Artificial intelligence

Artificial intelligence?(AI) is?intelligence—perceiving, synthesizing, and inferring information—demonstrated by?machines, as opposed to intelligence displayed by?non-human animals?and?humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.

AI applications?include advanced?web search?engines (e.g.,?Google Search),?recommendation systems?(used by?YouTube,?Amazon?and?Netflix),?understanding human speech?(such as?Siri?and?Alexa),?self-driving cars?(e.g.,?Waymo), generative or creative tools (ChatGPT?and?AI art),?automated decision-making?and competing at the highest level in?strategic game?systems (such as?chess?and?Go).[1]

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the?AI effect.[2]?For instance,?optical character recognition?is frequently excluded from things considered to be AI,[3]?having become a routine technology.[4]

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[5][6]?followed by disappointment and the loss of funding (known as an "AI winter"),[7][8]?followed by new approaches, success and renewed funding.[6][9]?AI research has tried and discarded many different approaches since its founding, including simulating the brain,?modeling human problem solving,?formal logic,?large databases of knowledge?and imitating animal behavior. In the first decades of the 21st century, highly mathematical-statistical?machine learning?has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.[9][10]

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include?reasoning,?knowledge representation,?planning,?learning,?natural language processing,?perception, and the ability to move and manipulate objects.[a]?General intelligence?(the ability to solve an arbitrary problem) is among the field's long-term goals.[11]?To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques – including search and mathematical optimization, formal logic,?artificial neural networks, and methods based on?statistics,?probability?and?economics. AI also draws upon?computer science,?psychology,?linguistics,?philosophy, and many other fields.

The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".[b]?This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by?myth,?fiction?and?philosophy?since antiquity.[13]?Computer scientists?and?philosophers?have since suggested that AI may become an?existential risk?to humanity if its rational capacities are not steered towards beneficial goals.[c]

History

Main articles:?History of artificial intelligence?and?Timeline of artificial intelligence

Silver?didrachma?from?Crete?depicting?Talos, an ancient mythical?automaton?with artificial intelligence

Artificial beings?with intelligence appeared as?storytelling devices?in antiquity,[14]?and have been common in fiction, as in?Mary Shelley's?Frankenstein?or?Karel ?apek's?R.U.R.[15]?These characters and their fates raised many of the same issues now discussed in the?ethics of artificial intelligence.[16]

The study of mechanical or?"formal" reasoning?began with?philosophers?and mathematicians in antiquity. The study of mathematical logic led directly to?Alan Turing's?theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight that digital computers can simulate any process of formal reasoning is known as the?Church–Turing thesis.[17]?This, along with concurrent discoveries in?neurobiology,?information theory?and?cybernetics, led researchers to consider the possibility of building an electronic brain.[18]?The first work that is now generally recognized as AI was?McCullouch?and?Pitts' 1943 formal design for?Turing-complete?"artificial neurons".[19]

By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as?Symbolic AI?or?GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included?Allen Newell,?Herbert A. Simon, and?Marvin Minsky. Closely associated with this approach was the?"heuristic search"?approach, which likened intelligence to a problem of exploring a space of possibilities for answers.

The second vision, known as the?connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently?Frank Rosenblatt, sought to connect?Perceptron?in ways inspired by connections of neurons.[20]?James Manyika?and others have compared the two approaches to the mind (Symbolic AI) and the brain (connectionist). Manyika argues that symbolic approaches dominated the push for artificial intelligence in this period, due in part to its connection to intellectual traditions of?Descartes,?Boole,?Gottlob Frege,?Bertrand Russell, and others. Connectionist approaches based on?cybernetics?or?artificial neural networks?were pushed to the background but have gained new prominence in recent decades.[21]

The field of AI research was born at?a workshop?at?Dartmouth College?in 1956.[d][24]?The attendees became the founders and leaders of AI research.[e]?They and their students produced programs that the press described as "astonishing":[f]?computers were learning?checkers?strategies, solving word problems in algebra, proving?logical theorems?and speaking English.[g][26]

By the middle of the 1960s, research in the U.S. was heavily funded by the?Department of Defense[27]?and laboratories had been established around the world.[28]

Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with?artificial general intelligence?and considered this the goal of their field.[29]?Herbert Simon?predicted, "machines will be capable, within twenty years, of doing any work a man can do".[30]?Marvin Minsky?agreed, writing, "within a generation?... the problem of creating 'artificial intelligence' will substantially be solved".[31]

They had failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the?criticism?of?Sir James Lighthill[32]?and ongoing pressure from the US Congress to?fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.[7]

In the early 1980s, AI research was revived by the commercial success of?expert systems,[33]?a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's?fifth generation computer?project inspired the U.S. and British governments to restore funding for?academic research.[6]?However, beginning with the collapse of the?Lisp Machine?market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.[8]

Many researchers began to doubt that the?symbolic approach?would be able to imitate all the processes of human cognition, especially?perception, robotics,?learning?and?pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.[34]?Robotics?researchers, such as?Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, and learn their environment.[h]

Interest in?neural networks?and "connectionism" was revived by?Geoffrey Hinton,?David Rumelhart?and others in the middle of the 1980s.[39]?Soft computing?tools were developed in the 1980s, such as?neural networks,?fuzzy systems,?Grey system theory,?evolutionary computation?and many tools drawn from?statistics?or?mathematical optimization.

AI gradually restored its reputation in the late 1990s and early 21st century by finding specific solutions to specific problems. The narrow focus allowed researchers to produce verifiable results, exploit more mathematical methods, and collaborate with other fields (such as?statistics,?economics?and?mathematics).[40]?By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[10]

Faster computers, algorithmic improvements, and access to?large amounts of data?enabled advances in?machine learning?and perception; data-hungry?deep learning?methods started to dominate accuracy benchmarks?around 2012.[41]?According to?Bloomberg's?Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within?Google?increased from a "sporadic usage" in 2012 to more than 2,700 projects.[i]?He attributed this to an increase in affordable?neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[9]

In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes".[42]?The amount of research into AI (measured by total publications) increased by 50% in the years 2015–2019.[43]

Numerous academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Much of current research involves statistical AI, which is overwhelmingly used to solve specific problems, even highly successful techniques such as?deep learning. This concern has led to the subfield of?artificial general intelligence?(or "AGI"), which had several well-funded institutions by the 2010s.[11]

Goals

The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[a]

Reasoning, problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[44]?By the late 1980s and 1990s, AI research had developed methods for dealing with?uncertain?or incomplete information, employing concepts from?probability?and?economics.[45]

Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.[46]?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.[47]

Knowledge representation

Main articles:?Knowledge representation,?Commonsense knowledge,?Description logic, and?Ontology

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

Knowledge representation and?knowledge engineering[48]?allow AI programs to answer questions intelligently and make deductions about real-world facts.

A representation of "what exists" is an?ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them.[49]?The most general ontologies are called?upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between?domain ontologies?that cover specific knowledge about a particular knowledge?domain?(field of interest or area of concern). A truly intelligent program would also need access to commonsense knowledge; the set of facts that an average person knows. The?semantics?of an ontology is typically represented in description logic, such as the?Web Ontology Language.[50]

AI research has developed tools to represent specific domains, such as objects, properties, categories and relations between objects;[50]?situations, events, states and time;[51]?causes and effects;[52]?knowledge about knowledge (what we know about what other people know);.[53]?default reasoning?(things that humans assume are true until they are told differently and will remain true even when other facts are changing);[54]?as well as other domains. Among the most difficult problems in AI are: the breadth of commonsense knowledge (the number of atomic facts that the average person knows is enormous);[55]?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).[47]

Formal knowledge representations are used in content-based indexing and retrieval,[56]?scene interpretation,[57]?clinical decision support,[58]?knowledge discovery (mining "interesting" and actionable inferences from large databases),[59]?and other areas.[60]

Learning

Main article:?Machine learning

Machine learning (ML), a fundamental concept of AI research since the field's inception,[j]?is the study of computer algorithms that improve automatically through experience.[k]

Unsupervised learning?finds patterns in a stream of input.

Supervised learning?requires a human to label the input data first, and comes in two main varieties:?classification?and numerical?regression. Classification is used to determine what category something belongs in – the program sees a number of examples of things from several categories and will learn to classify new inputs. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam".[64]

In?reinforcement learning?the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space.[65]

Transfer learning?is when the knowledge gained from one problem is applied to a new problem.[66]

Computational learning theory?can assess learners by?computational complexity, by?sample complexity?(how much data is required), or by other notions of?optimization.[67]

Natural language processing

Main article:?Natural language processing

A?parse tree?represents the?syntactic?structure of a sentence according to some?formal grammar.

Natural language processing (NLP)[68]?allows machines to read and?understand?human language. A sufficiently powerful natural language processing system would enable?natural-language user interfaces?and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include?information retrieval,?question answering?and?machine translation.[69]

Symbolic AI?used formal?syntax?to translate the?deep structure?of sentences into?logic. This failed to produce useful applications, due to the?intractability?of logic[46]?and the breadth of commonsense knowledge.[55]?Modern statistical techniques include co-occurrence frequencies (how often one word appears near another), "Keyword spotting" (searching for a particular word to retrieve information),?transformer-based?deep learning?(which finds patterns in text), and others.[70]?They have achieved acceptable accuracy at the page or paragraph level, and, by 2019, could generate coherent text.[71]

Perception

Main articles:?Machine perception,?Computer vision, and?Speech recognition

Feature detection?(pictured:?edge detection) helps AI compose informative abstract structures out of raw data.

Machine perception[72]?is the ability to use input from sensors (such as cameras, microphones, wireless signals, and active?lidar, sonar, radar, and?tactile sensors) to deduce aspects of the world. Applications include?speech recognition,[73]?facial recognition, and?object recognition.[74]?Computer vision is the ability to analyze visual input.[75]

Social intelligence

Main article:?Affective computing

Kismet, a robot with rudimentary social skills[76]

Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human?feeling, emotion and mood.[77]?For example, some?virtual assistants?are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate?human–computer interaction. However, this tends to give na?ve users an unrealistic conception of how intelligent existing computer agents actually are.[78]?Moderate successes related to affective computing include textual?sentiment analysis?and, more recently,?multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[79]

General intelligence

Main article:?Artificial general intelligence

A machine with general intelligence can solve a wide variety of problems with breadth and versatility similar to human intelligence. There are several competing ideas about how to develop artificial general intelligence.?Hans Moravec?and?Marvin Minsky?argue that work in different individual domains can be incorporated into an advanced?multi-agent system?or?cognitive architecture?with general intelligence.[80]?Pedro Domingos?hopes that there is a conceptually straightforward, but mathematically difficult, "master algorithm" that could lead to AGI.[81]?Others believe that?anthropomorphic?features like an?artificial brain[82]?or simulated?child development[l]?will someday reach a critical point where general intelligence?emerges.

Tools

Search and optimization

Main articles:?Search algorithm,?Mathematical optimization, and?Evolutionary computation

AI can solve many problems by intelligently searching through many possible solutions.[83]?Reasoning?can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from?premises?to?conclusions, where each step is the application of an?inference rule.[84]?Planning?algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called?means-ends analysis.[85]?Robotics?algorithms for moving limbs and grasping objects use?local searches?in?configuration space.[86]

Simple exhaustive searches[87]?are rarely sufficient for most real-world problems: the?search space?(the number of places to search) quickly grows to?astronomical numbers. The result is a search that is?too slow?or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies, heuristics can also serve to eliminate some choices unlikely to lead to a goal (called "pruning?the?search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies.[88]?Heuristics limit the search for solutions into a smaller sample size.[89]

A?particle swarm?seeking the?global minimum

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of?optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind?hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other related optimization algorithms include?random optimization,?beam search?and?metaheuristics?like?simulated annealing.[90]?Evolutionary computation?uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine,?selecting?only the fittest to survive each generation (refining the guesses). Classic?evolutionary algorithms?include?genetic algorithms,?gene expression programming, and?genetic programming.[91]?Alternatively, distributed search processes can coordinate via?swarm intelligence?algorithms. Two popular swarm algorithms used in search are?particle swarm optimization?(inspired by bird?flocking) and?ant colony optimization?(inspired by?ant trails).[92]

Logic

Main articles:?Logic programming?and?Automated reasoning

Logic[93]?is used for knowledge representation and problem-solving, but it can be applied to other problems as well. For example, the?satplan?algorithm uses logic for?planning[94]?and?inductive logic programming?is a method for?learning.[95]

Several different forms of logic are used in AI research.?Propositional logic[96]?involves?truth functions?such as "or" and "not".?First-order logic[97]?adds?quantifiers?and?predicates?and can express facts about objects, their properties, and their relations with each other.?Fuzzy logic?assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry), that are too linguistically imprecise to be completely true or false.[98]?Default logics,?non-monotonic logics?and?circumscription?are forms of logic designed to help with default reasoning and the?qualification problem.[54]?Several extensions of logic have been designed to handle specific domains of?knowledge, such as?description logics;[50]?situation calculus,?event calculus?and?fluent calculus?(for representing events and time);[51]?causal calculus;[52]?belief calculus (belief revision); and?modal logics.[53]?Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as?paraconsistent logics.[99]

Probabilistic methods for uncertain reasoning

Main articles:?Bayesian network,?Hidden Markov model,?Kalman filter,?Particle filter,?Decision theory, and?Utility theory

Expectation-maximization?clustering of?Old Faithful?eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.

Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from?probability?theory and economics.[100]?Bayesian networks[101]?are a very general tool that can be used for various problems, including reasoning (using the?Bayesian inference?algorithm),[m][103]?learning?(using the?expectation-maximization algorithm),[n][105]?planning?(using?decision networks)[106]?and?perception?(using?dynamic Bayesian networks).[107]?Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping?perception?systems to analyze processes that occur over time (e.g.,?hidden Markov models?or?Kalman filters).[107]

A key concept from the science of economics is "utility", a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using?decision theory,?decision analysis,[108]?and information value theory.[109]?These tools include models such as?Markov decision processes,[110]?dynamic?decision networks,[107]?game theory?and?mechanism design.[111]

Classifiers and statistical learning methods

Main articles:?Statistical classification?and?Machine learning

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if diamond then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems.?Classifiers?are functions that use?pattern matching?to determine the closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class is a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[112]

A classifier can be trained in various ways; there are many statistical and?machine learning?approaches. The?decision tree?is the simplest and most widely used symbolic machine learning algorithm.[113]?K-nearest neighbor algorithm?was the most widely used analogical AI until the mid-1990s.[114]?Kernel methods?such as the?support vector machine?(SVM) displaced k-nearest neighbor in the 1990s.[115]?The?naive Bayes classifier?is reportedly the "most widely used learner"[116]?at Google, due in part to its scalability.[117]?Neural networks?are also used for classification.[118]

Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.[119]

Artificial neural networks

Main articles:?Artificial neural network?and?Connectionism

A neural network is an interconnected group of nodes, akin to the vast network of?neurons?in the?human brain.

Neural networks[118]?were inspired by the architecture of neurons in the human brain. A simple "neuron"?N?accepts input from other neurons, each of which, when activated (or "fired"), casts a weighted "vote" for or against whether neuron?N?should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes.

Modern neural networks model complex relationships between inputs and outputs and?find patterns?in data. They can learn continuous functions and even digital logical operations. Neural networks can be viewed as a type of?mathematical optimization?– they perform?gradient descent?on a multi-dimensional topology that was created by?training?the network. The most common training technique is the?backpropagation?algorithm.[120]?Other?learning?techniques for neural networks are?Hebbian learning?("fire together, wire together"),?GMDH?or?competitive learning.[121]

The main categories of networks are acyclic or?feedforward neural networks?(where the signal passes in only one direction) and?recurrent neural networks?(which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are?perceptrons,?multi-layer perceptrons?and?radial basis networks.[122]

Deep learning

Representing images on multiple layers of abstraction in deep learning[123]

Deep learning[124]?uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in?image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[125]?Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including?computer vision,?speech recognition,?image classification[126]?and others.

Deep learning often uses?convolutional neural networks?for many or all of its layers. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's?receptive field. This can substantially reduce the number of weighted connections between neurons,[127]?and creates a hierarchy similar to the organization of the animal visual cortex.[128]

In a?recurrent neural network?(RNN) the signal will propagate through a layer more than once;[129]?thus, an RNN is an example of deep learning.[130]?RNNs can be trained by?gradient descent,[131]?however long-term gradients which are back-propagated can "vanish" (that is, they can tend to zero) or "explode" (that is, they can tend to infinity), known as the?vanishing gradient problem.[132]?The?long short term memory?(LSTM) technique can prevent this in most cases.[133]

Specialized languages and hardware

Main articles:?Programming languages for artificial intelligence?and?Hardware for artificial intelligence

Specialized languages for artificial intelligence have been developed, such as?Lisp,?Prolog,?TensorFlow?and many others. Hardware developed for AI includes?AI accelerators?and?neuromorphic computing.

Applications

Main article:?Applications of artificial intelligence

See also:?Embodied cognition?and?Legal informatics

For this project of the artist Joseph Ayerle the AI had to learn the typical patterns in the colors and brushstrokes of Renaissance painter?Raphael. The portrait shows the face of the actress?Ornella Muti, "painted" by AI in the style of Raphael.

AI is relevant to any intellectual task.[134]?Modern artificial intelligence techniques are pervasive and are too numerous to list here.[135]?Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the?AI effect.[136]

In the 2010s, AI applications were at the heart of the most commercially successful areas of computing, and have become a ubiquitous feature of daily life. AI is used in?search engines?(such as?Google Search),?targeting online advertisements,[137]?recommendation systems?(offered by?Netflix,?YouTube?or?Amazon), driving?internet traffic,[138][139]?targeted advertising?(AdSense,?Facebook),?virtual assistants?(such as?Siri?or?Alexa),[140]?autonomous vehicles?(including?drones,?ADAS?and?self-driving cars),?automatic language translation?(Microsoft Translator,?Google Translate),?facial recognition?(Apple's?Face ID?or?Microsoft's?DeepFace),?image labeling?(used by?Facebook,?Apple's?iPhoto?and?TikTok) ,?spam filtering?and?chatbots?(such as?Chat GPT).

There are also thousands of successful AI applications used to solve problems for specific industries or institutions. A few examples are?energy storage,[141]?deepfakes,[142]?medical diagnosis, military logistics, or supply chain management.

Game playing?has been a test of AI's strength since the 1950s.?Deep Blue?became the first computer chess-playing system to beat a reigning world chess champion,?Garry Kasparov, on 11 May 1997.[143]?In 2011, in a?Jeopardy!?quiz show?exhibition match,?IBM's?question answering system,?Watson, defeated the two greatest?Jeopardy!?champions,?Brad Rutter?and?Ken Jennings, by a significant margin.[144]?In March 2016,?AlphaGo?won 4 out of 5 games of?Go?in a match with Go champion?Lee Sedol, becoming the first?computer Go-playing system to beat a professional Go player without?handicaps.[145]?Other programs handle?imperfect-information?games; such as for?poker?at a superhuman level,?Pluribus[o]?and?Cepheus.[147]?DeepMind?in the 2010s developed a "generalized artificial intelligence" that could learn many diverse?Atari?games on its own.[148]

By 2020,?Natural Language Processing?systems such as the enormous?GPT-3?(then by far the largest artificial neural network) were matching human performance on pre-existing benchmarks, albeit without the system attaining a commonsense understanding of the contents of the benchmarks.[149]?DeepMind's?AlphaFold 2?(2020) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein.[150]?Other applications predict the result of judicial decisions,[151]?create art?(such as poetry or painting) and?prove mathematical theorems.

Smart traffic lights

Artificially intelligent traffic lights?use cameras with?radar,?ultrasonic acoustic?location sensors, and?predictive algorithms?to improve traffic flow

Smart traffic lights?have been developed at?Carnegie Mellon?since 2009. Professor Stephen Smith has started a company since then?Surtrac?that has installed smart traffic control systems in 22 cities. It costs about $20,000 per intersection to install. Drive time has been reduced by 25% and traffic jam waiting time has been reduced by 40% at the intersections it has been installed.[152]

Intellectual Property

AI Patent families for functional application categories and sub categories.?Computer vision?represents 49 percent of patent families related to a functional application in 2016.

In 2019,?WIPO?reported that AI was the most prolific?emerging technology?in terms of the number of?patent?applications and granted patents, the?Internet of things?was estimated to be the largest in terms of market size. It was followed, again in market size, by big data technologies, robotics, AI, 3D printing and the fifth generation of mobile services (5G).[153]?Since AI emerged in the 1950s, 340,000 AI-related patent applications were filed by innovators and 1.6 million scientific papers have been published by researchers, with the majority of all AI-related patent filings published since 2013. Companies represent 26 out of the top 30 AI patent applicants, with universities or public research organizations accounting for the remaining four.[154]?The ratio of scientific papers to inventions has significantly decreased from 8:1 in 2010 to 3:1 in 2016, which is attributed to be indicative of a shift from theoretical research to the use of AI technologies in commercial products and services.?Machine learning?is the dominant AI technique disclosed in patents and is included in more than one-third of all identified inventions (134,777 machine learning patents filed for a total of 167,038 AI patents filed in 2016), with?computer vision?being the most popular functional application. AI-related patents not only disclose AI techniques and applications, they often also refer to an application field or industry. Twenty application fields were identified in 2016 and included, in order of magnitude: telecommunications (15 percent), transportation (15 percent), life and medical sciences (12 percent), and personal devices, computing and human–computer interaction (11 percent). Other sectors included banking, entertainment, security, industry and manufacturing, agriculture, and networks (including social networks, smart cities and the Internet of things). IBM has the largest portfolio of AI patents with 8,290 patent applications, followed by Microsoft with 5,930 patent applications.[154]