Artificial intelligence (AI) is

intelligence Intelligence has been defined in many ways: the capacity for abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. More generally, it can b ...

—perceiving, synthesizing, and inferring information—demonstrated by

machine A machine is a physical system using Power (physics), power to apply Force, forces and control Motion, movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to na ...

s, as opposed to

displayed by

animals Animals are multicellular, eukaryotic organisms in the biological kingdom Animalia. With few exceptions, animals consume organic material, breathe oxygen, are able to move, can reproduce sexually, and go through an ontogenetic stage in ...

and

humans Humans (''Homo sapiens'') are the most abundant and widespread species of primate, characterized by bipedalism and exceptional cognitive skills due to a large and complex brain. This has enabled the development of advanced tools, culture, ...

. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''

Oxford English Dictionary The ''Oxford English Dictionary'' (''OED'') is the first and foundational historical dictionary of the English language, published by Oxford University Press (OUP). It traces the historical development of the English language, providing a com ...

'' of

Oxford University Press Oxford University Press (OUP) is the university press of the University of Oxford. It is the largest university press in the world, and its printing history dates back to the 1480s. Having been officially granted the legal right to print books ...

defines artificial intelligence as:

the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AI applications Artificial intelligence (AI) has been used in applications to alleviate certain problems throughout industry and academia. AI, like electricity or computers, is a general purpose technology that has a multitude of applications. It has been used ...

include advanced

web search Web most often refers to: * Spider web, a silken structure created by the animal * World Wide Web or the Web, an Internet-based hypertext system Web, WEB, or the Web may also refer to: Computing * WEB, a literate programming system created by ...

engines (e.g.,

Google Google LLC () is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. ...

), recommendation systems (used by

YouTube YouTube is a global online video platform, online video sharing and social media, social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by ...

Amazon Amazon most often refers to: * Amazons, a tribe of female warriors in Greek mythology * Amazon rainforest, a rainforest covering most of the Amazon basin * Amazon River, in South America * Amazon (company), an American multinational technology c ...

and

Netflix Netflix, Inc. is an American subscription video on-demand over-the-top streaming service and production company based in Los Gatos, California. Founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California, it offers a fil ...

), understanding human speech (such as

Siri Siri ( ) is a virtual assistant that is part of Apple Inc.'s iOS, iPadOS, watchOS, macOS, tvOS, and audioOS operating systems. It uses voice queries, gesture based control, focus-tracking and a natural-language user interface to answer questio ...

and

Alexa Alexa may refer to: Technology *Amazon Alexa, a virtual assistant developed by Amazon * Alexa Internet, a defunct website ranking and traffic analysis service * Arri Alexa, a digital motion picture camera People *Alexa (name), a given name and ...

self-driving car A self-driving car, also known as an autonomous car, driver-less car, or robotic car (robo-car), is a car that is capable of traveling without human input.Xie, S.; Hu, J.; Bhowmick, P.; Ding, Z.; Arvin, F.,Distributed Motion Planning for S ...

s (e.g., Tesla),

automated decision-making Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with var ...

and competing at the highest level in strategic game systems (such as

chess Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king. It is sometimes called international chess or Western chess to disti ...

and Go). 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 :''For the magnitude of effect of a pesticide, see Pesticide application. Of change in farming practices, see Agricultural intensification.'' The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by argui ...

. For instance,

optical character recognition Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scen ...

is frequently excluded from things considered to be AI, having become a routine technology. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "

AI winter In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. followed by new approaches, success and renewed funding. AI research has tried and discarded many different approaches since its founding, including simulating the brain, modeling human problem solving,

formal logic Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of logical truths. It is a formal science investigating how conclusions follow from premises ...

, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical-statistical

machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia. 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 Reason is the capacity of consciously applying logic by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, lang ...

knowledge representation Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medic ...

planning Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is consi ...

learning Learning is the process of acquiring new understanding, knowledge, behaviors, skills, value (personal and cultural), values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machine learning, machines ...

natural language processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...

perception Perception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system ...

, and the ability to move and manipulate objects.

General intelligence The ''g'' factor (also known as general intelligence, general mental ability or general intelligence factor) is a construct developed in psychometric investigations of cognitive abilities and human intelligence. It is a variable that summarizes ...

(the ability to solve an arbitrary problem) is among the field's long-term goals. 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 network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...

s, and methods based on

statistics Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of ...

probability Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and ...

and

economics Economics () is the social science that studies the Production (economics), production, distribution (economics), distribution, and Consumption (economics), consumption of goods and services. Economics focuses on the behaviour and intera ...

. AI also draws upon

computer science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...

psychology Psychology is the scientific study of mind and behavior. Psychology includes the study of conscious and unconscious phenomena, including feelings and thoughts. It is an academic discipline of immense scope, crossing the boundaries betwe ...

linguistics Linguistics is the scientific study of human language. It is called a scientific study because it entails a comprehensive, systematic, objective, and precise analysis of all aspects of language, particularly its nature and structure. Linguis ...

philosophy Philosophy (from , ) is the systematized study of general and fundamental questions, such as those about existence, reason, knowledge, values, mind, and language. Such questions are often posed as problems to be studied or resolved. Some ...

, 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". 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 Myth is a folklore genre consisting of Narrative, narratives that play a fundamental role in a society, such as foundational tales or Origin myth, origin myths. Since "myth" is widely used to imply that a story is not Objectivity (philosophy), ...

fiction Fiction is any creative work, chiefly any narrative work, portraying individuals, events, or places that are imaginary, or in ways that are imaginary. Fictional portrayals are thus inconsistent with history, fact, or plausibility. In a traditi ...

and

since antiquity.

Computer scientist A computer scientist is a person who is trained in the academic study of computer science. Computer scientists typically work on the theoretical side of computation, as opposed to the hardware side on which computer engineers mainly focus (al ...

s and

philosopher A philosopher is a person who practices or investigates philosophy. The term ''philosopher'' comes from the grc, φιλόσοφος, , translit=philosophos, meaning 'lover of wisdom'. The coining of the term has been attributed to the Greek th ...

s have since suggested that AI may become an

existential risk A global catastrophic risk or a doomsday scenario is a hypothetical future event that could damage human well-being on a global scale, even endangering or destroying modern civilization. An event that could cause human extinction or permanen ...

to humanity if its rational capacities are not steered towards beneficial goals.

History

Artificial being Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fictio ...

s with intelligence appeared as

storytelling device A narrative technique (known for literary fictional narratives as a literary technique, literary device, or fictional device) is any of several specific methods the creator of a narrative uses to convey what they want —in other words, a stra ...

s in antiquity, AI in myth: * * and have been common in fiction, as in

Mary Shelley Mary Wollstonecraft Shelley (; ; 30 August 1797 – 1 February 1851) was an English novelist who wrote the Gothic fiction, Gothic novel ''Frankenstein, Frankenstein; or, The Modern Prometheus'' (1818), which is considered an History of scie ...

's ''

Frankenstein ''Frankenstein; or, The Modern Prometheus'' is an 1818 novel written by English author Mary Shelley. ''Frankenstein'' tells the story of Victor Frankenstein, a young scientist who creates a sapient creature in an unorthodox scientific ex ...

'' or

Karel Čapek Karel Čapek (; 9 January 1890 – 25 December 1938) was a Czech writer, playwright and critic. He has become best known for his science fiction, including his novel ''War with the Newts'' (1936) and play ''R.U.R.'' (''Rossum's Universal Ro ...

's ''

R.U.R. ''R.U.R.'' is a 1920 science-fiction play by the Czech writer Karel Čapek. "R.U.R." stands for (Rossum's Universal Robots, a phrase that has been used as a subtitle in English versions). The play had its world premiere on 2 January 1921 in H ...

'' These characters and their fates raised many of the same issues now discussed in the

ethics of artificial intelligence The ethics of artificial intelligence is the branch of the ethics of technology specific to artificially intelligent systems. It is sometimes divided into a concern with the moral behavior of ''humans'' as they design, make, use and treat artific ...

. The study of mechanical or "formal" reasoning began with

s and mathematicians in antiquity. The study of mathematical logic led directly to

Alan Turing Alan Mathison Turing (; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist. Turing was highly influential in the development of theoretical com ...

theory of computation In theoretical computer science and mathematics, the theory of computation is the branch that deals with what problems can be solved on a model of computation, using an algorithm, how efficiently they can be solved or to what degree (e.g., a ...

, 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 In computability theory, the Church–Turing thesis (also known as computability thesis, the Turing–Church thesis, the Church–Turing conjecture, Church's thesis, Church's conjecture, and Turing's thesis) is a thesis about the nature of comp ...

. This, along with concurrent discoveries in

neurobiology Neuroscience is the scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions and disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, development ...

information theory Information theory is the scientific study of the quantification (science), quantification, computer data storage, storage, and telecommunication, communication of information. The field was originally established by the works of Harry Nyquist a ...

and

cybernetics Cybernetics is a wide-ranging field concerned with circular causality, such as feedback, in regulatory and purposive systems. Cybernetics is named after an example of circular causal feedback, that of steering a ship, where the helmsperson m ...

, led researchers to consider the possibility of building an electronic brain. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for

Turing-complete In computability theory, a system of data-manipulation rules (such as a computer's instruction set, a programming language, or a cellular automaton) is said to be Turing-complete or computationally universal if it can be used to simulate any Tur ...

"artificial neurons". By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as

Symbolic AI In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. S ...

GOFAI GOFAI is an acronym for "Good Old-Fashioned Artificial Intelligence" invented by the philosopher John Haugeland in his 1985 book, ''Artificial Intelligence: The Very Idea''. Technically, GOFAI refers only to a restricted kind of symbolic AI, name ...

, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included

Allen Newell Allen Newell (March 19, 1927 – July 19, 1992) was a researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Department ...

Herbert A. Simon Herbert Alexander Simon (June 15, 1916 – February 9, 2001) was an American political scientist, with a Ph.D. in political science, whose work also influenced the fields of computer science, economics, and cognitive psychology. His primary ...

, and

Marvin Minsky Marvin Lee Minsky (August 9, 1927 – January 24, 2016) was an American cognitive and computer scientist concerned largely with research of artificial intelligence (AI), co-founder of the Massachusetts Institute of Technology's AI laboratory, an ...

. 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 Frank Rosenblatt (July 11, 1928July 11, 1971) was an American psychologist notable in the field of artificial intelligence. He is sometimes called the father of deep learning. Life and career Rosenblatt was born in New Rochelle, New York as son o ...

, sought to connect

Perceptron In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belon ...

in ways inspired by connections of neurons.

James Manyika James M. Manyika is a Zimbabwean-Americans, American academic, consultant, and business executive. He is known for his research and scholarship into the intersection of technology and the economy, including artificial intelligence, robotics autom ...

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 Descarte,

Boole George Boole (; 2 November 1815 – 8 December 1864) was a largely self-taught English mathematician, philosopher, and logician, most of whose short career was spent as the first professor of mathematics at Queen's College, Cork in Irel ...

Gottlob Frege Friedrich Ludwig Gottlob Frege (; ; 8 November 1848 – 26 July 1925) was a German philosopher, logician, and mathematician. He was a mathematics professor at the University of Jena, and is understood by many to be the father of analytic phil ...

Bertrand Russell Bertrand Arthur William Russell, 3rd Earl Russell, (18 May 1872 – 2 February 1970) was a British mathematician, philosopher, logician, and public intellectual. He had a considerable influence on mathematics, logic, set theory, linguistics, ...

, and others. Connectionist approaches based on

s were pushed to the background but have gained new prominence in recent decades. The field of AI research was born at a workshop at

Dartmouth College Dartmouth College (; ) is a private research university in Hanover, New Hampshire. Established in 1769 by Eleazar Wheelock, it is one of the nine colonial colleges chartered before the American Revolution. Although founded to educate Native A ...

in 1956. The attendees became the founders and leaders of AI research. They and their students produced programs that the press described as "astonishing": computers were learning

checkers Checkers (American English), also known as draughts (; British English), is a group of strategy board games for two players which involve diagonal moves of uniform game pieces and mandatory captures by jumping over opponent pieces. Checkers ...

strategies, solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the

Department of Defense Department of Defence or Department of Defense may refer to: Current departments of defence * Department of Defence (Australia) * Department of National Defence (Canada) * Department of Defence (Ireland) * Department of National Defense (Philipp ...

and laboratories had been established around the world. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with

artificial general intelligence Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fictio ...

and considered this the goal of their field. Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".

agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". They had failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the

criticism Criticism is the construction of a judgement about the negative qualities of someone or something. Criticism can range from impromptu comments to a written detailed response. , ''"the act of giving your opinion or judgment about the good or bad q ...

Sir James Lighthill Sir Michael James Lighthill (23 January 1924 – 17 July 1998) was a British applied mathematician, known for his pioneering work in the field of aeroacoustics and for writing the Lighthill report on artificial intelligence. Biography J ...

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 In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. First

AI Winter In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.Lighthill report __NOTOC__ ''Artificial Intelligence: A General Survey'', commonly known as the Lighthill report, is a scholarly article by James Lighthill, published in ''Artificial Intelligence: a paper symposium'' in 1973. Published in 1973, it was compiled by ...

Mansfield Amendment Michael Joseph Mansfield (March 16, 1903 – October 5, 2001) was an American politician and diplomat. A Democrat, he served as a U.S. representative (1943–1953) and a U.S. senator (1953–1977) from Montana. He was the longest-serving Sena ...

* * * * * In the early 1980s, AI research was revived by the commercial success of

expert system In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if� ...

s, 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 The Fifth Generation Computer Systems (FGCS) was a 10-year initiative begun in 1982 by Japan's Ministry of International Trade and Industry (MITI) to create computers using massively parallel computing and logic programming. It aimed to create ...

project inspired the U.S. and British governments to restore funding for

academic research Research is " creative and systematic work undertaken to increase the stock of knowledge". It involves the collection, organization and analysis of evidence to increase understanding of a topic, characterized by a particular attentiveness ...

. Funding initiatives in the early 80s: Fifth Generation Project (Japan),

Alvey The Alvey Programme was a British government sponsored research programme in information technology that ran from 1984 to 1990. The programme was a reaction to the Japanese Fifth Generation project, which aimed to create a computer using massi ...

(UK),

Microelectronics and Computer Technology Corporation Microelectronics and Computer Technology Corporation, originally the Microelectronics and Computer Consortium and widely seen as the acronym MCC, was the first, and at one time one of the largest, computer industry research and development con ...

(US),

Strategic Computing Initiative The United States government's Strategic Computing Initiative funded research into advanced computer hardware and artificial intelligence from 1983 to 1993. The initiative was designed to support various projects that were required to develop ma ...

(US): * * * * * However, beginning with the collapse of the

Lisp Machine Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture, and in a sense, the ...

market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began. Second

AI Winter In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.symbolic approach would be able to imitate all the processes of human cognition, especially

, robotics,

and

pattern recognition Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphi ...

. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.

Robotics Robotics is an interdisciplinary branch of computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist humans. Robotics integrat ...

researchers, such as

Rodney Brooks Rodney Allen Brooks (born 30 December 1954) is an Australian roboticist, Fellow of the Australian Academy of Science, author, and robotics entrepreneur, most known for popularizing the actionist approach to robotics. He was a Panasonic Profes ...

, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, and learn their environment. Interest in

neural networks A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...

and "

connectionism Connectionism refers to both an approach in the field of cognitive science that hopes to explain mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial in ...

" was revived by

Geoffrey Hinton Geoffrey Everest Hinton One or more of the preceding sentences incorporates text from the royalsociety.org website where: (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on ar ...

David Rumelhart David Everett Rumelhart (June 12, 1942 – March 13, 2011) was an American psychologist who made many contributions to the formal analysis of human cognition, working primarily within the frameworks of mathematical psychology, symbolic artif ...

and others in the middle of the 1980s.

Soft computing Soft computing is a set of algorithms, including neural networks, fuzzy logic, and evolutionary algorithms. These algorithms are tolerant of imprecision, uncertainty, partial truth and approximation. It is contrasted with hard computing: al ...

tools were developed in the 1980s, such as

fuzzy system A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, ...

Grey system theory Grey relational analysis (GRA) was developed by Deng Julong of Huazhong University of Science and Technology. It is one of the most widely used models of grey system theory. GRA uses a specific concept of information. It defines situations with no i ...

evolutionary computation In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they ...

and many tools drawn from

mathematical optimization Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...

. 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

and

mathematics Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics ...

). Formal and

narrow Narrow may refer to: * The Narrow, rock band from South Africa * Narrow banking, proposed banking system that would eliminate bank runs and the need for a deposit insurance * narrow gauge railway, a railway that has a track gauge narrower than th ...

methods adopted in the 1990s: * * By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence". AI widely used in late 1990s: * * * * Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in

and perception; data-hungry

deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. De ...

methods started to dominate accuracy benchmarks around 2012. According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within

increased from a "sporadic usage" in 2012 to more than 2,700 projects. He attributes this to an increase in affordable

, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes". The amount of research into AI (measured by total publications) increased by 50% in the years 2015–2019. 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

. This concern has led to the subfield of

(or "AGI"), which had several well-funded institutions by the 2010s. ;

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.

Reasoning, problem-solving

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

and

. 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. Intractability and efficiency and the

combinatorial explosion In mathematics, a combinatorial explosion is the rapid growth of the complexity of a problem due to how the combinatorics of the problem is affected by the input, constraints, and bounds of the problem. Combinatorial explosion is sometimes used ...

: * Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Psychological evidence of the prevalence sub-symbolic reasoning and knowledge: * * * *

Knowledge representation

Knowledge representation and

knowledge engineering Knowledge engineering (KE) refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems. Background Expert systems One of the first examples of an expert system was MYCIN, an appl ...

allow AI programs to answer questions intelligently and make deductions about real-world facts. A representation of "what exists" is an

ontology In metaphysics, ontology is the philosophical study of being, as well as related concepts such as existence, becoming, and reality. Ontology addresses questions like how entities are grouped into categories and which of these entities exis ...

: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. 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 Domain may refer to: Mathematics *Domain of a function, the set of input values for which the (total) function is defined **Domain of definition of a partial function **Natural domain of a partial function **Domain of holomorphy of a function * Do ...

(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 Semantics (from grc, σημαντικός ''sēmantikós'', "significant") is the study of reference, meaning, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy Philosophy (f ...

of an ontology is typically represented in description logic, such as the

Web Ontology Language The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for variou ...

. AI research has developed tools to represent specific domains, 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 Default logic is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions. Default logic can express facts like “by default, something is true”; by contrast, standard logic can only express that somethin ...

(things that humans assume are true until they are told differently and will remain true even when other facts are changing); 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); Breadth of commonsense knowledge: * , * , * , * 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). Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.

Learning

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

Unsupervised learning Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and t ...

finds patterns in a stream of input.

Supervised learning Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning alg ...

requires a human to label the input data first, and comes in two main varieties:

classification Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood. Classification is the grouping of related facts into classes. It may also refer to: Business, organizat ...

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". In

reinforcement learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

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.

Transfer learning Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize ...

is when the knowledge gained from one problem is applied to a new problem.

Computational learning theory In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms. Overview Theoretical results in machine learning m ...

can assess learners by

computational complexity In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) ...

, by

sample complexity The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. More precisely, the sample complexity is the number of training-samples that we need to ...

(how much data is required), or by other notions of

optimization Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...

Natural language processing

Natural language processing (NLP) allows machines to read and

understand Understanding is a psychological process related to an abstract or physical object, such as a person, situation, or message whereby one is able to use concepts to model that object. Understanding is a relation between the knower and an object of ...

human language. A sufficiently powerful natural language processing system would enable

natural-language user interface Natural-language user interface (LUI or NLUI) is a type of computer human interface where linguistic phenomena such as verbs, phrases and clauses act as UI controls for creating, selecting and modifying data in software applications. In interface d ...

s and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include

information retrieval Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other co ...

question answering Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural l ...

and

machine translation Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation), is a sub-field of computational linguistics that investigates t ...

used formal

syntax In linguistics, syntax () is the study of how words and morphemes combine to form larger units such as phrases and sentences. Central concerns of syntax include word order, grammatical relations, hierarchical sentence structure ( constituency) ...

to translate the

deep structure Deep structure and surface structure (also D-structure and S-structure although those abbreviated forms are sometimes used with distinct meanings) are concepts used in linguistics, specifically in the study of syntax in the Chomskyan tradition of t ...

of sentences into

logic Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of logical truths. It is a formal science investigating how conclusions follow from premises ...

. This failed to produce useful applications, due to the intractability of logic and the breadth of commonsense knowledge. 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 A transformer is a passive component that transfers electrical energy from one electrical circuit to another circuit, or multiple circuits. A varying current in any coil of the transformer produces a varying magnetic flux in the transformer' ...

-based

(which finds patterns in text), and others. They have achieved acceptable accuracy at the page or paragraph level, and, by 2019, could generate coherent text.

Perception

Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, and active

lidar Lidar (, also LIDAR, or LiDAR; sometimes LADAR) is a method for determining ranges (variable distance) by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver. It can also be ...

, sonar, radar, and

tactile sensor A tactile sensor is a device that measures information arising from physical interaction with its environment. Tactile sensors are generally modeled after the biological sense of cutaneous receptor, cutaneous touch which is capable of detecti ...

s) to deduce aspects of the world. Applications include

speech recognition Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the m ...

, facial recognition, and

object recognition Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the ...

. Computer vision is the ability to analyze visual input.

Social intelligence

Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood. For example, some

virtual assistant An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term "chatbot" is sometimes used to refer to virtual ...

s are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate

human–computer interaction Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design tec ...

. However, this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are. Moderate successes related to affective computing include textual

sentiment analysis Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjec ...

and, more recently,

multimodal sentiment analysis Multimodal sentiment analysis is a new dimension of the traditional text-based sentiment analysis, which goes beyond the analysis of texts, and includes other modalities such as audio and visual data. It can be bimodal, which includes different com ...

), wherein AI classifies the affects displayed by a videotaped subject.

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 Hans Peter Moravec (born November 30, 1948, Kautzen, Austria) is an adjunct faculty member at the Robotics Institute of Carnegie Mellon University in Pittsburgh, USA. He is known for his work on robotics, artificial intelligence, and writings on ...

and

argue that work in different individual domains can be incorporated into an advanced

multi-agent system A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...

cognitive architecture A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. The formalized mod ...

with general intelligence.

Pedro Domingos Pedro Domingos is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning known for Markov logic network enabling uncertain inference. Education Domingos received an und ...

hopes that there is a conceptually straightforward, but mathematically difficult, " master algorithm" that could lead to AGI. Others believe that

anthropomorphic Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. It is considered to be an innate tendency of human psychology. Personification is the related attribution of human form and characteristics t ...

features like an artificial brain or simulated

child development Child development involves the Human development (biology), biological, developmental psychology, psychological and emotional changes that occur in human beings between birth and the conclusion of adolescence. Childhood is divided into 3 stages o ...

will someday reach a critical point where general intelligence emerges.

Tools

Search and optimization

AI can solve many problems by intelligently searching through many possible solutions.

Reasoning Reason is the capacity of consciously applying logic by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, lang ...

can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from

premise A premise or premiss is a true or false statement that helps form the body of an argument, which logically leads to a true or false conclusion. A premise makes a declarative statement about its subject matter which enables a reader to either agre ...

s to conclusions, where each step is the application of an

inference rule In the philosophy of logic, a rule of inference, inference rule or transformation rule is a logical form consisting of a function which takes premises, analyzes their syntax, and returns a conclusion (or conclusions). For example, the rule of in ...

Forward chaining Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of ''modus ponens''. Forward chaining is a popular implementation strategy fo ...

backward chaining Backward chaining (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence application ...

Horn clause In mathematical logic and logic programming, a Horn clause is a logical formula of a particular rule-like form which gives it useful properties for use in logic programming, formal specification, and model theory. Horn clauses are named for the log ...

s, and logical deduction as search: * * * *

Planning Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is consi ...

algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.

State space search State space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or ''states'' of an instance are considered, with the intention of finding a ''goal state'' with the ...

and

: * * *

algorithms for moving limbs and grasping objects use local searches in configuration space.Moving and configuration space: * Simple exhaustive searchesUninformed searches (

breadth first search Breadth-first search (BFS) is an algorithm for searching a tree data structure for a node that satisfies a given property. It starts at the tree root and explores all nodes at the present depth prior to moving on to the nodes at the next de ...

depth-first search Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible alon ...

and general

state space search State space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or ''states'' of an instance are considered, with the intention of finding a ''goal state'' with the ...

): * * * * 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 A heuristic (; ), or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, ...

" 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 Pruning is a horticultural, arboricultural, and silvicultural practice involving the selective removal of certain parts of a plant, such as branches, buds, or roots. The practice entails the ''targeted'' removal of diseased, damaged, dead, ...

the

search tree In computer science, a search tree is a tree data structure used for locating specific keys from within a set. In order for a tree to function as a search tree, the key for each node must be greater than any keys in subtrees on the left, and less ...

"). Heuristics supply the program with a "best guess" for the path on which the solution lies.

Heuristic A heuristic (; ), or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, ...

or informed searches (e.g., greedy best first and A*): * * * * Heuristics limit the search for solutions into a smaller sample size. ParticleSwarmArrowsAnimation

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of

. 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 numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution ...

: 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 Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not continuous or differentiable. Such optimization methods are al ...

beam search In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is an optimization of best-first search that reduces its memory requirements. Best-first se ...

and

metaheuristics In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimiza ...

simulated annealing Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It ...

.Optimization (mathematics), Optimization searches: * * * 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, Artificial selection, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Genetic programming and genetic algorithms: * * 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 (behavior), flocking) and ant colony optimization (inspired by ant trails). Artificial life and society based learning: * *

Logic

Logic Logic: * , * , * 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

Satplan: * , * , * and inductive logic programming is a method for Machine learning, learning. Explanation based learning, relevance based learning, inductive logic programming, case based reasoning: * , * , * , * Several different forms of logic are used in AI research. Propositional logic Propositional logic: * , * * involves truth functions such as "or" and "not". First-order logicFirst-order logic and features such as Equality (mathematics), equality: * , * , * , * adds Quantifier (logic), quantifiers and Predicate (mathematical logic), 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. Fuzzy logic: * * Default logics, non-monotonic logics and Circumscription (logic), circumscription are forms of logic designed to help with default reasoning and the qualification problem. Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription (logic), circumscription, closed world assumption, abductive reasoning, abduction: * * * * (Poole ''et al.'' places abduction under "default reasoning". Luger ''et al.'' places this under "uncertain reasoning"). Several extensions of logic have been designed to handle specific domains of Knowledge representation, knowledge, such as description logics; Representing categories and relations: Semantic networks, description logics, Inheritance (computer science), inheritance (including Frame (artificial intelligence), frames and Scripts (artificial intelligence), scripts): * , * , * , * situation calculus, event calculus and fluent calculus (for representing events and time);Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * , * , * Causality#Causal calculus, causal calculus; Causality#Causal calculus, Causal calculus: * Belief revision, belief calculus (belief revision); and modal logics. Representing knowledge about knowledge: Belief calculus, modal logics: * , * Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics.

Probabilistic methods for uncertain reasoning

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

theory and economics. Bayesian networks Bayesian networks: * , * , * , * are a very general tool that can be used for various problems, including reasoning (using the Bayesian inference algorithm), Bayesian inference algorithm: * , * , * , * Machine learning, learning (using the expectation-maximization algorithm), Bayesian learning and the expectation-maximization algorithm: * , * , * *

(using decision networks)Bayesian decision theory and Bayesian decision networks: * and Machine perception, perception (using dynamic Bayesian networks).Stochastic temporal models: * Dynamic Bayesian networks: * Hidden Markov model: * Kalman filters: * Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping Machine perception, perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). 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, decision theory and decision analysis: * , * and information value theory. Information value theory: * These tools include models such as Markov decision processes,Markov decision processes and dynamic decision networks: * dynamic decision networks, game theory and mechanism design.Game theory and mechanism design: *

Classifiers and statistical learning methods

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. Classifier (mathematics), 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. A classifier can be trained in various ways; there are many statistical and

approaches. The Decision tree learning, decision tree is the simplest and most widely used symbolic machine learning algorithm. K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s. Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s. The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability. Artificial neural network, Neural networks are also used for classification. 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.

Artificial neural networks

artificial neural network, Neural networks Neural networks: * , * , * , * * 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 "Hebbian learning, 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 Pattern recognition, find patterns in data. They can learn continuous functions and even digital logical operations. Neural networks can be viewed as a type of

– they perform gradient descent on a multi-dimensional topology that was created by Machine learning, training the network. The most common training technique is the backpropagation algorithm. Backpropagation: * , * , * Paul Werbos' introduction of backpropagation to AI: * ; Automatic differentiation, an essential precursor: * ; Other Machine learning, learning techniques for neural networks are Hebbian learning ("fire together, wire together"), GMDH or competitive learning. Competitive learning, Hebbian theory, Hebbian coincidence learning, Hopfield networks and attractor networks: * 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. Feedforward neural networks, perceptrons and radial basis networks: * *

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, 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. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision,

, image classification 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, and creates a hierarchy similar to the organization of the animal visual cortex. In a recurrent neural network (RNN) the signal will propagate through a layer more than once; thus, an RNN is an example of deep learning. RNNs can be trained by gradient descent, 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. The long short term memory (LSTM) technique can prevent this in most cases.

Specialized languages and hardware

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

Applications

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

. 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), targeted advertising, targeting online advertisements, recommendation systems (offered by

), driving internet traffic, marketing and artificial intelligence, targeted advertising (AdSense, Facebook),

s (such as

), autonomous vehicles (including unmanned aerial vehicle, drones and self-driving cars), machine translation, automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple Computer, Apple's Face ID or Microsoft's DeepFace), automatic image annotation, image labeling (used by Facebook, Apple Computer, Apple's iPhoto and TikTok) and spam filtering. There are also thousands of successful AI applications used to solve problems for specific industries or institutions. A few examples are energy storage, deepfakes, medical diagnosis, military logistics, or supply chain management. Game AI, Game playing has been a test of AI's strength since the 1950s. IBM Deep Blue, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a ''Jeopardy!'' quiz show exhibition match, IBM's question answering system, Watson (artificial intelligence software), Watson, defeated the two greatest ''Jeopardy!'' champions, Brad Rutter and Ken Jennings, by a significant margin. 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 Go handicaps, handicaps. Other programs handle Imperfect information, imperfect-information games; such as for poker at a superhuman level, Pluribus (poker bot), Pluribus and Cepheus (poker bot), Cepheus. DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari 2600, Atari games on its own. 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. DeepMind's AlphaFold 2 (2020) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein. Other applications predict the result of judicial decisions, computer art, create art (such as poetry or painting) and automated theorem prover, prove mathematical theorems. AI Patent families for functional application categories and sub categories

AI Patent families for functional application categories and sub categories

Intellectual Property

In 2019, WIPO reported that AI was the most prolific emerging technologies, emerging technology in terms of 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). 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. 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.

Philosophy

Defining artificial intelligence

wrote 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. Turing's original publication of the Turing test in "Computing machinery and intelligence": * Historical influence and philosophical implications: * * * * Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks" Stuart J. Russell, Russell and Peter Norvig, Norvig agree with Turing that AI must be defined in terms of "acting" and not "thinking". However, they are critical that the test compares machines to ''people''. "Aeronautics, Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons. AI founder John McCarthy (computer scientist), John McCarthy agreed, 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,

similarly defines it as "the ability to solve hard problems". 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. A definition that has also been adopted by Google - major practitionary in the field of AI. This definition stipulated the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.

Evaluating approaches to AI

No established unifying theory or paradigm has guided AI research for most of its history. The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, neats and scruffies, neat, soft computing, soft and artificial general intelligence, narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers.

Symbolic AI and its limits

(or "

") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." Physical symbol system hypothesis: * Historical significance: * * However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult. Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge. Dreyfus' critique of AI: * * Historical significance and philosophical implications: * * * * Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree. The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence, in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neurosymbolic artificial intelligence attempts to bridge the two approaches.

Neat vs. scruffy

"Neats" hope that intelligent behavior is described using simple, elegant principles (such as

, optimization (mathematics), optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems (especially in areas like common sense reasoning). This issue was actively discussed in the 70s and 80s, Neats vs. scruffies, the historic debate: * * * A classic example of the "scruffy" approach to intelligence: * A modern example of neat AI and its aspirations: * but in the 1990s mathematical methods and solid scientific standards became the norm, a transition that Russell and Norvig termed "the victory of the neats".

Soft vs. hard computing

Finding a provably correct or optimal solution is Intractability (complexity), intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 80s and most successful AI programs in the 21st century are examples of soft computing with neural networks.

Narrow vs. general AI

AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence (general AI) directly or to solve as many specific problems as possible (Weak artificial intelligence, narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. 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

The philosophy of mind does not know whether a machine can have a mind, consciousness and philosophy of mind, mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field. Stuart J. Russell, Stuart Russell and Peter Norvig observe that most AI researchers "don't care about the [philosophy of AI] – as long as the program works, they don't care whether you call it a simulation of intelligence or real intelligence." However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction.

Consciousness

David Chalmers identified 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 this ''feels'' or why it should feel like anything at all. Human information processing is easy to explain, however, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to ''know what red looks like''.

Computationalism and functionalism

Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam. Philosopher John Searle characterized this position as strong AI hypothesis, "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." 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. Searle's Chinese room argument: * . Searle's original presentation of the thought experiment. * . Discussion: * * *

Robot rights

If a machine has a mind and subjective experience, then it may also have sentience (the ability to feel), and if so, then it could also ''suffer'', and thus it would be entitled to certain rights. Any hypothetical robot rights would lie on a spectrum with animal rights and human rights. This issue has been considered in artificial intelligence in fiction, fiction for centuries, and is now being considered by, for example, California's Institute for the Future; however, critics argue that the discussion is premature.

Future

Superintelligence

A superintelligence, hyperintelligence, or superhuman intelligence, is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. ''Superintelligence'' may also refer to the form or degree of intelligence possessed by such an agent. If research into

produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to Intelligence explosion, recursive self-improvement. Its intelligence would increase exponentially in an intelligence explosion and could dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario the "singularity". Because it is difficult or impossible to know the limits of intelligence or the capabilities of superintelligent machines, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable. Robot designer

, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger. Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler (novelist), Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson (science historian), George Dyson in his book of the same name in 1998.

Risks

Technological unemployment

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are Redistribution of income and wealth, redistributed. Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk". Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''The Economist'' states 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 from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.

Bad actors and weaponized AI

AI provides a number of tools that are particularly useful for authoritarian governments: smart spyware, Facial recognition system, face recognition and Speaker recognition, voice recognition allow widespread surveillance; such surveillance allows

to classifier (machine learning), classify potential enemies of the state and can prevent them from hiding; recommendation systems can precisely target propaganda and misinformation for maximum effect; deepfakes aid in producing misinformation; advanced AI can make technocracy, centralized decision making more competitive with liberal and decentralized systems such as markets. Terrorists, criminals and rogue states may use other forms of weaponized AI such as advanced digital warfare and lethal autonomous weapons. By 2015, over fifty countries were reported to be researching battlefield robots. Machine-learning AI is also able to design tens of thousands of toxic molecules in a matter of hours.

Algorithmic bias

AI programs can become biased after learning from real-world data. It is not typically introduced by the system designers but is learned by the program, and thus the programmers are often unaware that the bias exists. Bias can be inadvertently introduced by the way training data is selected. It can also Algorithmic bias#Emergent, emerge from correlations: AI is used to statistical classifier, classify individuals into groups and then make predictions assuming that the individual will resemble other members of the group. In some cases, this assumption may be unfair. An example of this is COMPAS (software), COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. ProPublica claims that the COMPAS-assigned recidivism risk level of black defendants is far more likely to be overestimated than that of white defendants, despite the fact that the program was not told the races of the defendants. Other examples where algorithmic bias can lead to unfair outcomes are when AI is used for credit rating or Recruitment, hiring. At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the Association for Computing Machinery, in Seoul, South Korea, presented and published findings recommending that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.

Existential risk

Superintelligent AI may be able to improve itself to the point that humans could not control it. This could, as physicist Stephen Hawking puts it, "Global catastrophic risk, spell the end of the human race". Philosopher Nick Bostrom argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit Instrumental convergence, convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI's goals do not fully reflect humanity's, it might need to harm humanity to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. He concludes that AI poses a risk to mankind, however humble or "Friendly AI, friendly" its stated goals might be. Political scientist Charles T. Rubin argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no ''a priori'' reason to believe that they would share our system of morality. The opinion of experts and industry insiders is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. Stephen Hawking, Microsoft founder Bill Gates, history professor Yuval Noah Harari, and SpaceX founder Elon Musk have all expressed serious misgivings about the future of AI. Prominent tech titans including Peter Thiel (Amazon Web Services) and Musk have committed more than $1 billion to nonprofit companies that champion responsible AI development, such as OpenAI and the Future of Life Institute. Mark Zuckerberg (CEO, Facebook) has said that artificial intelligence is helpful in its current form and will continue to assist humans. Other experts argue is that the risks are far enough in the future to not be worth researching, or that humans will be valuable from the perspective of a superintelligent machine.

, in particular, has said that "malevolent" AI is still centuries away.

Copyright

AI's decisions making abilities raises the questions of legal responsibility and copyright status of created works. This issues are being refined in various jurisdictions.

Ethical machines

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. Machine ethics is also called machine morality, computational ethics or computational morality, and was founded at an AAAI symposium in 2005. Other approaches include Wendell Wallach's "artificial moral agents" and Stuart J. Russell's Human Compatible#Russell's three principles, three principles for developing provably beneficial machines.

Regulation

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. 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, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel P. Huttenlocher, Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI.

In fiction

Thought-capable artificial beings have appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction. A common Trope (literature), trope in these works began with

's ''

'', where a human creation becomes a threat to its masters. This includes such works as 2001: A Space Odyssey (novel), Arthur C. Clarke's and 2001: A Space Odyssey (film), Stanley Kubrick's ''2001: A Space Odyssey'' (both 1968), with HAL 9000, the murderous computer in charge of the ''Discovery One'' spaceship, as well as ''The Terminator'' (1984) and ''The Matrix'' (1999). In contrast, the rare loyal robots such as Gort from ''The Day the Earth Stood Still'' (1951) and Bishop from ''Aliens (film), Aliens'' (1986) are less prominent in popular culture. Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics; 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. Transhumanism (the merging of humans and machines) is explored in the manga ''Ghost in the Shell'' and the science-fiction series ''Dune (novel), Dune''. Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have sentience, the ability to feel, and thus to suffer. This appears in

's ''

'', the films ''A.I. Artificial Intelligence'' and ''Ex Machina (film), Ex Machina'', as well as the novel ''Do Androids Dream of Electric Sheep?'', by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.

Scientific diplomacy

Warfare

As technology and research evolve and the world enters the third revolution of warfare following gunpowder and nuclear weapons, the artificial intelligence arms race ensues between the United States, China, and Russia, three countries with the world's top five highest military budgets. Intentions of being a world leader in AI research by 2030 have been declared by China's leader Xi Jinping, and President Putin of Russia has stated that "Whoever becomes the leader in this sphere will become the ruler of the world". If Russia were to become the leader in AI research, President Putin has stated Russia's intent to share some of their research with the world so as to not monopolize the field, similar to their current sharing of nuclear technologies, maintaining science diplomacy relations. The United States, China, and Russia, are some examples of countries that have taken their Artificial intelligence arms race#Stances toward military artificial intelligence, stances toward military artificial intelligence since as early as 2014, having established military programs to develop cyber weapons, control lethal autonomous weapons, and drones that can be Artificial intelligence for video surveillance, used for surveillance.

Russo-Ukrainian War

President Putin announced that artificial intelligence is the future for all mankind and recognizes the power and opportunities that the development and deployment of lethal autonomous weapons AI technology can hold in warfare and homeland security, as well as its threats. President Putin's prediction that future wars will be fought using AI has started to come to fruition to an extent after 2022 Russian invasion of Ukraine, Russia invaded Ukraine on 24 February 2022. The Ukrainian military is making use of the Turkish Baykar Bayraktar TB2, Bayraktar TB2-drones that still require human operation to deploy laser-guided bombs but can take off, land, and cruise autonomously. Ukraine has also been using Switchblade drones supplied by the US and receiving information gathering by the United States's own surveillance operations regarding battlefield intelligence and national security about Russia. Similarly, Russia can use AI to help analyze battlefield data from surveillance footage taken by drones. Reports and images show that Russia's military has deployed KUB- BLA suicide drones into Ukraine, with speculations of intentions to assassinate Ukrainian President Volodymyr Zelenskyy.

Warfare regulations

As research in the AI realm progresses, there is pushback about the use of AI from the Campaign to Stop Killer Robots and world technology leaders have sent a petition to the United Nations calling for new regulations on the development and use of AI technologies in 2017, including a ban on the use of lethal autonomous weapons due to ethical concerns for innocent civilian populations.

Cybersecurity

With the ever evolving cyber-attacks and generation of devices, AI can be used for threat detection and more effective response by risk prioritization. With this tool, some challenges are also presented such as privacy, informed consent, and responsible use. According to Cybersecurity and Infrastructure Security Agency, CISA, the cyberspace is difficult to secure for the following factors: the ability of malicious actors to operate from anywhere in the world, the linkages between cyberspace and physical systems, and the difficulty of reducing vulnerabilities and consequences in complex cyber networks. With the increased technological advances of the world, the risk for wide scale consequential events rises. Paradoxically, the ability to protect information and create a line of communication between the scientific and diplomatic community thrives. The role of cybersecurity in diplomacy has become increasingly relevant, creating the term of United States cyber-diplomacy, cyber diplomacy – which is not uniformly defined and not synonymous with cyber defence. Many nations have developed unique approaches to scientific diplomacy in cyberspace.

Czech Republic's approach

Dating back to 2011, when the Czech National Security Authority (Czech Republic), National Security Authority (NSA) was appointed as the national authority for the cyber agenda. The role of cyber diplomacy strengthened in 2017 when the Ministry of Foreign Affairs (Czech Republic), Czech Ministry of Foreign Affairs (MFA) detected a serious cyber campaign directed against its own computer networks. In 2016, three cyber diplomats were deployed to Washington, D.C., Brussels and Tel Aviv, with the goal of establishing active international cooperation focused on engagement with the European Union, EU and NATO. The main agenda for these scientific diplomacy efforts is to bolster research on artificial intelligence and how it can be used in cybersecurity research, development, and overall consumer trust. CzechInvest is a key stakeholder in scientific diplomacy and cybersecurity. For example, in September 2018, they organized a mission to Canada in September 2018 with a special focus on artificial intelligence. The main goal of this particular mission was a promotional effort on behalf of Prague, attempting to establish it as a future knowledge hub for the industry for interested Canadian firms.

Germany's approach

Cybersecurity is recognized as a governmental task, dividing into three ministries of responsibility: the Federal Ministry of the Interior, the Federal Ministry of Defence, and the Federal Foreign Office. These distinctions promoted the creation of various institutions, such as The German National Office for Information Security, The National Cyberdefence Centre, The German National Cyber Security Council, and The Cyber and Information Domain Service. In 2018, a new strategy for artificial intelligence was established by the German government, with the creation of a German-French virtual research and innovation network, holding opportunity for research expansion into cybersecurity.

European Union's approach

The adoption of ''The Cybersecurity Strategy of the European Union – An Open, Safe and Secure Cyberspace'' document in 2013 by the European commission pushed forth cybersecurity efforts integrated with scientific diplomacy and artificial intelligence. Efforts are strong, as the EU funds various programs and institutions in the effort to bring science to diplomacy and bring diplomacy to science. Some examples are the cyber security programme Competence Research Innovation (CONCORDIA), which brings together 14 member states, and Cybersecurity for Europe (CSE), which brings together 43 partners involving 20 member states. In addition, The European Network of Cybersecurity Centres and Competence Hub for Innovation and Operations (ECHO) gathers 30 partners with 15 member states and SPARTA gathers 44 partners involving 14 member states. These efforts reflect the overall goals of the EU, to innovate cybersecurity for defense and protection, establish a highly integrated cyberspace among many nations, and further contribute to the security of artificial intelligence.

Russo-Ukrainian War

With the 2022 invasion of Ukraine, there has been a rise in malicious cyber activity against the United States, Ukraine, and Russia. A prominent and rare documented use of artificial intelligence in conflict is on behalf of Ukraine, using facial recognition software to uncover Russian assailants and identify Ukrainians killed in the ongoing war. Though these governmental figures are not primarily focused on scientific and cyber diplomacy, other institutions are commenting on the use of artificial intelligence in cybersecurity with that focus. For example, Georgetown University's Center for Security and Emerging Technology (CSET) has the Cyber-AI Project, with one goal being to attract policymakers' attention to the growing body of academic research, which exposes the exploitive consequences of AI and machine-learning (ML) algorithms. This vulnerability can be a plausible explanation as to why Russia is not engaging in the use of AI in conflict per, Andrew Lohn, a senior fellow at CSET. In addition to use on the battlefield, AI is being used by the Pentagon to analyze data from the war, analyzing to strengthen cybersecurity and warfare intelligence for the United States.

Election security

As artificial intelligence grows and the overwhelming amount of news portrayed through cyberspace expands, it is becoming extremely overwhelming for a voter to know what to believe. There are many intelligent codes, referred to as bots, written to portray people on social media with the goal of spreading misinformation. The 2016 US election is a victim of such actions. During the Hillary Clinton and Donald Trump campaign, artificial intelligent bots from Russia were spreading misinformation about the candidates in order to help the Trump campaign. Analysts concluded that approximately 19% of Twitter tweets centered around the 2016 election were detected to come from bots.

in recent years has been used to spread political information as well. Although there is no proof that the platform attempts to manipulate its viewers opinions, Youtubes AI algorithm recommends videos of similar variety. If a person begins to research Right-wing politics, right wing political podcasts, then YouTube's algorithm will recommend more right wing videos. The uprising in a program called Deepfake, a software used to replicate someone's face and words, has also shown its potential threat. In 2018 a Deepfake video of Barack Obama was released saying words he claims to have never said. While in a national election a Deepfake will quickly be debunked, the software has the capability to heavily sway a smaller local election. This tool holds a lot of potential for spreading misinformation and is monitored with great attention. Although it may be seen as a tool used for harm, AI can help enhance election campaigns as well. AI bots can be programed to target articles with known misinformation. The bots can then indicate what is being misinformed to help shine light on the truth. AI can also be used to inform a person where each parts stands on a certain topic such as Health care, healthcare or climate change. The political leaders of a nation have heavy sway on international affairs. Thus, a political leader with a lack of interest for international collaborative scientific advancement can have a negative impact in the scientific diplomacy of that nation

Future of work

Facial recognition

The use of artificial intelligence (AI) has subtly grown to become part of everyday life. It is used every day in Facial recognition system, facial recognition software. It is the first measure of security for many companies in the form of a biometric authentication. This means of authentication allows even the most official organizations such as the United States Internal Revenue Service to verify a person's identity via a database generated from machine learning. As of the year 2022, the United States IRS requires those who do not undergo a live interview with an agent to complete a biometric verification of their identity via ID.me's facial recognition tool.

AI and school

In Japan and South Korea, artificial intelligence software is used in the instruction of English language via the company Riiid. Riiid is a Korean education company working alongside Japan to give students the means to learn and use their English communication skills via engaging with artificial intelligence in a live chat. Riid is not the only company to do this. American company Duolingo is well known for their automated teaching of 41 languages. Babbel, a German language learning program, also uses artificial intelligence in its teaching automation, allowing for European students to learn vital communication skills needed in social, economic, and diplomatic settings. Artificial intelligence will also automate the routine tasks that teachers need to do such as grading, taking attendance, and handling routine student inquiries. This enables the teacher to carry on with the complexities of teaching that an automated machine cannot handle. These include creating exams, explaining complex material in a way that will benefit students individually and handling unique questions from students.

AI and medicine

Unlike the human brain, which possess generalized intelligence, the specialized intelligence of AI can serve as a means of support to physicians internationally. The medical field has a diverse and profound amount of data in which AI can employ to generate a predictive diagnosis. Researchers at an Oxford hospital have developed artificial intelligence that can diagnose heart scans for heart disease and cancer. This artificial intelligence can pick up diminutive details in the scans that doctors may miss. As such, artificial intelligence in medicine will better the industry, giving doctors the means to precisely diagnose their patients using the tools available. The artificial intelligence algorithms will also be used to further improve diagnosis over time, via an application of machine learning called precision medicine. Furthermore, the narrow application of artificial intelligence can use "

" in order to improve medical image analysis. In radiology imaging, AI uses deep learning algorithms to identify potentially cancerous lesions which is an important process assisting in early diagnosis.

AI in business

Data analysis is a fundamental property of artificial intelligence that enables it to be used in every facet of life from search results to the way people buy product. According to NewVantage Partners, over 90% of top businesses have ongoing investments in artificial intelligence. According to IBM, one of the world's leaders in technology, 45% of respondents from companies with over 1,000 employees have adopted AI. Recent data shows that the business market for artificial intelligence during the year 2020 was valued at $51.08 billion. The business market for artificial intelligence is projected to be over $640.3 billion by the year 2028. To prevent harm, AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and take accountability to mitigate the risks.

Business and diplomacy

With the exponential surge of artificial technology and communication, the distribution of one's ideals and values has been evident in daily life. Digital information is spread via communication apps such as Whatsapp, Facebook/Meta, Snapchat, Instagram and Twitter. However, it is known that these sites relay specific information corresponding to data analysis. If a right-winged individual were to do a google search, Google's algorithms would target that individual and relay data pertinent to that target audience. US President Bill Clinton noted in 2000:"In the new century, liberty will spread by cell phone and cable modem. [...] We know how much the Internet has changed America, and we are already an open society. However, when the private sector uses artificial intelligence to gather data, a shift in power from the state to the private sector may be seen. This shift in power, specifically in large technological corporations, could profoundly change how diplomacy functions in society. The rise in digital technology and usage of artificial technology enabled the private sector to gather immense data on the public, which is then further categorized by race, location, age, gender, etc. ''The New York Times'' calculates that "the ten largest tech firms, which have become gatekeepers in commerce, finance, entertainment and communications, now have a combined market capitalization of more than $10 trillion. In gross domestic product terms, that would rank them as the world's third-largest economy." Beyond the general lobbying of congressmen/congresswomen, companies such as Facebook/Meta or Google use collected data in order to reach their intended audiences with targeted information.

AI and foreign policy

Multiple nations around the globe employ artificial intelligence to assist with their foreign policy decisions. The Chinese Department of External Security Affairs – under the Ministry of Foreign Affairs – uses AI to review almost all its foreign investment projects for risk mitigation. The government of China plans to use artificial intelligence in its $900 billion global infrastructure development plan, called the "Belt and Road Initiative" for political, economic, and environmental risk alleviation. Over 200 applications of artificial intelligence are being used by over 46 United Nations agencies, in sectors ranging from health care dealing with issues such as combating COVID-19 to smart agriculture, to assist the UN in political and diplomatic relations. One example is the use of AI by the UN Global Pulse program to model the effect of the spread of COVID-19 on internally displaced people (IDP) and refugee settlements to assist them in creating an appropriate global health policy. Novel AI tools such as remote sensing can also be employed by diplomats for collecting and analyzing data and near-real-time tracking of objects such as troop or refugee movements along borders in violent conflict zones. Artificial intelligence can be used to mitigate vital cross-national diplomatic talks to prevent translation errors caused by human translators. A major example is the 2021 Anchorage meetings held between US and China aimed at stabilizing foreign relations, only for it to have the opposite effect, increasing tension and aggressiveness between the two nations, due to translation errors caused by human translators. In the meeting, when United States National Security Advisor to President Joe Biden, Jacob Jeremiah Sullivan stated, "We do not seek conflict, but we welcome stiff competition and we will always stand up for our principles, for our people, and for our friends", it was mistranslated into Chinese as "we will face competition between us, and will present our stance in a very clear manner", adding an aggressive tone to the speech. AI's ability for fast and efficient natural language processing and real-time translation and transliteration makes it an important tool for foreign-policy communication between nations and prevents unintended mistranslation.

Explanatory notes

References

AI textbooks

These were the four the most widely used AI textbooks in 2008: * * * . * Later editions. * . * The two most widely used textbooks in 202
Open Syllabus: Explorer
* *

History of AI

* . * . * *

Other sources

* * * * * * was introduced by Kunihiko Fukushima in 1980. * * * * * * * * * * * * * * * * * * * * * * * , * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * . * Presidential Address to the Association for the Advancement of Artificial Intelligence. * * * * * * Later published as
* * * * * . * * * *

External links

* *
Artificial Intelligence
BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (''In Our Time'', 8 December 2005). {{Authority control Artificial intelligence, Cybernetics Formal sciences Data science Computational neuroscience Emerging technologies Unsolved problems in computer science Computational fields of study

History

Goals

Reasoning, problem-solving

Knowledge representation

Learning

Natural language processing

Perception

Social intelligence

General intelligence

Tools

Search and optimization

Logic

Probabilistic methods for uncertain reasoning

Classifiers and statistical learning methods

Artificial neural networks

Deep learning

Specialized languages and hardware

Applications

Intellectual Property

Philosophy

Defining artificial intelligence

Evaluating approaches to AI

Symbolic AI and its limits

Neat vs. scruffy

Soft vs. hard computing

Narrow vs. general AI

Machine consciousness, sentience and mind

Consciousness

Computationalism and functionalism

Robot rights

Future

Superintelligence

Risks

Technological unemployment

Bad actors and weaponized AI

Algorithmic bias

Existential risk

Copyright

Ethical machines

Regulation

In fiction

Scientific diplomacy

Warfare

Russo-Ukrainian War

Warfare regulations

Cybersecurity

Czech Republic's approach

Germany's approach

European Union's approach

Russo-Ukrainian War

Election security

Future of work

Facial recognition

AI and school

AI and medicine

AI in business

Business and diplomacy

AI and foreign policy

See also

Explanatory notes

References

AI textbooks

History of AI

Other sources

Further reading

External links