Artificial intelligence

"AI" redirects here. For other uses, seeAI (disambiguation),Artificial intelligence (disambiguation),andIntelligent agent.

Artificial intelligence(AI), in its broadest sense, isintelligenceexhibited bymachines,particularlycomputer systems.It is afield of researchincomputer sciencethat develops and studies methods andsoftwarethat enable machines toperceive their environmentand uselearningand intelligence to take actions that maximize their chances of achieving defined goals.Such machines may be called AIs.

Some high-profileapplications of AIinclude advancedweb search engines(e.g.,Google Search);recommendation systems(used byYouTube,Amazon,andNetflix); interactingvia human speech(e.g.,Google Assistant,Siri,andAlexa);autonomous vehicles(e.g.,Waymo);generativeandcreativetools (e.g.,ChatGPT,Apple Intelligence,andAI art); andsuperhumanplay and analysis instrategy games(e.g.,chessandGo).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'snot labeled AI anymore."

Alan Turingwas the first person to conduct substantial research in the field that he called machine intelligence.Artificial intelligence was founded as an academic discipline in 1956,by those now considered the founding fathers of AI,John McCarthy,Marvin Minksy,Nathaniel Rochester,andClaude Shannon.The field went through multiple cycles of optimism,followed by periods of disappointment and loss of funding, known asAI winter.Funding and interest vastly increased after 2012 whendeep learningsurpassed all previous AI techniques,and after 2017 with thetransformer architecture.This led to theAI boomof the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significantadvances in artificial intelligence.

The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increasedautomation,data-driven decision-making,and theintegration of AI systemsinto various economic sectors and areas of life,impacting job markets,healthcare,government,industry,education,propaganda,anddisinformation.This raises questions aboutthe long-term effects,ethical implications,andrisks of AI,prompting discussions aboutregulatory policiesto ensure thesafety and benefits of the technology.

The various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research includereasoning,knowledge representation,planning,learning,natural language processing,perception, and support forrobotics.General intelligence—the ability to complete any task performable by a human on an at least equal level—is among the field's long-term goals.

To reach these goals, AI researchers have adapted and integrated a wide range of techniques, includingsearchandmathematical optimization,formal logic,artificial neural networks,and methods based onstatistics,operations research,andeconomics.AI also draws uponpsychology,linguistics,philosophy,neuroscience,and other fields.

Goals

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

Reasoning and problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logicaldeductions.By the late 1980s and 1990s, methods were developed for dealing withuncertainor incomplete information, employing concepts fromprobabilityandeconomics.

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.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.Accurate and efficient reasoning is an unsolved problem.

Knowledge representation

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

Knowledge representationandknowledge engineeringallow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,scene interpretation,clinical decision support,knowledge discovery (mining "interesting" and actionable inferences from largedatabases),and other areas.

Aknowledge baseis a body of knowledge represented in a form that can be used by a program. Anontologyis the set of objects, relations, concepts, and properties used by a particular domain of knowledge.Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;situations, events, states, and time;causes and effects;knowledge about knowledge (what we know about what other people know);default reasoning(things that humans assume are true until they are told differently and will remain true even when other facts are changing);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);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).There is also the difficulty ofknowledge acquisition,the problem of obtaining knowledge for AI applications.

Planning and decision-making

An "agent" is anything that perceives and takes actions in the world. Arational agenthas goals or preferences and takes actions to make them happen.Inautomated planning,the agent has a specific goal.Inautomated 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":theutilityof 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.

Inclassical planning,the agent knows exactly what the effect of any action will be.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.

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., withinverse reinforcement learning), or the agent can seek information to improve its preferences.Information value theorycan be used to weigh the value of exploratory or experimental actions.The space of possible future actions and situations is typicallyintractablylarge, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.

AMarkov decision processhas atransition modelthat describes the probability that a particular action will change the state in a particular way and areward functionthat supplies the utility of each state and the cost of each action. Apolicyassociates a decision with each possible state. The policy could be calculated (e.g., byiteration), beheuristic,or it can be learned.

Game theorydescribes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.

Learning

Machine learningis the study of programs that can improve their performance on a given task automatically.It has been a part of AI from the beginning.

There are several kinds of machine learning.Unsupervised learninganalyzes a stream of data and finds patterns and makes predictions without any other guidance.Supervised learningrequires a human to label the input data first, and comes in two main varieties:classification(where the program must learn to predict what category the input belongs in) andregression(where the program must deduce a numeric function based on numeric input).

Inreinforcement learning,the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".Transfer learningis when the knowledge gained from one problem is applied to a new problem.Deep learningis a type of machine learning that runs inputs through biologically inspiredartificial neural networksfor all of these types of learning.

Computational learning theorycan assess learners bycomputational complexity,bysample complexity(how much data is required), or by other notions ofoptimization.

Natural language processing

Natural language processing(NLP)allows programs to read, write and communicate in human languages such asEnglish.Specific problems includespeech recognition,speech synthesis,machine translation,information extraction,information retrievalandquestion answering.

Early work, based onNoam Chomsky'sgenerative grammarandsemantic networks,had difficulty withword-sense disambiguationunless restricted to small domains called "micro-worlds"(due to the common sense knowledge problem).Margaret Mastermanbelieved that it was meaning and not grammar that was the key to understanding languages, and thatthesauriand not dictionaries should be the basis of computational language structure.

Modern deep learning techniques for NLP includeword embedding(representing words, typically asvectorsencoding their meaning),transformers(a deep learning architecture using anattentionmechanism),and others.In 2019,generative pre-trained transformer(or "GPT" ) language models began to generate coherent text,and by 2023 these models were able to get human-level scores on thebar exam,SATtest,GREtest, and many other real-world applications.

Perception

Machine perceptionis the ability to use input from sensors (such as cameras, microphones, wireless signals, activelidar,sonar, radar, andtactile sensors) to deduce aspects of the world.Computer visionis the ability to analyze visual input.

The field includesspeech recognition,image classification,facial recognition,object recognition,object tracking,androbotic perception.

Social intelligence

Kismet,a robot head which was made in the 1990s; a machine that can recognize and simulate emotions

Affective computingis an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate humanfeeling, emotion, and mood.For example, somevirtual assistantsare 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 facilitatehuman–computer interaction.

However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents.Moderate successes related to affective computing include textualsentiment analysisand, more recently,multimodal sentiment analysis,wherein AI classifies the affects displayed by a videotaped subject.

General intelligence

A machine withartificial general intelligenceshould be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.

Techniques

AI research uses a wide variety of techniques to accomplish the goals above.

Search and optimization

AI can solve many problems by intelligently searching through many possible solutions.There are two very different kinds of search used in AI:state space searchandlocal search.

State space search

State space searchsearches through a tree of possible states to try to find a goal state.For example,planningalgorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process calledmeans-ends analysis.

Simple exhaustive searchesare rarely sufficient for most real-world problems: thesearch space(the number of places to search) quickly grows toastronomical numbers.The result is a search that istoo slowor never completes."Heuristics"or" rules of thumb "can help prioritize choices that are more likely to reach a goal.

Adversarial searchis used forgame-playingprograms, such as chess or Go. It searches through atreeof possible moves and counter-moves, looking for a winning position.

Local search

Illustration ofgradient descentfor 3 different starting points; two parameters (represented by the plan coordinates) are adjusted in order to minimize theloss function(the height)

Local searchusesmathematical optimizationto find a solution to a problem. It begins with some form of guess and refines it incrementally.

Gradient descentis a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize aloss function.Variants ofgradient descentare commonly used to train neural networks.

Another type of local search isevolutionary computation,which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them,selectingonly the fittest to survive each generation.

Distributed search processes can coordinate viaswarm intelligencealgorithms. Two popular swarm algorithms used in search areparticle swarm optimization(inspired by birdflocking) andant colony optimization(inspired byant trails).

Logic

Formallogicis used forreasoningandknowledge representation. Formal logic comes in two main forms:propositional logic(which operates on statements that are true or false and useslogical connectivessuch as "and", "or", "not" and "implies" )andpredicate logic(which also operates on objects, predicates and relations and usesquantifierssuch as "EveryXis aY"and" There aresomeXs that areYs ").

Deductive reasoningin logic is the process ofprovinga new statement (conclusion) from other statements that are given and assumed to be true (thepremises).Proofs can be structured as prooftrees,in which nodes are labelled by sentences, and children nodes are connected to parent nodes byinference rules.

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 whoseleaf nodesare labelled by premises oraxioms.In the case ofHorn clauses,problem-solving search can be performed by reasoningforwardsfrom the premises orbackwardsfrom the problem.In the more general case of the clausal form offirst-order logic,resolutionis 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.

Inference in both Horn clause logic and first-order logic isundecidable,and thereforeintractable.However, backward reasoning with Horn clauses, which underpins computation in thelogic programminglanguageProlog,isTuring complete.Moreover, its efficiency is competitive with computation in othersymbolic programminglanguages.

Fuzzy logicassigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.

Non-monotonic logics,including logic programming withnegation as failure,are designed to handledefault reasoning. Other specialized versions of logic have been developed to describe many complex domains.

Probabilistic methods for uncertain reasoning

A simpleBayesian network,with the associatedconditional 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 fromprobabilitytheory and economics.Precise mathematical tools have been developed that analyze how an agent can make choices and plan, usingdecision theory,decision analysis,andinformation value theory.These tools include models such asMarkov decision processes,dynamicdecision networks,game theoryandmechanism design.

Bayesian networksare a tool that can be used forreasoning(using theBayesian inferencealgorithm),learning(using theexpectation–maximization algorithm),planning(usingdecision networks)andperception(usingdynamic Bayesian networks).

Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helpingperceptionsystems analyze processes that occur over time (e.g.,hidden Markov modelsorKalman filters).

Expectation–maximizationclusteringofOld Faithfuleruption 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.Classifiersare functions that usepattern matchingto determine the closest match. They can be fine-tuned based on chosen examples usingsupervised learning.Each pattern (also called an "observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as adata set.When a new observation is received, that observation is classified based on previous experience.

There are many kinds of classifiers in use. Thedecision treeis the simplest and most widely used symbolic machine learning algorithm.K-nearest neighboralgorithm was the most widely used analogical AI until the mid-1990s, andKernel methodssuch as thesupport vector machine(SVM) displaced k-nearest neighbor in the 1990s. Thenaive Bayes classifieris reportedly the "most widely used learner"at Google, due in part to its scalability. Neural networksare also used as classifiers.

Artificial neural networks

A neural network is an interconnected group of nodes, akin to the vast network ofneuronsin thehuman brain

An artificial neural network is based on a collection of nodes also known asartificial neurons,which loosely model theneuronsin 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 theweightcrosses 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.

Learning algorithms for neural networks uselocal searchto choose the weights that will get the right output for each input during training. The most common training technique is thebackpropagationalgorithm. Neural networks learn to model complex relationships between inputs and outputs andfind patternsin data. In theory, a neural network can learn any function.

Infeedforward neural networksthe signal passes in only one direction.Recurrent neural networksfeed the output signal back into the input, which allows short-term memories of previous input events.Long short term memoryis the most successful network architecture for recurrent networks. Perceptrons use only a single layer of neurons, deep learninguses multiple layers. Convolutional neural networksstrengthen the connection between neurons that are "close" to each other—this is especially important inimage processing,where a local set of neurons mustidentify an "edge"before the network can identify an object.

Deep learning

Deep learning 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, inimage processing,lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.

Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, includingcomputer vision,speech recognition,natural language processing,image classification,and others. The reason that deep learning performs so well in so many applications is not known as of 2023. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks andbackpropagationhad been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching toGPUs) and the availability of vast amounts of training data, especially the giantcurated datasetsused for benchmark testing, such asImageNet.

GPT

Generative pre-trained transformers(GPT) arelarge language modelsthat are based on the semantic relationships between words in sentences (natural language processing). Text-based GPT models are pre-trained on a largecorpus of textwhich can be from the internet. The pre-training consists in predicting the nexttoken(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 calledreinforcement learning from human feedback(RLHF). Current GPT models are still prone to generating falsehoods called "hallucinations",although this can be reduced with RLHF and quality data. They are used inchatbots,which allow you to ask a question or request a task in simple text.

Current models and services include:Gemini(formerly Bard),ChatGPT,Grok,Claude,CopilotandLLaMA.MultimodalGPT models can process different types of data (modalities) such as images, videos, sound, and text.

Specialized hardware and software

Main articles:Programming languages for artificial intelligenceandHardware for artificial intelligence

In the late 2010s,graphics processing units(GPUs) that were increasingly designed with AI-specific enhancements and used with specializedTensorFlowsoftware had replaced previously usedcentral processing unit(CPUs) as the dominant means for large-scale (commercial and academic)machine learningmodels' training.Specializedprogramming languagessuch asPrologwere used in early AI research,butgeneral-purpose programming languageslikePythonhave become predominant.

Applications

Main article:Applications of artificial intelligence

AI and machine learning technology is used in most of the essential applications of the 2020s, including:search engines(such asGoogle Search),targeting online advertisements,recommendation systems(offered byNetflix,YouTubeorAmazon), drivinginternet traffic,targeted advertising(AdSense,Facebook),virtual assistants(such asSiriorAlexa),autonomous vehicles(includingdrones,ADASandself-driving cars),automatic language translation(Microsoft Translator,Google Translate),facial recognition(Apple'sFace IDorMicrosoft'sDeepFaceandGoogle'sFaceNet) andimage labeling(used byFacebook,Apple'siPhotoandTikTok). The deployment of AI may be overseen by aChief automation officer(CAO).

Health and medicine

Main article:Artificial intelligence in healthcare

The application of AI inmedicineandmedical researchhas the potential to increase patient care and quality of life.Through the lens of theHippocratic 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 integratingbig data.This is particularly important fororganoidandtissue engineeringdevelopment which usemicroscopyimaging as a key technique in fabrication.It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.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 3Dstructure of a protein.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.In 2024, researchers used machine learning to accelerate the search forParkinson's diseasedrug treatments. Their aim was to identify compounds that block the clumping, or aggregation, ofalpha-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.

Games

Main article:Game artificial intelligence

Game playingprograms have been used since the 1950s to demonstrate and test AI's most advanced techniques.Deep Bluebecame the first computer chess-playing system to beat a reigning world chess champion,Garry Kasparov,on 11 May 1997.In 2011, in aJeopardy!quiz showexhibition match,IBM'squestion answering system,Watson,defeated the two greatestJeopardy!champions,Brad RutterandKen Jennings,by a significant margin.In March 2016,AlphaGowon 4 out of 5 games ofGoin a match with Go championLee Sedol,becoming the firstcomputer Go-playing system to beat a professional Go player withouthandicaps.Then in 2017 itdefeated Ke Jie,who was the best Go player in the world.Other programs handleimperfect-informationgames, such as thepoker-playing programPluribus.DeepMinddeveloped increasingly generalisticreinforcement learningmodels, such as withMuZero,which could be trained to play chess, Go, orAtarigames.In 2019, DeepMind's AlphaStar achieved grandmaster level inStarCraft II,a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map.In 2021, an AI agent competed in a PlayStationGran Turismocompetition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.

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.

World Pensionsexperts like Nicolas Firzli insist it may be too early to see the emergence of highly innovative AI-informed financial products and services: "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I’m not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."

Military

Main article:Military artificial intelligence

Various countries are deploying AI military applications.The main applications enhancecommand and control,communications, sensors, integration and interoperability.Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous andautonomous vehicles.AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions,target acquisition,coordination and deconfliction of distributedJoint Firesbetween networked combat vehicles involving manned and unmanned teams.AI was incorporated into military operations in Iraq and Syria.

In November 2023, US Vice PresidentKamala Harrisdisclosed a declaration signed by 31 nations to set guardrails for the military use of AI. The commitments include using legal reviews to ensure the compliance of military AI with international laws, and being cautious and transparent in the development of this technology.

Generative AI

Main article:Generative artificial intelligence

Vincent van Gogh in watercolour created by generative AI software

In the early 2020s,generative AIgained widespread prominence. In March 2023, 58% of U.S. adults had heard aboutChatGPTand 14% had tried it.The increasing realism and ease-of-use of AI-basedtext-to-imagegenerators such asMidjourney,DALL-E,andStable Diffusionsparked a trend ofviralAI-generated photos. Widespread attention was gained by a fake photo ofPope Franciswearing a white puffer coat, the fictional arrest ofDonald Trump,and a hoax of an attack on thePentagon,as well as the usage in professional creative arts.

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.A few examples areenergy storage,medical diagnosis, military logistics, applications that predict the result of judicial decisions,foreign policy,or supply chain management.

AI applications for evacuation anddisastermanagment 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.

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

Main article:Ethics of artificial intelligence

AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems:Demis HassabisofDeep Mindhopes to "solve intelligence, and then use that to solve everything else".However, as the use of AI has become widespread, several unintended consequences and risks have been identified.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.

Risks and harm

Privacy and copyright

Further information:Information privacyandArtificial intelligence and copyright

Machine-learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns aboutprivacy,surveillanceandcopyright.

Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio. For example, in order to buildspeech recognitionalgorithms,Amazonhas recorded millions of private conversations and allowedtemporary workersto listen to and transcribe some of them.Opinions about this widespreadsurveillancerange from those who see it as anecessary evilto those for whom it is clearlyunethicaland a violation of theright to privacy.

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 asdata aggregation,de-identificationanddifferential privacy.Since 2016, some privacy experts, such asCynthia Dwork,have begun to view privacy in terms offairness.Brian Christianwrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."

Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use".Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include" the purpose and character of the use of the copyrighted work "and" the effect upon the potential market for the copyrighted work ".Website owners who do not wish to have their content scraped can indicate it in a "robots.txt"file.In 2023, leading authors (includingJohn GrishamandJonathan Franzen) sued AI companies for using their work to train generative AI.Another discussed approach is to envision a separatesui generissystem of protection for creations generated by AI to ensure fair attribution and compensation for human authors.

Misinformation

See also:YouTube § Moderation and offensive content

YouTube,Facebookand others userecommender systemsto guide users to more content. These AI programs were given the goal ofmaximizinguser engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choosemisinformation,conspiracy theories,and extremepartisancontent, 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 intofilter bubbleswhere they received multiple versions of the same misinformation.This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government.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 AIbegan 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.AI pioneerGeoffrey Hintonexpressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.

Algorithmic bias and fairness

Main articles:Algorithmic biasandFairness (machine learning)

Machine learning applications will be biased if they learn from biased data.The developers may not be aware that the bias exists. Bias can be introduced by the waytraining datais selected and by the way a model is deployed.If a biased algorithm is used to make decisions that can seriouslyharmpeople (as it can inmedicine,finance,recruitment,housingorpolicing) then the algorithm may causediscrimination. Fairnessin 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.

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,a problem called "sample size disparity".Google "fixed" this problem by preventing the system from labellinganythingas 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.

COMPASis a commercial program widely used byU.S. courtsto assess the likelihood of adefendantbecoming arecidivist. In 2016,Julia AngwinatProPublicadiscovered 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.In 2017, several researchersshowed 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.

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". Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."

Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions asrecommendations,some of these "recommendations" will likely be racist.Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will bebetterthan the past. It is descriptive rather than prescriptive.

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.

At its 2022Conference on Fairness, Accountability, and Transparency(ACM FAccT 2022), theAssociation for Computing Machinery,in Seoul, South Korea, presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe, and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.

Lack of transparency

See also:Explainable AI,Algorithmic transparency,andRight to explanation

Many AI systems are so complex that their designers cannot explain how they reach their decisions.Particularly withdeep neural networks,in which there are a large amount of non-linearrelationships between inputs and outputs. But some popular explainability techniques exist.

It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with aruleras "cancerous", because pictures of malignancies typically include a ruler to show the scale.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.

People who have been harmed by an algorithm's decision have a right to an explanation.Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union'sGeneral Data Protection Regulationin 2016 included an explicit statement that this right exists.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.

DARPAestablished theXAI( "Explainable Artificial Intelligence" ) program in 2014 to try and solve these problems.

There are several possible solutions to the transparency problem. SHAP tried to solve the transparency problems by visualising the contribution of each feature to the output.LIME can locally approximate a model with a simpler, interpretable model.Multitask learningprovides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned.Deconvolution,DeepDreamand othergenerativemethods 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.

Bad actors and weaponized AI

Main articles:Lethal autonomous weapon,Artificial intelligence arms race,andAI safety

Artificial intelligence provides a number of tools that are useful tobad actors,such asauthoritarian governments,terrorists,criminalsorrogue states.

A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision.Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentiallyweapons of mass destruction.Even when used in conventional warfare, it is unlikely that they will be unable to reliably choose targets and could potentiallykill an innocent person.In 2014, 30 nations (including China) supported a ban on autonomous weapons under theUnited Nations'Convention on Certain Conventional Weapons,however theUnited Statesand others disagreed.By 2015, over fifty countries were reported to be researching battlefield robots.

AI tools make it easier forauthoritarian governmentsto efficiently control their citizens in several ways.Faceandvoice recognitionallow widespreadsurveillance.Machine learning,operating this data, canclassifypotential enemies of the state and prevent them from hiding.Recommendation systemscan precisely targetpropagandaandmisinformationfor maximum effect.Deepfakesandgenerative AIaid in producing misinformation. Advanced AI can make authoritariancentralized decision makingmore competitive than liberal and decentralized systems such asmarkets.It lowers the cost and difficulty ofdigital warfareandadvanced spyware.All these technologies have been available since 2020 or earlier—AIfacial recognition systemsare already being used formass surveillancein China.

There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.

Reliance on industry giants

Training AI systems requires an enormous amount of computing power. Usually onlyBig Techcompanies have the financial resources to make such investments. Smaller startups such asCohereandOpenAIend up buying access todata centersfromGoogleandMicrosoftrespectively.

Technological unemployment

Main articles:Workplace impact of artificial intelligenceandTechnological unemployment

Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-termunemployment,but they generally agree that it could be a net benefit ifproductivitygains areredistributed.Risk estimates vary; for example, in the 2010s, Michael Osborne andCarl Benedikt Freyestimated 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".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.In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.

Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence;The Economiststated 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".Jobs at extreme risk range fromparalegalsto fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.

From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward byJoseph 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.

Existential risk

Main article: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 physicistStephen Hawkingstated, "spell the end of the human race".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.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. PhilosopherNick Bostromargued that if one givesalmost anygoal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of apaperclip factory manager).Stuart Russellgives 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."In order to be safe for humanity, asuperintelligencewould have to be genuinelyalignedwith humanity's morality and values so that it is "fundamentally on our side".

Second,Yuval Noah Harariargues 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 likeideologies,law,government,moneyand theeconomyare made oflanguage;they exist because there are stories that billions of people believe. The current prevalence ofmisinformationsuggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.

The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.Personalities such asStephen Hawking,Bill Gates,andElon Musk,as well as AI pioneers such asGeoffrey Hinton,Yoshua Bengio,Demis Hassabis,andSam Altman,have expressed concerns about existential risk from AI.

In 2023, many leading AI experts issuedthe joint statementthat "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".

Other researchers, however, spoke in favor of a less dystopian view. AI pioneerJuergen Schmidhuberdid not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier."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."Andrew Ngalso argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests."Yann LeCun"scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."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.However, after 2016, the study of current and future risks and possible solutions became a serious area of research.

Ethical machines and alignment

Main articles:Machine ethics,AI safety,Friendly artificial intelligence,Artificial moral agents,andHuman 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.

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. The field of machine ethics is also called computational morality, and was founded at anAAAIsymposium in 2005.

Other approaches includeWendell Wallach's "artificial moral agents"andStuart J. Russell'sthree principlesfor developing provably beneficial machines.

Open source

Active organizations in the AI open-source community includeHugging Face,Google,EleutherAIandMeta.Various AI models, such asLlama 2,MistralorStable Diffusion,have been made open-weight,meaning that their architecture and trained parameters (the "weights" ) are publicly available. Open-weight models can be freelyfine-tuned,which allows companies to specialize them with their own data and for their own use-case.Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitatebioterrorism), and that once released on the Internet, they can't be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.

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 theAlan Turing Institutetests projects in four main areas:

• Respectthe dignity of individual people
• Connectwith other people sincerely, openly, and inclusively
• Carefor the wellbeing of everyone
• Protectsocial values, justice, and the public interest

Other developments in ethical frameworks include those decided upon during theAsilomar Conference,the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others;however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks.

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.

The AI Safety Institute in the UK has released a testing toolset called 'Inspect' for AI safety evaluations available under a MIT open-source licence which is freely available on GitHub and can be improved with third-party packages. It can be used to evaluate AI models in a range of areas including core knowledge, ability to reason, and autonomous capabilities.

Voicing concerns about issues including theexistential risk from artificial general intelligence,in May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to "freely speak out about the risks of A.I."He stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of A.I.

Regulation

Main articles:Regulation of artificial intelligence,Regulation of algorithms,andAI safety

The first globalAI Safety Summitwas 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.The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.According to AI Index atStanford,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.Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia.TheGlobal Partnership on Artificial Intelligencewas 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.Henry Kissinger,Eric Schmidt,andDaniel Huttenlocherpublished a joint statement in November 2021 calling for a government commission to regulate AI.In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.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.

In a 2022Ipsossurvey, 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".A 2023Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.In a 2023Fox Newspoll, 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".

In November 2023, the first globalAI Safety Summitwas held inBletchley Parkin the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks.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.In May 2024 at theAI Seoul Summit,16 global AI tech companies agreed to safety commitments on the development of AI.

History

Main article:History of artificial intelligence

For a chronological guide, seeTimeline of artificial intelligence.

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly toAlan Turing'stheory of computation,which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning.This, along with concurrent discoveries incybernetics,information theoryandneurobiology,led researchers to consider the possibility of building an "electronic brain". They developed several areas of research that would become part of AI, such asMcCullouchandPittsdesign for "artificial neurons" in 1943,and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced theTuring testand showed that "machine intelligence" was plausible.

The field of AI research was founded ata workshopatDartmouth Collegein 1956.The attendees became the leaders of AI research in the 1960s.They and their students produced programs that the press described as "astonishing":computers were learningcheckersstrategies, solving word problems in algebra, provinglogical theoremsand speaking English.Artificial intelligence laboratories were set up at a number of British and U.S. Universities in the latter 1950s and early 1960s.

Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine withgeneral intelligenceand considered this the goal of their field.Herbert Simonpredicted, "machines will be capable, within twenty years, of doing any work a man can do".Marvin Minskyagreed, writing, "within a generation... the problem of creating 'artificial intelligence' will substantially be solved".They had, however, underestimated the difficulty of the problem.In 1974, both the U.S. and British governments cut off exploratory research in response to thecriticismofSir James Lighthilland ongoing pressure from the U.S. Congress tofund more productive projects.Minsky's andPapert's bookPerceptronswas understood as proving thatartificial neural networkswould never be useful for solving real-world tasks, thus discrediting the approach altogether.The "AI winter",a period when obtaining funding for AI projects was difficult, followed.

In the early 1980s, AI research was revived by the commercial success ofexpert systems,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'sfifth generation computerproject inspired the U.S. and British governments to restore funding foracademic research.However, beginning with the collapse of theLisp Machinemarket in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.

Up to this point, most of AI's funding had gone to projects that used high-levelsymbolsto representmental objectslike plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especiallyperception,robotics,learningandpattern recognition,and began to look into "sub-symbolic" approaches.Rodney Brooksrejected "representation" in general and focussed directly on engineering machines that move and survive.Judea Pearl,Lofti Zadehand others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.But the most important development was the revival of "connectionism",including neural network research, byGeoffrey Hintonand others.In 1990,Yann LeCunsuccessfully showed thatconvolutional neural networkscan recognize handwritten digits, the first of many successful applications of neural networks.

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"and" formal "focus allowed researchers to produce verifiable results and collaborate with other fields (such asstatistics,economicsandmathematics).By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence". However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield ofartificial general intelligence(or "AGI" ), which had several well-funded institutions by the 2010s.

Deep learningbegan to dominate industry benchmarks in 2012 and was adopted throughout the field. For many specific tasks, other methods were abandoned. Deep learning's success was based on both hardware improvements (faster computers,graphics processing units,cloud computing) and access tolarge amounts of data(including curated datasets,such asImageNet). Deep learning's success led to an enormous increase in interest and funding in AI.The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.

In 2016, issues offairnessand 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. Thealignment problembecame a serious field of academic study.

In the late teens and early 2020s,AGIcompanies began to deliver programs that created enormous interest. In 2015,AlphaGo,developed byDeepMind,beat the world championGo player.The program was taught only the rules of the game and developed strategy by itself.GPT-3is alarge language modelthat was released in 2020 byOpenAIand is capable of generating high-quality human-like text.These programs, and others, inspired an aggressiveAI boom,where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI". About 800,000 "AI" -related U.S. job openings existed in 2022.

Philosophy

Main article:Philosophy of artificial intelligence

Defining artificial intelligence

Main articles:Turing test,Intelligent agent,Dartmouth workshop,andSynthetic intelligence

Alan Turingwrote in 1950 "I propose to consider the question 'can machines think'?"He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".He devised the Turing test, which measures the ability of a machine to simulate human conversation.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 thatwe can not determine these things about other peoplebut "it is usual to have a polite convention that everyone thinks."

RussellandNorvigagree with Turing that intelligence must be defined in terms of external behavior, not internal structure.However, they are critical that the test requires the machine to imitate humans. "Aeronautical engineeringtexts, "they wrote," do not define the goal of their field as making 'machines that fly so exactly likepigeonsthat they can fool other pigeons.'"AI founderJohn McCarthyagreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".

McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world".Another AI founder,Marvin Minskysimilarly describes it as "the ability to solve hard problems".The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals.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,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 orparadigmhas guided AI research for most of its history.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 mostlysub-symbolic,softandnarrow.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")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 thephysical symbol systems hypothesis:"A physical symbol system has the necessary and sufficient means of general intelligent action."

However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning.Moravec's paradoxis the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.PhilosopherHubert Dreyfushadarguedsince 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.Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.

The issue is not resolved:sub-symbolicreasoning can make many of the same inscrutable mistakes that human intuition does, such asalgorithmic bias.Critics such asNoam Chomskyargue continuing research into symbolic AI will still be necessary to attain general intelligence,in part because sub-symbolic AI is a move away fromexplainable AI:it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field ofneuro-symbolic artificial intelligenceattempts to bridge the two approaches.

Neat vs. scruffy

Main article:Neats and scruffies

"Neats" hope that intelligent behavior is described using simple, elegant principles (such aslogic,optimization,orneural 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,but eventually was seen as irrelevant. Modern AI has elements of both.

Soft vs. hard computing

Main article:Soft computing

Finding a provably correct or optimal solution isintractablefor many important problems.Soft computing is a set of techniques, includinggenetic algorithms,fuzzy logicand 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 articles:Weak artificial intelligenceandArtificial general intelligence

AI researchers are divided as to whether to pursue the goals of artificial general intelligence andsuperintelligencedirectly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals.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 articles:Philosophy of artificial intelligenceandArtificial consciousness

Thephilosophy of minddoes not know whether a machine can have amind,consciousnessandmental 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.RussellandNorvigadd 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."However, the question has become central to the philosophy of mind. It is also typically the central question at issue inartificial intelligence in fiction.

Consciousness

Main articles:Hard problem of consciousnessandTheory of mind

David Chalmersidentified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how thisfeelsor why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). While humaninformation processingis easy to explain, humansubjective experienceis 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 toknow what red looks like.

Computationalism and functionalism

Main articles:Computational theory of mind,Functionalism (philosophy of mind),andChinese room

Computationalism is the position in thephilosophy of mindthat 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 themind–body problem.This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophersJerry FodorandHilary Putnam.

PhilosopherJohn Searlecharacterized this position as "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. "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.

AI welfare and rights

It is difficult or impossible to reliably evaluate whether an advancedAI is sentient(has the ability to feel), and if so, to what degree.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.Sapience(a set of capacities related to high intelligence, such as discernment orself-awareness) may provide another moral basis for AI rights.Robot rightsare also sometimes proposed as a practical way to integrate autonomous agents into society.

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.Critics argued in 2018 that granting rights to AI systems would downplay the importance ofhuman 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.

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 amoral blind spotanalogous toslaveryorfactory farming,which could lead tolarge-scale sufferingif sentient AI is created and carelessly exploited.

Future

Superintelligence and the singularity

Asuperintelligenceis a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind.

If research intoartificial general intelligenceproduced sufficiently intelligent software, it might be able toreprogram and improve itself.The improved software would be even better at improving itself, leading to whatI. J. Goodcalled an "intelligence explosion"andVernor Vingecalled a "singularity".

However, technologies cannot improve exponentially indefinitely, and typically follow anS-shaped curve,slowing when they reach the physical limits of what the technology can do.

Transhumanism

Robot designerHans Moravec,cyberneticistKevin Warwick,and inventorRay Kurzweilhave predicted that humans and machines will merge in the future intocyborgsthat are more capable and powerful than either. This idea, called transhumanism, has roots inAldous HuxleyandRobert Ettinger.

Edward Fredkinargues that "artificial intelligence is the next stage in evolution", an idea first proposed bySamuel Butler's "Darwin among the Machines"as far back as 1863, and expanded upon byGeorge Dysonin his 1998 bookDarwin Among the Machines: The Evolution of Global Intelligence.

In fiction

Main article:Artificial intelligence in fiction

The word "robot" itself was coined byKarel Čapekin his 1921 playR.U.R.,the title standing for "Rossum's Universal Robots"

Thought-capable artificial beings have appeared as storytelling devices since antiquity,and have been a persistent theme inscience fiction.

A commontropein these works began withMary Shelley'sFrankenstein,where a human creation becomes a threat to its masters. This includes such works asArthur C. Clarke'sandStanley Kubrick's2001: A Space Odyssey(both 1968), withHAL 9000,the murderous computer in charge of theDiscovery Onespaceship, as well asThe Terminator(1984) andThe Matrix(1999). In contrast, the rare loyal robots such as Gort fromThe Day the Earth Stood Still(1951) and Bishop fromAliens(1986) are less prominent in popular culture.

Isaac Asimovintroduced theThree Laws of Roboticsin many stories, most notably with the "Multivac"super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics;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.

Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that havethe ability to feel,and thus to suffer. This appears inKarel Čapek'sR.U.R.,the filmsA.I. Artificial IntelligenceandEx Machina,as well as the novelDo Androids Dream of Electric Sheep?,byPhilip K. Dick.Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.

See also

• Artificial intelligence detection software– Software to detect AI-generated contentPages displaying short descriptions of redirect targets
• Behavior selection algorithm– Algorithm that selects actions for intelligent agents
• Business process automation– Technology-enabled automation of complex business processes
• Case-based reasoning– Process of solving new problems based on the solutions of similar past problems
• Computational intelligence– Ability of a computer to learn a specific task from data or experimental observation
• Digital immortality– Hypothetical concept of storing a personality in digital form
• Emergent algorithm– Algorithm exhibiting emergent behavior
• Female gendering of AI technologies– Gender biases in digital technologyPages displaying short descriptions of redirect targets
• Glossary of artificial intelligence– List of definitions of terms and concepts commonly used in the study of artificial intelligence
• Intelligence amplification– Use of information technology to augment human intelligence
• Mind uploading– Hypothetical process of digitally emulating a brain
• Robotic process automation– Form of business process automation technology
• Weak artificial intelligence– Form of artificial intelligence
• Wetware computer– Computer composed of organic material

References

AI textbooks

The two most widely used textbooks in 2023. (See theOpen Syllabus).

• Russell, Stuart J.;Norvig, Peter.(2021).Artificial Intelligence: A Modern Approach(4th ed.). Hoboken: Pearson.ISBN978-0134610993.LCCN20190474.
• Rich, Elaine;Knight, Kevin; Nair, Shivashankar B (2010).Artificial Intelligence(3rd ed.). New Delhi: Tata McGraw Hill India.ISBN978-0070087705.

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

• Luger, George;Stubblefield, William(2004).Artificial Intelligence: Structures and Strategies for Complex Problem Solving(5th ed.). Benjamin/Cummings.ISBN978-0-8053-4780-7.Archivedfrom the original on 26 July 2020.Retrieved17 December2019.
• Nilsson, Nils(1998).Artificial Intelligence: A New Synthesis.Morgan Kaufmann.ISBN978-1-55860-467-4.Archivedfrom the original on 26 July 2020.Retrieved18 November2019.
• Russell, Stuart J.;Norvig, Peter(2003),Artificial Intelligence: A Modern Approach(2nd ed.), Upper Saddle River, New Jersey: Prentice Hall,ISBN0-13-790395-2.
• Poole, David;Mackworth, Alan;Goebel, Randy(1998).Computational Intelligence: A Logical Approach.New York: Oxford University Press.ISBN978-0-19-510270-3.Archivedfrom the original on 26 July 2020.Retrieved22 August2020.

Later editions.

• Poole, David;Mackworth, Alan(2017).Artificial Intelligence: Foundations of Computational Agents(2nd ed.). Cambridge University Press.ISBN978-1-107-19539-4.Archivedfrom the original on 7 December 2017.Retrieved6 December2017.

History of AI

• Crevier, Daniel(1993).AI: The Tumultuous Search for Artificial Intelligence.New York, NY: BasicBooks.ISBN0-465-02997-3..
• McCorduck, Pamela(2004),Machines Who Think(2nd ed.), Natick, MA: A. K. Peters, Ltd.,ISBN1-56881-205-1.
• Newquist, H. P.(1994).The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think.New York: Macmillan/SAMS.ISBN978-0-672-30412-5.

Other sources

• AI & ML in Fusion
• AI & ML in Fusion, video lectureArchived2 July 2023 at theWayback Machine
• "AlphaGo – Google DeepMind".Archivedfrom the original on 10 March 2016.
• Alter, Alexandra; Harris, Elizabeth A. (20 September 2023),"Franzen, Grisham and Other Prominent Authors Sue OpenAI",The New York Times
• Altman, Sam;Brockman, Greg;Sutskever, Ilya(22 May 2023)."Governance of Superintelligence".openai.com.Archivedfrom the original on 27 May 2023.Retrieved27 May2023.
• Anderson, Susan Leigh (2008). "Asimov's" three laws of robotics "and machine metaethics".AI & Society.22(4): 477–493.doi:10.1007/s00146-007-0094-5.S2CID1809459.
• Anderson, Michael; Anderson, Susan Leigh (2011).Machine Ethics.Cambridge University Press.
• Arntz, Melanie; Gregory, Terry; Zierahn, Ulrich (2016), "The risk of automation for jobs in OECD countries: A comparative analysis",OECD Social, Employment, and Migration Working Papers 189
• Asada, M.; Hosoda, K.; Kuniyoshi, Y.; Ishiguro, H.; Inui, T.; Yoshikawa, Y.; Ogino, M.; Yoshida, C. (2009). "Cognitive developmental robotics: a survey".IEEE Transactions on Autonomous Mental Development.1(1): 12–34.doi:10.1109/tamd.2009.2021702.S2CID10168773.
• "Ask the AI experts: What's driving today's progress in AI?".McKinsey & Company.Archivedfrom the original on 13 April 2018.Retrieved13 April2018.
• Barfield, Woodrow; Pagallo, Ugo (2018).Research handbook on the law of artificial intelligence.Cheltenham, UK: Edward Elgar Publishing.ISBN978-1-78643-904-8.OCLC1039480085.
• Beal, J.;Winston, Patrick(2009), "The New Frontier of Human-Level Artificial Intelligence",IEEE Intelligent Systems,24:21–24,doi:10.1109/MIS.2009.75,hdl:1721.1/52357,S2CID32437713
• Berdahl, Carl Thomas; Baker, Lawrence; Mann, Sean; Osoba, Osonde; Girosi, Federico (7 February 2023)."Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review".JMIR AI.2:e42936.doi:10.2196/42936.ISSN2817-1705.PMC11041459.S2CID256681439.
• Berlinski, David(2000).The Advent of the Algorithm.Harcourt Books.ISBN978-0-15-601391-8.OCLC46890682.Archivedfrom the original on 26 July 2020.Retrieved22 August2020.
• Berryhill, Jamie; Heang, Kévin Kok; Clogher, Rob; McBride, Keegan (2019).Hello, World: Artificial Intelligence and its Use in the Public Sector(PDF).Paris: OECD Observatory of Public Sector Innovation.Archived(PDF)from the original on 20 December 2019.Retrieved9 August2020.
• Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies".MM '06 Proceedings of the 14th ACM international conference on Multimedia.14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682.
• Bostrom, Nick(2014).Superintelligence: Paths, Dangers, Strategies.Oxford University Press.
• Bostrom, Nick (2015)."What happens when our computers get smarter than we are?".TED (conference).Archivedfrom the original on 25 July 2020.Retrieved30 January2020.
• Brooks, Rodney (10 November 2014)."artificial intelligence is a tool, not a threat".Archived fromthe originalon 12 November 2014.
• Brooks, Rodney(1990)."Elephants Don't Play Chess"(PDF).Robotics and Autonomous Systems.6(1–2): 3–15.CiteSeerX10.1.1.588.7539.doi:10.1016/S0921-8890(05)80025-9.Archived(PDF)from the original on 9 August 2007.
• Buiten, Miriam C (2019)."Towards Intelligent Regulation of Artificial Intelligence".European Journal of Risk Regulation.10(1): 41–59.doi:10.1017/err.2019.8.ISSN1867-299X.
• Bushwick, Sophie (16 March 2023),"What the New GPT-4 AI Can Do",Scientific American
• Butler, Samuel(13 June 1863)."Darwin among the Machines".Letters to the Editor.The Press.Christchurch, New Zealand.Archivedfrom the original on 19 September 2008.Retrieved16 October2014– via Victoria University of Wellington.
• Buttazzo, G. (July 2001). "Artificial consciousness: Utopia or real possibility?".Computer.34(7): 24–30.doi:10.1109/2.933500.
• Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]".IEEE Computational Intelligence Magazine.9(2): 48–57.doi:10.1109/MCI.2014.2307227.S2CID206451986.
• Cellan-Jones, Rory (2 December 2014)."Stephen Hawking warns artificial intelligence could end mankind".BBC News.Archivedfrom the original on 30 October 2015.Retrieved30 October2015.
• Chalmers, David(1995)."Facing up to the problem of consciousness".Journal of Consciousness Studies.2(3): 200–219.Archivedfrom the original on 8 March 2005.Retrieved11 October2018.
• Challa, Subhash; Moreland, Mark R.; Mušicki, Darko; Evans, Robin J. (2011).Fundamentals of Object Tracking.Cambridge University Press.doi:10.1017/CBO9780511975837.
• Christian, Brian(2020).The Alignment Problem:Machine learning and human values.W. W. Norton & Company.ISBN978-0-393-86833-3.OCLC1233266753.
• Ciresan, D.; Meier, U.; Schmidhuber, J. (2012). "Multi-column deep neural networks for image classification".2012 IEEE Conference on Computer Vision and Pattern Recognition.pp. 3642–3649.arXiv:1202.2745.doi:10.1109/cvpr.2012.6248110.ISBN978-1-4673-1228-8.S2CID2161592.
• Clark, Jack (2015b)."Why 2015 Was a Breakthrough Year in Artificial Intelligence".Bloomberg.com.Archivedfrom the original on 23 November 2016.Retrieved23 November2016.
• CNA (12 January 2019)."Commentary: Bad news. Artificial intelligence is biased".CNA.Archivedfrom the original on 12 January 2019.Retrieved19 June2020.
• Cybenko, G.(1988). Continuous valued neural networks with two hidden layers are sufficient (Report). Department of Computer Science, Tufts University.
• Deng, L.; Yu, D. (2014)."Deep Learning: Methods and Applications"(PDF).Foundations and Trends in Signal Processing.7(3–4): 1–199.doi:10.1561/2000000039.Archived(PDF)from the original on 14 March 2016.Retrieved18 October2014.
• Dennett, Daniel(1991).Consciousness Explained.The Penguin Press.ISBN978-0-7139-9037-9.
• DiFeliciantonio, Chase (3 April 2023)."AI has already changed the world. This report shows how".San Francisco Chronicle.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.
• Dickson, Ben (2 May 2022)."Machine learning: What is the transformer architecture?".TechTalks.Retrieved22 November2023.
• Dockrill, Peter (27 June 2022),"Robots With Flawed AI Make Sexist And Racist Decisions, Experiment Shows",Science Alert,archived fromthe originalon 27 June 2022
• Domingos, Pedro(2015).The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.Basic Books.ISBN978-0465065707.
• Dreyfus, Hubert(1972).What Computers Can't Do.New York: MIT Press.ISBN978-0-06-011082-6.
• Dreyfus, Hubert;Dreyfus, Stuart (1986).Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer.Oxford: Blackwell.ISBN978-0-02-908060-3.Archivedfrom the original on 26 July 2020.Retrieved22 August2020.
• Dyson, George(1998).Darwin among the Machines.Allan Lane Science.ISBN978-0-7382-0030-9.Archivedfrom the original on 26 July 2020.Retrieved22 August2020.
• Edelson, Edward (1991).The Nervous System.New York: Chelsea House.ISBN978-0-7910-0464-7.Archivedfrom the original on 26 July 2020.Retrieved18 November2019.
• Edwards, Benj (17 May 2023)."Poll: AI poses risk to humanity, according to majority of Americans".Ars Technica.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.
• Evans, Woody(2015)."Posthuman Rights: Dimensions of Transhuman Worlds".Teknokultura.12(2).doi:10.5209/rev_TK.2015.v12.n2.49072.
• Fearn, Nicholas (2007).The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers.New York: Grove Press.ISBN978-0-8021-1839-4.
• Ford, Martin; Colvin, Geoff (6 September 2015)."Will robots create more jobs than they destroy?".The Guardian.Archivedfrom the original on 16 June 2018.Retrieved13 January2018.
• Fox News (2023)."Fox News Poll"(PDF).Fox News.Archived(PDF)from the original on 12 May 2023.Retrieved19 June2023.
• Frank, Michael (22 September 2023)."US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order".The Diplomat.Retrieved8 December2023.Instead, the United States has developed a new area of dominance that the rest of the world views with a mixture of awe, envy, and resentment: artificial intelligence... From AI models and research to cloud computing and venture capital, U.S. companies, universities, and research labs – and their affiliates in allied countries – appear to have an enormous lead in both developing cutting-edge AI and commercializing it. The value of U.S. venture capital investments in AI start-ups exceeds that of the rest of the world combined.
• Frey, Carl Benedikt; Osborne, Michael A (1 January 2017). "The future of employment: How susceptible are jobs to computerisation?".Technological Forecasting and Social Change.114:254–280.CiteSeerX10.1.1.395.416.doi:10.1016/j.techfore.2016.08.019.ISSN0040-1625.
• "From not working to neural networking".The Economist.2016.Archivedfrom the original on 31 December 2016.Retrieved26 April2018.
• Galvan, Jill (1 January 1997). "Entering the Posthuman Collective in Philip K. Dick's" Do Androids Dream of Electric Sheep? "".Science Fiction Studies.24(3): 413–429.JSTOR4240644.
• Geist, Edward Moore (9 August 2015)."Is artificial intelligence really an existential threat to humanity?".Bulletin of the Atomic Scientists.Archivedfrom the original on 30 October 2015.Retrieved30 October2015.
• Gertner, Jon(18 July 2023)."Wikipedia's Moment of Truth – Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process? + comment".The New York Times.Archivedfrom the original on 18 July 2023.Retrieved19 July2023.
• Gibbs, Samuel (27 October 2014)."Elon Musk: artificial intelligence is our biggest existential threat".The Guardian.Archivedfrom the original on 30 October 2015.Retrieved30 October2015.
• Goffrey, Andrew (2008). "Algorithm". In Fuller, Matthew (ed.).Software studies: a lexicon.Cambridge, Mass.: MIT Press. pp.15–20.ISBN978-1-4356-4787-9.
• Goldman, Sharon (14 September 2022)."10 years later, deep learning 'revolution' rages on, say AI pioneers Hinton, LeCun and Li".VentureBeat.Retrieved8 December2023.
• Good, I. J.(1965),Speculations Concerning the First Ultraintelligent Machine
• Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016),Deep Learning,MIT Press., archived fromthe originalon 16 April 2016,retrieved12 November2017
• Goodman, Bryce; Flaxman, Seth (2017). "EU regulations on algorithmic decision-making and a 'right to explanation'".AI Magazine.38(3): 50.arXiv:1606.08813.doi:10.1609/aimag.v38i3.2741.S2CID7373959.
• Government Accountability Office(13 September 2022).Consumer Data: Increasing Use Poses Risks to Privacy.gao.gov(Report).
• Grant, Nico; Hill, Kashmir (22 May 2023)."Google's Photo App Still Can't Find Gorillas. And Neither Can Apple's".The New York Times.
• Goswami, Rohan (5 April 2023)."Here's where the A.I. jobs are".CNBC.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.
• Harari, Yuval Noah(October 2018)."Why Technology Favors Tyranny".The Atlantic.Archivedfrom the original on 25 September 2021.Retrieved23 September2021.
• Harari, Yuval Noah (2023)."AI and the future of humanity".YouTube.
• Haugeland, John(1985).Artificial Intelligence: The Very Idea.Cambridge, Mass.: MIT Press.ISBN978-0-262-08153-5.
• Henderson, Mark (24 April 2007)."Human rights for robots? We're getting carried away".The Times Online.London.Archivedfrom the original on 31 May 2014.Retrieved31 May2014.
• Hinton, G.; Deng, L.; Yu, D.; Dahl, G.; Mohamed, A.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.;Sainath, T.;Kingsbury, B. (2012). "Deep Neural Networks for Acoustic Modeling in Speech Recognition – The shared views of four research groups".IEEE Signal Processing Magazine.29(6): 82–97.Bibcode:2012ISPM...29...82H.doi:10.1109/msp.2012.2205597.S2CID206485943.
• Holley, Peter (28 January 2015)."Bill Gates on dangers of artificial intelligence: 'I don't understand why some people are not concerned'".The Washington Post.ISSN0190-8286.Archivedfrom the original on 30 October 2015.Retrieved30 October2015.
• Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert (1989).Multilayer Feedforward Networks are Universal Approximators(PDF).Neural Networks.Vol. 2. Pergamon Press. pp. 359–366.
• Horst, Steven (2005)."The Computational Theory of Mind".The Stanford Encyclopedia of Philosophy.Archivedfrom the original on 6 March 2016.Retrieved7 March2016.
• Howe, J. (November 1994)."Artificial Intelligence at Edinburgh University: a Perspective".Archivedfrom the original on 15 May 2007.Retrieved30 August2007.
• IGM Chicago (30 June 2017)."Robots and Artificial Intelligence".www.igmchicago.org.Archivedfrom the original on 1 May 2019.Retrieved3 July2019.
• Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society".Contemporary Social Science.16(2): 170–184.doi:10.1080/21582041.2018.1563803.ISSN2158-2041.S2CID59298502.
• Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects".Science.349(6245): 255–260.Bibcode:2015Sci...349..255J.doi:10.1126/science.aaa8415.PMID26185243.S2CID677218.
• Kahneman, Daniel(2011).Thinking, Fast and Slow.Macmillan.ISBN978-1-4299-6935-2.Archivedfrom the original on 15 March 2023.Retrieved8 April2012.
• Kahneman, Daniel;Slovic, D.;Tversky, Amos(1982). "Judgment under uncertainty: Heuristics and biases".Science.185(4157). New York: Cambridge University Press: 1124–1131.Bibcode:1974Sci...185.1124T.doi:10.1126/science.185.4157.1124.ISBN978-0-521-28414-1.PMID17835457.S2CID143452957.
• Kasperowicz, Peter (1 May 2023)."Regulate AI? GOP much more skeptical than Dems that government can do it right: poll".Fox News.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.
• Katz, Yarden (1 November 2012)."Noam Chomsky on Where Artificial Intelligence Went Wrong".The Atlantic.Archivedfrom the original on 28 February 2019.Retrieved26 October2014.
• "Kismet".MIT Artificial Intelligence Laboratory, Humanoid Robotics Group.Archivedfrom the original on 17 October 2014.Retrieved25 October2014.
• Kissinger, Henry(1 November 2021)."The Challenge of Being Human in the Age of AI".The Wall Street Journal.Archivedfrom the original on 4 November 2021.Retrieved4 November2021.
• Kobielus, James (27 November 2019)."GPUs Continue to Dominate the AI Accelerator Market for Now".InformationWeek.Archivedfrom the original on 19 October 2021.Retrieved11 June2020.
• Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006)."Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations".Journal of the American Medical Informatics Association.13(4): 369–371.doi:10.1197/jamia.M2055.PMC1513681.PMID16622160.
• Kurzweil, Ray(2005).The Singularity is Near.Penguin Books.ISBN978-0-670-03384-3.
• Langley, Pat (2011)."The changing science of machine learning".Machine Learning.82(3): 275–279.doi:10.1007/s10994-011-5242-y.
• Larson, Jeff;Angwin, Julia(23 May 2016)."How We Analyzed the COMPAS Recidivism Algorithm".ProPublica.Archivedfrom the original on 29 April 2019.Retrieved19 June2020.
• Laskowski, Nicole (November 2023)."What is Artificial Intelligence and How Does AI Work? TechTarget".Enterprise AI.Retrieved30 October2023.
• Law Library of Congress (U.S.). Global Legal Research Directorate, issuing body. (2019).Regulation of artificial intelligence in selected jurisdictions.LCCN2019668143.OCLC1110727808.
• Lee, Timothy B. (22 August 2014)."Will artificial intelligence destroy humanity? Here are 5 reasons not to worry".Vox.Archivedfrom the original on 30 October 2015.Retrieved30 October2015.
• Lenat, Douglas;Guha, R. V. (1989).Building Large Knowledge-Based Systems.Addison-Wesley.ISBN978-0-201-51752-1.
• Lighthill, James(1973). "Artificial Intelligence: A General Survey".Artificial Intelligence: a paper symposium.Science Research Council.
• Lipartito, Kenneth (6 January 2011),The Narrative and the Algorithm: Genres of Credit Reporting from the Nineteenth Century to Today(PDF)(Unpublished manuscript),doi:10.2139/ssrn.1736283,S2CID166742927,archived(PDF)from the original on 9 October 2022
• Lohr, Steve (2017)."Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says".The New York Times.Archivedfrom the original on 14 January 2018.Retrieved13 January2018.
• Lungarella, M.; Metta, G.; Pfeifer, R.; Sandini, G. (2003). "Developmental robotics: a survey".Connection Science.15(4): 151–190.CiteSeerX10.1.1.83.7615.doi:10.1080/09540090310001655110.S2CID1452734.
• "Machine Ethics".aaai.org.Archived fromthe originalon 29 November 2014.
• Madrigal, Alexis C. (27 February 2015)."The case against killer robots, from a guy actually working on artificial intelligence".Fusion.net.Archivedfrom the original on 4 February 2016.Retrieved31 January2016.
• Mahdawi, Arwa (26 June 2017)."What jobs will still be around in 20 years? Read this to prepare your future".The Guardian.Archivedfrom the original on 14 January 2018.Retrieved13 January2018.
• Maker, Meg Houston (2006)."AI@50: AI Past, Present, Future".Dartmouth College. Archived fromthe originalon 3 January 2007.Retrieved16 October2008.
• Marmouyet, Françoise (15 December 2023)."Google's Gemini: is the new AI model really better than ChatGPT?".The Conversation.Retrieved25 December2023.
• Minsky, Marvin(1986),The Society of Mind,Simon and Schuster
• Maschafilm (2010)."Content: Plug & Pray Film – Artificial Intelligence – Robots".plugandpray-film.de.Archivedfrom the original on 12 February 2016.
• McCarthy, John;Minsky, Marvin;Rochester, Nathan;Shannon, Claude(1955)."A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence".Archived fromthe originalon 26 August 2007.Retrieved30 August2007.
• McCarthy, John(2007), "From Here to Human-Level AI",Artificial Intelligence:171
• McCarthy, John(1999),What is AI?,archivedfrom the original on 4 December 2022,retrieved4 December2022
• McCauley, Lee (2007). "AI armageddon and the three laws of robotics".Ethics and Information Technology.9(2): 153–164.CiteSeerX10.1.1.85.8904.doi:10.1007/s10676-007-9138-2.S2CID37272949.
• McGarry, Ken (1 December 2005). "A survey of interestingness measures for knowledge discovery".The Knowledge Engineering Review.20(1): 39–61.doi:10.1017/S0269888905000408.S2CID14987656.
• McGaughey, E (2022),Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy,p. 51(3) Industrial Law Journal 511–559,doi:10.2139/ssrn.3044448,S2CID219336439,SSRN3044448,archivedfrom the original on 31 January 2021,retrieved27 May2023
• Merkle, Daniel; Middendorf, Martin (2013). "Swarm Intelligence". In Burke, Edmund K.; Kendall, Graham (eds.).Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques.Springer Science & Business Media.ISBN978-1-4614-6940-7.
• Minsky, Marvin(1967),Computation: Finite and Infinite Machines,Englewood Cliffs, N.J.: Prentice-Hall
• Moravec, Hans(1988).Mind Children.Harvard University Press.ISBN978-0-674-57616-2.Archivedfrom the original on 26 July 2020.Retrieved18 November2019.
• Morgenstern, Michael (9 May 2015)."Automation and anxiety".The Economist.Archivedfrom the original on 12 January 2018.Retrieved13 January2018.
• Müller, Vincent C.; Bostrom, Nick (2014)."Future Progress in Artificial Intelligence: A Poll Among Experts"(PDF).AI Matters.1(1): 9–11.doi:10.1145/2639475.2639478.S2CID8510016.Archived(PDF)from the original on 15 January 2016.
• Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics".Image and Vision Computing.26(1): 82–101.doi:10.1016/j.imavis.2007.08.013.S2CID10767011.
• Nilsson, Nils(1995), "Eyes on the Prize",AI Magazine,vol. 16, pp. 9–17
• Newell, Allen;Simon, H. A.(1976)."Computer Science as Empirical Inquiry: Symbols and Search".Communications of the ACM.19(3): 113–126.doi:10.1145/360018.360022.
• Nicas, Jack (7 February 2018)."How YouTube Drives People to the Internet's Darkest Corners".The Wall Street Journal.ISSN0099-9660.Retrieved16 June2018.
• Nilsson, Nils(1983)."Artificial Intelligence Prepares for 2001"(PDF).AI Magazine.1(1).Archived(PDF)from the original on 17 August 2020.Retrieved22 August2020.Presidential Address to theAssociation for the Advancement of Artificial Intelligence.
• NRC (United States National Research Council)(1999). "Developments in Artificial Intelligence".Funding a Revolution: Government Support for Computing Research.National Academy Press.
• Omohundro, Steve(2008).The Nature of Self-Improving Artificial Intelligence.presented and distributed at the 2007 Singularity Summit, San Francisco, CA.
• Oudeyer, P-Y. (2010)."On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development"(PDF).IEEE Transactions on Autonomous Mental Development.2(1): 2–16.doi:10.1109/tamd.2009.2039057.S2CID6362217.Archived(PDF)from the original on 3 October 2018.Retrieved4 June2013.
• Pennachin, C.; Goertzel, B. (2007). "Contemporary Approaches to Artificial General Intelligence".Artificial General Intelligence.Cognitive Technologies. Berlin, Heidelberg: Springer. pp. 1–30.doi:10.1007/978-3-540-68677-4_1.ISBN978-3-540-23733-4.
• Pinker, Steven(2007) [1994],The Language Instinct,Perennial Modern Classics, Harper,ISBN978-0-06-133646-1
• Poria, Soujanya; Cambria, Erik; Bajpai, Rajiv; Hussain, Amir (September 2017)."A review of affective computing: From unimodal analysis to multimodal fusion".Information Fusion.37:98–125.doi:10.1016/j.inffus.2017.02.003.hdl:1893/25490.S2CID205433041.Archivedfrom the original on 23 March 2023.Retrieved27 April2021.
• Rawlinson, Kevin (29 January 2015)."Microsoft's Bill Gates insists AI is a threat".BBC News.Archivedfrom the original on 29 January 2015.Retrieved30 January2015.
• Reisner, Alex (19 August 2023),"Revealed: The Authors Whose Pirated Books are Powering Generative AI",The Atlantic
• Roberts, Jacob (2016)."Thinking Machines: The Search for Artificial Intelligence".Distillations.Vol. 2, no. 2. pp. 14–23. Archived fromthe originalon 19 August 2018.Retrieved20 March2018.
• Robitzski, Dan (5 September 2018)."Five experts share what scares them the most about AI".Archivedfrom the original on 8 December 2019.Retrieved8 December2019.
• "Robots could demand legal rights".BBC News.21 December 2006.Archivedfrom the original on 15 October 2019.Retrieved3 February2011.
• Rose, Steve (11 July 2023). "AI Utopia or dystopia?".The Guardian Weekly.pp. 42–43.
• Russell, Stuart(2019).Human Compatible: Artificial Intelligence and the Problem of Control.United States: Viking.ISBN978-0-525-55861-3.OCLC1083694322.
• Sainato, Michael (19 August 2015)."Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence".Observer.Archivedfrom the original on 30 October 2015.Retrieved30 October2015.
• Sample, Ian (5 November 2017)."Computer says no: why making AIs fair, accountable and transparent is crucial".The Guardian.Retrieved30 January2018.
• Rothman, Denis (7 October 2020)."Exploring LIME Explanations and the Mathematics Behind It".Codemotion.Retrieved25 November2023.
• Scassellati, Brian (2002). "Theory of mind for a humanoid robot".Autonomous Robots.12(1): 13–24.doi:10.1023/A:1013298507114.S2CID1979315.
• Schmidhuber, J.(2015). "Deep Learning in Neural Networks: An Overview".Neural Networks.61:85–117.arXiv:1404.7828.doi:10.1016/j.neunet.2014.09.003.PMID25462637.S2CID11715509.
• Schmidhuber, Jürgen(2022)."Annotated History of Modern AI and Deep Learning".
• Schulz, Hannes; Behnke, Sven (1 November 2012)."Deep Learning".KI – Künstliche Intelligenz.26(4): 357–363.doi:10.1007/s13218-012-0198-z.ISSN1610-1987.S2CID220523562.
• Searle, John(1980)."Minds, Brains and Programs"(PDF).Behavioral and Brain Sciences.3(3): 417–457.doi:10.1017/S0140525X00005756.S2CID55303721.Archived(PDF)from the original on 17 March 2019.Retrieved22 August2020.
• Searle, John(1999).Mind, language and society.New York: Basic Books.ISBN978-0-465-04521-1.OCLC231867665.Archivedfrom the original on 26 July 2020.Retrieved22 August2020.
• Simon, H. A. (1965),The Shape of Automation for Men and Management,New York: Harper & Row
• Simonite, Tom (31 March 2016)."How Google Plans to Solve Artificial Intelligence".MIT Technology Review.
• Smith, Craig S. (15 March 2023)."ChatGPT-4 Creator Ilya Sutskever on AI Hallucinations and AI Democracy".Forbes.Retrieved25 December2023.
• Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval".IEEE MultiMedia.1(2): 62–72.doi:10.1109/93.311653.S2CID32710913.
• Solomonoff, Ray(1956).An Inductive Inference Machine(PDF).Dartmouth Summer Research Conference on Artificial Intelligence.Archived(PDF)from the original on 26 April 2011.Retrieved22 March2011– via std.com, pdf scanned copy of the original.Later published as
  Solomonoff, Ray (1957). "An Inductive Inference Machine".IRE Convention Record.Vol. Section on Information Theory, part 2. pp. 56–62.
• Stanford University (2023)."Artificial Intelligence Index Report 2023/Chapter 6: Policy and Governance"(PDF).AI Index.Archived(PDF)from the original on 19 June 2023.Retrieved19 June2023.
• Tao, Jianhua; Tan, Tieniu (2005).Affective Computing and Intelligent Interaction.Affective Computing: A Review. Lecture Notes in Computer Science. Vol. 3784. Springer. pp. 981–995.doi:10.1007/11573548.ISBN978-3-540-29621-8.
• Taylor, Josh; Hern, Alex (2 May 2023)."'Godfather of AI' Geoffrey Hinton quits Google and warns over dangers of misinformation ".The Guardian.
• Thompson, Derek (23 January 2014)."What Jobs Will the Robots Take?".The Atlantic.Archivedfrom the original on 24 April 2018.Retrieved24 April2018.
• Thro, Ellen (1993).Robotics: The Marriage of Computers and Machines.New York: Facts on File.ISBN978-0-8160-2628-9.Archivedfrom the original on 26 July 2020.Retrieved22 August2020.
• Toews, Rob (3 September 2023)."Transformers Revolutionized AI. What Will Replace Them?".Forbes.Retrieved8 December2023.
• Turing, Alan(October 1950), "Computing Machinery and Intelligence",Mind,LIX(236): 433–460,doi:10.1093/mind/LIX.236.433,ISSN0026-4423
• UNESCO Science Report: the Race Against Time for Smarter Development.Paris: UNESCO. 2021.ISBN978-92-3-100450-6.Archivedfrom the original on 18 June 2022.Retrieved18 September2021.
• Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric; Ekins, Sean (7 March 2022)."Dual use of artificial-intelligence-powered drug discovery".Nature Machine Intelligence.4(3): 189–191.doi:10.1038/s42256-022-00465-9.PMC9544280.PMID36211133.S2CID247302391.
• Valance, Christ (30 May 2023)."Artificial intelligence could lead to extinction, experts warn".BBC News.Archivedfrom the original on 17 June 2023.Retrieved18 June2023.
• Valinsky, Jordan (11 April 2019),"Amazon reportedly employs thousands of people to listen to your Alexa conversations",CNN.com
• Verma, Yugesh (25 December 2021)."A Complete Guide to SHAP – SHAPley Additive exPlanations for Practitioners".Analytics India Magazine.Retrieved25 November2023.
• Vincent, James (7 November 2019)."OpenAI has published the text-generating AI it said was too dangerous to share".The Verge.Archivedfrom the original on 11 June 2020.Retrieved11 June2020.
• Vincent, James (15 November 2022)."The scary truth about AI copyright is nobody knows what will happen next".The Verge.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.
• Vincent, James (3 April 2023)."AI is entering an era of corporate control".The Verge.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.
• Vinge, Vernor(1993)."The Coming Technological Singularity: How to Survive in the Post-Human Era".Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace:11.Bibcode:1993vise.nasa...11V.Archived fromthe originalon 1 January 2007.Retrieved14 November2011.
• Waddell, Kaveh (2018)."Chatbots Have Entered the Uncanny Valley".The Atlantic.Archivedfrom the original on 24 April 2018.Retrieved24 April2018.
• Wallach, Wendell (2010).Moral Machines.Oxford University Press.
• Wason, P. C.;Shapiro, D. (1966)."Reasoning".In Foss, B. M. (ed.).New horizons in psychology.Harmondsworth: Penguin.Archivedfrom the original on 26 July 2020.Retrieved18 November2019.
• Weng, J.; McClelland; Pentland, A.; Sporns, O.; Stockman, I.; Sur, M.; Thelen, E. (2001)."Autonomous mental development by robots and animals"(PDF).Science.291(5504): 599–600.doi:10.1126/science.291.5504.599.PMID11229402.S2CID54131797.Archived(PDF)from the original on 4 September 2013.Retrieved4 June2013– via msu.edu.
• "What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?".Scientific American.21 October 1999.Archivedfrom the original on 6 May 2018.Retrieved5 May2018.
• Williams, Rhiannon (28 June 2023),"Humans may be more likely to believe disinformation generated by AI",MIT Technology Review
• Wirtz, Bernd W.; Weyerer, Jan C.; Geyer, Carolin (24 July 2018)."Artificial Intelligence and the Public Sector – Applications and Challenges".International Journal of Public Administration.42(7): 596–615.doi:10.1080/01900692.2018.1498103.ISSN0190-0692.S2CID158829602.Archivedfrom the original on 18 August 2020.Retrieved22 August2020.
• Wong, Matteo (19 May 2023),"ChatGPT Is Already Obsolete",The Atlantic
• Yudkowsky, E (2008),"Artificial Intelligence as a Positive and Negative Factor in Global Risk"(PDF),Global Catastrophic Risks,Oxford University Press, 2008,Bibcode:2008gcr..book..303Y,archived(PDF)from the original on 19 October 2013,retrieved24 September2021

Further reading

• Ashish Vaswani, Noam Shazeer, Niki Parmar et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Seminal paper ontransformers.
• Autor, David H.,"Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3)Journal of Economic Perspectives3.
• Boden, Margaret,Mind As Machine,Oxford University Press,2006.
• Cukier, Kenneth,"Ready for Robots? How to Think about the Future of AI",Foreign Affairs,vol. 98, no. 4 (July/August 2019), pp. 192–98.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. 197.) Computer scientistAlex Pentlandwrites: "CurrentAI machine-learningalgorithmsare, at their core, dead simple stupid. They work, but they work by brute force. "(p. 198.)
• Gertner, Jon. (2023) "Wikipedia's Moment of Truth: Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process?"New York Times Magazine(July 18, 2023)online
• Gleick, James,"The Fate of Free Will" (review ofKevin 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. "Agencyis what distinguishes us from machines. For biological creatures,reasonandpurposecome from acting in the world and experiencing the consequences. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no occasion for that. "(p. 30.)
• Hughes-Castleberry, Kenna,"A Murder Mystery Puzzle: The literary puzzleCain'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. 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 ofcontextthey receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyzeancient languages.In some cases, there are few historical records on long-gonecivilizationsto serve astraining datafor such a purpose. "(p. 82.)
• Immerwahr, Daniel,"Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?",The New Yorker,20 November 2023, pp. 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 ofcartoons,especially smutty ones. "(p. 59.)
• Johnston, John (2008)The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI,MIT Press.
• Jumper, John; Evans, Richard; Pritzel, Alexander; et al. (26 August 2021)."Highly accurate protein structure prediction with AlphaFold".Nature.596(7873): 583–589.Bibcode:2021Natur.596..583J.doi:10.1038/s41586-021-03819-2.PMC8371605.PMID34265844.S2CID235959867.
• LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (28 May 2015)."Deep learning".Nature.521(7553): 436–444.Bibcode:2015Natur.521..436L.doi:10.1038/nature14539.PMID26017442.S2CID3074096.Archivedfrom the original on 5 June 2023.Retrieved19 June2023.
• 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. 42–45.
• Mitchell, Melanie (2019).Artificial intelligence: a guide for thinking humans.New York: Farrar, Straus and Giroux.ISBN9780374257835.
• Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; et al. (26 February 2015)."Human-level control through deep reinforcement learning".Nature.518(7540): 529–533.Bibcode:2015Natur.518..529M.doi:10.1038/nature14236.PMID25719670.S2CID205242740.Archivedfrom the original on 19 June 2023.Retrieved19 June2023.IntroducedDQN,which produced human-level performance on some Atari games.
• Press, Eyal,"In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?",The New Yorker,20 November 2023, pp. 20–26.
• Roivainen, Eka,"AI's IQ:ChatGPTaced a [standard intelligence] test but showed thatintelligencecannot be measured byIQalone ",Scientific American,vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ,ChatGPTfails at tasks that require real humanlike reasoning or an understanding of the physical and social world.... ChatGPT seemed unable to reason logically and tried to rely on its vast database of... facts derived from online texts. "
• Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race",Foreign Affairs,vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.)
• Serenko, Alexander; Michael Dohan (2011)."Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence"(PDF).Journal of Informetrics.5(4): 629–49.doi:10.1016/j.joi.2011.06.002.Archived(PDF)from the original on 4 October 2013.Retrieved12 September2013.
• Silver, David; Huang, Aja; Maddison, Chris J.; et al. (28 January 2016)."Mastering the game of Go with deep neural networks and tree search".Nature.529(7587): 484–489.Bibcode:2016Natur.529..484S.doi:10.1038/nature16961.PMID26819042.S2CID515925.Archivedfrom the original on 18 June 2023.Retrieved19 June2023.
• White Paper: On Artificial Intelligence – A European approach to excellence and trust(PDF).Brussels: European Commission. 2020.Archived(PDF)from the original on 20 February 2020.Retrieved20 February2020.

External links

• "Artificial Intelligence".Internet Encyclopedia of Philosophy.
• Thomason, Richmond."Logic and Artificial Intelligence".InZalta, Edward N.(ed.).Stanford Encyclopedia of Philosophy.
• Artificial Intelligence.BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (In Our Time,8 December 2005).
• Theranostics and AI – The Next Advance in Cancer Precision Medicine