistant)|Lean]] to define mathematical tasks.
Some models have been developed to solve challenging problems and reach good results in benchmark tests, others to serve as educational tools in mathematics.[{{Cite web |last=McFarland |first=Alex |date=2024-07-12 |title=8 Best AI for Math Tools (January 2025) |url=https://www.unite.ai/best-ai-for-math-tools/ |access-date=2025-01-26 |website=Unite.AI |language=en-US}}]
[[Topological deep learning]] integrates various [[topology|topological]] approaches.
=== Finance ===
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.[Matthew Finio & Amanda Downie: IBM Think 2024 Primer, "What is Artificial Intelligence (AI) in Finance?" 8 Dec. 2023]
According to Nicolas Firzli, director of the [[World Pensions & Investments Forum]], it may be too early to see the emergence of highly innovative AI-informed financial products and services. He argues that "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."[M. Nicolas, J. Firzli: Pensions Age / European Pensions magazine, "Artificial Intelligence: Ask the Industry", May–June 2024. https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ {{Webarchive|url=https://web.archive.org/web/20240911125502/https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ |date=11 September 2024}}.]
=== Military ===
{{main|Military applications of artificial intelligence}}
Various countries are deploying AI military applications.[{{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|archive-date=8 May 2020|access-date=25 February 2024|archive-url=https://web.archive.org/web/20200508062631/https://fas.org/sgp/crs/natsec/R45178.pdf|url-status=live}}[[Template:PD-notice|PD-notice]]] The main applications enhance [[command and control]], communications, sensors, integration and interoperability.[{{cite report |type=Preprint |last1=Slyusar |first1=Vadym |title=Artificial intelligence as the basis of future control networks |date=2019 |doi=10.13140/RG.2.2.30247.50087 }}] Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and [[Vehicular automation|autonomous vehicles]].[{{Cite web |last=Iraqi |first=Amjad |date=2024-04-03 |title='Lavender': The AI machine directing Israel's bombing spree in Gaza |url=https://www.972mag.com/lavender-ai-israeli-army-gaza/ |access-date=2024-04-06 |website=+972 Magazine |language=en-US |archive-date=10 October 2024 |archive-url=https://web.archive.org/web/20241010022042/https://www.972mag.com/lavender-ai-israeli-army-gaza/ |url-status=live }}][{{Cite news |last1=Davies |first1=Harry |last2=McKernan |first2=Bethan |last3=Sabbagh |first3=Dan |date=2023-12-01 |title='The Gospel': how Israel uses AI to select bombing targets in Gaza |language=en-GB |work=The Guardian |url=https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets |access-date=2023-12-04 |archive-date=6 December 2023 |archive-url=https://web.archive.org/web/20231206213901/https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets |url-status=live }}][{{Cite news|last=Marti|first=J Werner|title=Drohnen haben den Krieg in der Ukraine revolutioniert, doch sie sind empfindlich auf Störsender – deshalb sollen sie jetzt autonom operieren|url=https://www.nzz.ch/international/die-ukraine-setzt-auf-drohnen-die-autonom-navigieren-und-toeten-koennen-ld.1838731|date=10 August 2024|access-date=10 August 2024|newspaper=Neue Zürcher Zeitung|language=German|archive-date=10 August 2024|archive-url=https://web.archive.org/web/20240810054043/https://www.nzz.ch/international/die-ukraine-setzt-auf-drohnen-die-autonom-navigieren-und-toeten-koennen-ld.1838731|url-status=live}}]
=== Generative AI ===
[[File:Vincent van Gogh in watercolour.png|thumb|[[Vincent van Gogh]] in watercolour created by generative AI software]]{{Excerpt|Generative artificial intelligence|only=paragraphs|paragraphs=1-3}}
===Agents===
Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including [[virtual assistant]]s, [[chatbots]], [[autonomous vehicles]], [[Video game console|game-playing systems]], and [[industrial robotics]]. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations. This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities. In real-world applications, AI agents often face time constraints for decision-making and action execution. Many AI agents incorporate learning algorithms, enabling them to improve their performance over time through experience or training. Using machine learning, AI agents can adapt to new situations and optimise their behaviour for their designated tasks.[{{Cite book |last1=Poole |first1=David |url=https://doi.org/10.1017/9781009258227 |title=Artificial Intelligence, Foundations of Computational Agents |last2=Mackworth |first2=Alan |date=2023 |publisher=Cambridge University Press |isbn=978-1-0092-5819-7 |edition=3rd |doi=10.1017/9781009258227 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://www.cambridge.org/highereducation/books/artificial-intelligence/C113F6CE284AB00F5489EBA5A59B93B7#overview |url-status=live }}][{{Cite book |last1=Russell |first1=Stuart |title=[[Artificial Intelligence: A Modern Approach]] |last2=Norvig |first2=Peter |publisher=Pearson |date=2020 |isbn=978-0-1346-1099-3 |edition=4th}}][{{Cite web |date=2024-07-24 |title=Why agents are the next frontier of generative AI |url=https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai |access-date=2024-08-10 |website=McKinsey Digital |archive-date=3 October 2024 |archive-url=https://web.archive.org/web/20241003212335/https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai |url-status=live }}]
=== Sexuality ===
Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer prediction,[{{Cite journal |last1=Figueiredo |first1=Mayara Costa |last2=Ankrah |first2=Elizabeth |last3=Powell |first3=Jacquelyn E. |last4=Epstein |first4=Daniel A. |last5=Chen |first5=Yunan |date=2024-01-12 |title=Powered by AI: Examining How AI Descriptions Influence Perceptions of Fertility Tracking Applications |url=https://dl.acm.org/doi/10.1145/3631414 |journal=Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. |volume=7 |issue=4 |pages=154:1–154:24 |doi=10.1145/3631414}}] AI-integrated sex toys (e.g., [[teledildonics]]),[{{Cite journal |last1=Power |first1=Jennifer |last2=Pym |first2=Tinonee |last3=James |first3=Alexandra |last4=Waling |first4=Andrea |date=2024-07-05 |title=Smart Sex Toys: A Narrative Review of Recent Research on Cultural, Health and Safety Considerations |journal=Current Sexual Health Reports |language=en |volume=16 |issue=3 |pages=199–215 |doi=10.1007/s11930-024-00392-3 |issn=1548-3592 |doi-access=free}}] AI-generated sexual education content,[{{Cite journal |last1=Marcantonio |first1=Tiffany L. |last2=Avery |first2=Gracie |last3=Thrash |first3=Anna |last4=Leone |first4=Ruschelle M. |date=2024-09-10 |title=Large Language Models in an App: Conducting a Qualitative Synthetic Data Analysis of How Snapchat's "My AI" Responds to Questions About Sexual Consent, Sexual Refusals, Sexual Assault, and Sexting |url=https://www.tandfonline.com/doi/full/10.1080/00224499.2024.2396457 |url-status=live |journal=The Journal of Sex Research |language=en |pages=1–15 |doi=10.1080/00224499.2024.2396457 |pmid=39254628 |pmc=11891083 |pmc-embargo-date=March 10, 2026 |issn=0022-4499 |archive-url=https://web.archive.org/web/20241209185843/https://www.tandfonline.com/doi/full/10.1080/00224499.2024.2396457 |archive-date=9 December 2024 |access-date=9 December 2024}}] and AI agents that simulate sexual and romantic partners (e.g., [[Replika]]).[{{Cite journal |last1=Hanson |first1=Kenneth R. |last2=Bolthouse |first2=Hannah |date=2024 |title="Replika Removing Erotic Role-Play Is Like Grand Theft Auto Removing Guns or Cars": Reddit Discourse on Artificial Intelligence Chatbots and Sexual Technologies |journal=Socius: Sociological Research for a Dynamic World |language=en |volume=10 |doi=10.1177/23780231241259627 |issn=2378-0231 |doi-access=free}}] AI is also used for the production of non-consensual [[deepfake pornography]], raising significant ethical and legal concerns.[{{Cite journal |last=Mania |first=Karolina |date=2024-01-01 |title=Legal Protection of Revenge and Deepfake Porn Victims in the European Union: Findings From a Comparative Legal Study |url=https://journals.sagepub.com/doi/abs/10.1177/15248380221143772?journalCode=tvaa |journal=Trauma, Violence, & Abuse |language=en |volume=25 |issue=1 |pages=117–129 |doi=10.1177/15248380221143772 |pmid=36565267 |issn=1524-8380}}]
AI technologies have also been used to attempt to identify [[online gender-based violence]] and online [[sexual grooming]] of minors.[{{Cite journal |last1=Singh |first1=Suyesha |last2=Nambiar |first2=Vaishnavi |date=2024 |title=Role of Artificial Intelligence in the Prevention of Online Child Sexual Abuse: A Systematic Review of Literature |url=https://www.tandfonline.com/doi/full/10.1080/19361610.2024.2331885 |url-status=live |journal=Journal of Applied Security Research |language=en |volume=19 |issue=4 |pages=586–627 |doi=10.1080/19361610.2024.2331885 |issn=1936-1610 |archive-url=https://web.archive.org/web/20241209171923/https://www.tandfonline.com/doi/full/10.1080/19361610.2024.2331885 |archive-date=9 December 2024 |access-date=9 December 2024}}][{{Cite journal |last1=Razi |first1=Afsaneh |last2=Kim |first2=Seunghyun |last3=Alsoubai |first3=Ashwaq |last4=Stringhini |first4=Gianluca |last5=Solorio |first5=Thamar |last6=De Choudhury |first6=Munmun|author6-link=Munmun De Choudhury |last7=Wisniewski |first7=Pamela J. |date=2021-10-13 |title=A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection |url=https://dl.acm.org/doi/10.1145/3479609 |url-status=live |journal=Proceedings of the ACM on Human-Computer Interaction |language=en |volume=5 |issue=CSCW2 |pages=1–38 |doi=10.1145/3479609 |issn=2573-0142 |archive-url=https://web.archive.org/web/20241209171735/https://dl.acm.org/doi/10.1145/3479609 |archive-date=9 December 2024 |access-date=9 December 2024}}]
===Other industry-specific tasks===
There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes.[{{Cite journal |last1=Ransbotham |first1=Sam |last2=Kiron |first2=David |last3=Gerbert |first3=Philipp |last4=Reeves |first4=Martin |date=2017-09-06 |title=Reshaping Business With Artificial Intelligence |url=https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence |url-status=live |journal=MIT Sloan Management Review |archive-url=https://web.archive.org/web/20240213070751/https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence |archive-date=Feb 13, 2024}}] A few examples are [[energy storage]], medical diagnosis, military logistics, applications that predict the result of judicial decisions, [[foreign policy]], or supply chain management.
AI applications for evacuation and [[disaster]] management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions.[{{Citation |last1=Sun |first1=Yuran |title=8 – AI for large-scale evacuation modeling: promises and challenges |date=2024-01-01 |work=Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure |pages=185–204 |editor-last=Naser |editor-first=M. Z. |url=https://www.sciencedirect.com/science/article/pii/B9780128240731000149 |access-date=2024-06-28 |series=Woodhead Publishing Series in Civil and Structural Engineering |publisher=Woodhead Publishing |isbn=978-0-1282-4073-1 |last2=Zhao |first2=Xilei |last3=Lovreglio |first3=Ruggiero |last4=Kuligowski |first4=Erica |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121547/https://www.sciencedirect.com/science/article/abs/pii/B9780128240731000149 |url-status=live }}.][{{Cite journal |last1=Gomaa |first1=Islam |last2=Adelzadeh |first2=Masoud |last3=Gwynne |first3=Steven |last4=Spencer |first4=Bruce |last5=Ko |first5=Yoon |last6=Bénichou |first6=Noureddine |last7=Ma |first7=Chunyun |last8=Elsagan |first8=Nour |last9=Duong |first9=Dana |last10=Zalok |first10=Ehab |last11=Kinateder |first11=Max |date=2021-11-01 |title=A Framework for Intelligent Fire Detection and Evacuation System |url=https://doi.org/10.1007/s10694-021-01157-3 |journal=Fire Technology |volume=57 |issue=6 |pages=3179–3185 |doi=10.1007/s10694-021-01157-3 |issn=1572-8099 |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://link.springer.com/article/10.1007/s10694-021-01157-3 |url-status=live }}][{{Cite journal |last1=Zhao |first1=Xilei |last2=Lovreglio |first2=Ruggiero |last3=Nilsson |first3=Daniel |date=2020-05-01 |title=Modelling and interpreting pre-evacuation decision-making using machine learning |url=https://www.sciencedirect.com/science/article/pii/S0926580519313184 |journal=Automation in Construction |volume=113 |pages=103140 |doi=10.1016/j.autcon.2020.103140 |issn=0926-5805 |access-date=5 October 2024 |archive-date=19 May 2024 |archive-url=https://web.archive.org/web/20240519121548/https://www.sciencedirect.com/science/article/abs/pii/S0926580519313184 |url-status=live |hdl=10179/17315 |hdl-access=free }}]
In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct [[predictive analytics]], classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.
Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights." For example, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.
During the [[2024 Indian general election|2024 Indian elections]], US$50 million was spent on authorized AI-generated content, notably by creating [[deepfake]]s of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages.[{{Cite web |date=2024-06-12 |title=India's latest election embraced AI technology. Here are some ways it was used constructively |url=https://www.pbs.org/newshour/world/indias-latest-election-embraced-ai-technology-here-are-some-ways-it-was-used-constructively |access-date=2024-10-28 |website=PBS News |language=en-us |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917194950/https://www.pbs.org/newshour/world/indias-latest-election-embraced-ai-technology-here-are-some-ways-it-was-used-constructively |url-status=live }}]
==Ethics==
{{Main|Ethics of artificial intelligence}}
AI has potential benefits and potential risks.[{{Cite web |title=Ethics of Artificial Intelligence and Robotics |url=https://plato.stanford.edu/archives/fall2023/entries/ethics-ai/ |website=Stanford Encyclopedia of Philosophy Archive |date=30 April 2020 |last1=Müller |first1=Vincent C. |access-date=5 October 2024 |archive-date=5 October 2024 |archive-url=https://web.archive.org/web/20241005165650/https://plato.stanford.edu/archives/fall2023/entries/ethics-ai/ |url-status=live }}] AI may be able to advance science and find solutions for serious problems: [[Demis Hassabis]] of [[DeepMind]] hopes to "solve intelligence, and then use that to solve everything else".{{Sfnp|Simonite|2016}} However, as the use of AI has become widespread, several unintended consequences and risks have been identified.{{Sfnp|Russell|Norvig|2021|p=987}} In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning.{{Sfnp|Laskowski|2023}}