Artificial Intelligence
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Artificial intelligence (AI) is intelligence—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 intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The '' Oxford English Dictionary'' of Oxford University Press 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. Artificial intelligence, AI, like electricity or computers, is a general purpose technology that has a multitude of applica ...
include advanced web search engines (e.g., Google),
recommendation systems A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular u ...
(used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in
strategic game A strategy game or strategic game is a game (e.g. a board game) in which the players' uncoerced, and often autonomous, decision-making skills have a high significance in determining the outcome. Almost all strategy games require internal decis ...
systems (such as chess 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 instance, optical character recognition 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"), 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, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia. 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, and the ability to move and manipulate objects. General intelligence (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 networks, 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 and economics. AI also draws upon computer science, psychology, linguistics,
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
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 ...
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 to humanity if its rational capacities are not steered towards beneficial goals.


History

Artificial beings with intelligence appeared as storytelling devices in antiquity, AI in myth: * * and have been common in fiction, as in Mary Shelley's '' Frankenstein'' or Karel Čapek's '' R.U.R.'' These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence. The study of mechanical or "formal" reasoning began with
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 and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight that digital computers can simulate any process of formal reasoning is known as the
Church–Turing thesis 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,
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 "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 ...
or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included
Allen Newell 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, and Marvin Minsky. Closely associated with this approach was the "heuristic search" approach, which likened intelligence to a problem of exploring a space of possibilities for answers. The second vision, known as the connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently Frank Rosenblatt, sought to connect Perceptron in ways inspired by connections of neurons.
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, Bertrand Russell, and others. Connectionist approaches based on
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 ...
or artificial neural networks 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 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 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 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 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". Marvin Minsky 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 ...
of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an " AI winter", a period when obtaining funding for AI projects was difficult. First AI Winter, Lighthill report, Mansfield Amendment * * * * * 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 project inspired the U.S. and British governments to restore funding for academic research. Funding initiatives in the early 80s:
Fifth Generation Project 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 ...
(Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): * * * * *
However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began. Second AI Winter: * * * * Many researchers began to doubt that the symbolic approach would be able to imitate all the processes of human cognition, especially perception, robotics,
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 ...
and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Robotics researchers, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, and learn their environment. 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" was revived by Geoffrey Hinton, David Rumelhart 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
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 ...
, fuzzy systems,
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 ...
,
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
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 ...
or
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
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 ...
, economics 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 Formal, formality, informal or informality imply the complying with, or not complying with, some set of requirements (forms, in Ancient Greek). They may refer to: Dress code and events * Formal wear, attire for formal events * Semi-formal attire ...
and narrow 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 machine learning 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 Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. He attributes this to an increase in affordable
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 ...
, 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
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 ...
. This concern has led to the subfield of artificial general intelligence (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 probability and economics. 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: * 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 allow AI programs to answer questions intelligently and make deductions about real-world facts. A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). A truly intelligent program would also need access to commonsense knowledge; the set of facts that an average person knows. The semantics of an ontology is typically represented in description logic, such as the Web Ontology Language. 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 (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 finds patterns in a stream of input. Supervised learning requires a human to label the input data first, and comes in two main varieties:
classification 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 Regression or regressions may refer to: Science * Marine regression, coastal advance due to falling sea level, the opposite of marine transgression * Regression (medicine), a characteristic of diseases to express lighter symptoms or less extent ( ...
. 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 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 is when the knowledge gained from one problem is applied to a new problem. Computational learning theory 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 (how much data is required), or by other notions of optimization.


Natural language processing

Natural language processing (NLP) allows machines to read and understand 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.
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 ...
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 of sentences into logic. 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-based
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 ...
(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 sensors) to deduce aspects of the world. Applications include speech recognition,
facial recognition Facial recognition or face recognition may refer to: * Face detection, often a step done before facial recognition * Face perception, the process by which the human brain understands and interprets the face * Pareidolia, which involves, in part, se ...
, and object recognition. 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 assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. However, this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.


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 Marvin Minsky argue that work in different individual domains can be incorporated into an advanced multi-agent system or cognitive architecture with general intelligence. Pedro Domingos 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 An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science: #An o ...
or simulated child development 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 premises to conclusions, where each step is the application of an inference rule. Forward chaining,
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 clauses, 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 and
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 ...
: * * *
Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.Moving and configuration space: * Simple exhaustive searchesUninformed searches ( breadth first search,
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): * * * *
are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use " heuristics" or "rules of thumb" that prioritize choices in favor of those more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies, heuristics can also serve to eliminate some choices unlikely to lead to a goal (called " pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies. Heuristic or informed searches (e.g., greedy best first and A*): * * * * Heuristics limit the search for solutions into a smaller sample size. A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other related optimization algorithms include random optimization, beam search and metaheuristics like simulated annealing. Optimization searches: * * *
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 ...
uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Genetic programming and genetic algorithms: * * Alternatively, distributed search processes can coordinate via
swarm intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in ...
algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by
ant trail Ants are eusocial insects of the family Formicidae and, along with the related wasps and bees, belong to the order Hymenoptera. Ants evolved from vespoid wasp ancestors in the Cretaceous period. More than 13,800 of an estimated total of 22 ...
s). 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
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 ...
Satplan: * , * , * and inductive logic programming is a method for
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 ...
. 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 logic First-order logic and features such as equality: * , * , * , * adds quantifiers and predicates and can express facts about objects, their properties, and their relations with each other.
Fuzzy logic Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely ...
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 Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely ...
: * *
Default logics, non-monotonic logics and 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, closed world assumption,
abduction Abduction may refer to: Media Film and television * "Abduction" (''The Outer Limits''), a 2001 television episode * " Abduction" (''Death Note'') a Japanese animation television series * " Abductions" (''Totally Spies!''), a 2002 episode of an ...
: * * * * (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, such as description logics; Representing categories and relations: Semantic networks, description logics, inheritance (including
frames A frame is often a structural system that supports other components of a physical construction and/or steel frame that limits the construction's extent. Frame and FRAME may also refer to: Physical objects In building construction *Framing (co ...
and
scripts Script may refer to: Writing systems * Script, a distinctive writing system, based on a repertoire of specific elements or symbols, or that repertoire * Script (styles of handwriting) ** Script typeface, a typeface with characteristics of ha ...
): * , * , * , *
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): * , * , * causal calculus; Causal calculus: * belief calculus (belief revision); and
modal logic Modal logic is a collection of formal systems developed to represent statements about necessity and possibility. It plays a major role in philosophy of language, epistemology, metaphysics, and natural language semantics. Modal logics extend other ...
s. Representing knowledge about knowledge: Belief calculus,
modal logic Modal logic is a collection of formal systems developed to represent statements about necessity and possibility. It plays a major role in philosophy of language, epistemology, metaphysics, and natural language semantics. Modal logics extend other ...
s: * , *
Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as
paraconsistent logic A paraconsistent logic is an attempt at a logical system to deal with contradictions in a discriminating way. Alternatively, paraconsistent logic is the subfield of logic that is concerned with studying and developing "inconsistency-tolerant" syste ...
s.


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 probability 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 Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, a ...
algorithm),
Bayesian inference Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, a ...
algorithm: * , * , * , *
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 ...
(using the expectation-maximization algorithm), Bayesian learning and the expectation-maximization algorithm: * , * , * *
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 ...
(using
decision network Decision may refer to: Law and politics * Judgment (law), as the outcome of a legal case *Landmark decision, the outcome of a case that sets a legal precedent * ''Per curiam'' decision, by a court with multiple judges Books * ''Decision'' (nove ...
s) Bayesian decision theory and Bayesian
decision network Decision may refer to: Law and politics * Judgment (law), as the outcome of a legal case *Landmark decision, the outcome of a case that sets a legal precedent * ''Per curiam'' decision, by a court with multiple judges Books * ''Decision'' (nove ...
s: *
and 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 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 network Decision may refer to: Law and politics * Judgment (law), as the outcome of a legal case *Landmark decision, the outcome of a case that sets a legal precedent * ''Per curiam'' decision, by a court with multiple judges Books * ''Decision'' (nove ...
s: *
dynamic
decision network Decision may refer to: Law and politics * Judgment (law), as the outcome of a legal case *Landmark decision, the outcome of a case that sets a legal precedent * ''Per curiam'' decision, by a court with multiple judges Books * ''Decision'' (nove ...
s,
game theory Game theory is the study of mathematical models of strategic interactions among rational agents. Myerson, Roger B. (1991). ''Game Theory: Analysis of Conflict,'' Harvard University Press, p.&nbs1 Chapter-preview links, ppvii–xi It has appli ...
and mechanism design.
Game theory Game theory is the study of mathematical models of strategic interactions among rational agents. Myerson, Roger B. (1991). ''Game Theory: Analysis of Conflict,'' Harvard University Press, p.&nbs1 Chapter-preview links, ppvii–xi It has appli ...
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. 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 machine learning approaches. The
decision tree A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...
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 In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratorie ...
(SVM) displaced k-nearest neighbor in the 1990s. The
naive Bayes classifier In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Baye ...
is reportedly the "most widely used learner" at Google, due in part to its scalability.
Neural network 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 ...
s 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

Neural network 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 ...
s 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 " fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks model complex relationships between inputs and outputs and find patterns in data. They can learn continuous functions and even digital logical operations. Neural networks can be viewed as a type of
mathematical optimization 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 ...
– they perform gradient descent on a multi-dimensional topology that was created by training the network. The most common training technique is the backpropagation algorithm. Backpropagation: * , * , * Paul Werbos' introduction of backpropagation to AI: * ; Automatic differentiation, an essential precursor: * ; Other
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 ...
techniques for neural networks are Hebbian learning ("fire together, wire together"),
GMDH Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models. GMDH is used in such fiel ...
or
competitive learning Competitive learning is a form of unsupervised learning in artificial neural networks Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural net ...
.
Competitive learning Competitive learning is a form of unsupervised learning in artificial neural networks Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural net ...
, Hebbian coincidence learning, Hopfield networks and attractor networks: *
The main categories of networks are acyclic or
feedforward neural network A feedforward neural network (FNN) is an artificial neural network wherein connections between the nodes do ''not'' form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the ...
s (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 perceptron A multilayer perceptron (MLP) is a fully connected class of feedforward artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to mean ''any'' feedforward ANN, sometimes strictly to refer to networks composed of mu ...
s and
radial basis network In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inp ...
s.
Feedforward neural network A feedforward neural network (FNN) is an artificial neural network wherein connections between the nodes do ''not'' form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the ...
s, perceptrons and
radial basis network In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inp ...
s: * *


Deep learning

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 ...
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 An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimensiona ...
, 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 Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the hum ...
, speech recognition, 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 Long short-term memory (LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) ...
(LSTM) technique can prevent this in most cases.


Specialized languages and hardware

Specialized languages for artificial intelligence have been developed, such as
Lisp A lisp is a speech impairment in which a person misarticulates sibilants (, , , , , , , ). These misarticulations often result in unclear speech. Types * A frontal lisp occurs when the tongue is placed anterior to the target. Interdental lisping ...
, 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 AI effect. In the 2010s, AI applications were at the heart of the most commercially successful areas of computing, and have become a ubiquitous feature of daily life. AI is used in search engines (such as Google Search), targeting online advertisements,
recommendation systems A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular u ...
(offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising ( AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including
drones Drone most commonly refers to: * Drone (bee), a male bee, from an unfertilized egg * Unmanned aerial vehicle * Unmanned surface vehicle, watercraft * Unmanned underwater vehicle or underwater drone Drone, drones or The Drones may also refer to: ...
and self-driving cars), automatic language translation (
Microsoft Translator Microsoft Translator is a multilingual machine translation cloud service provided by Microsoft. Microsoft Translator is a part of Microsoft Cognitive Services and integrated across multiple consumer, developer, and enterprise products; including ...
, Google Translate),
facial recognition Facial recognition or face recognition may refer to: * Face detection, often a step done before facial recognition * Face perception, the process by which the human brain understands and interprets the face * Pareidolia, which involves, in part, se ...
( Apple's Face ID or Microsoft's DeepFace), image labeling (used by Facebook, 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 Energy storage is the capture of energy produced at one time for use at a later time to reduce imbalances between energy demand and energy production. A device that stores energy is generally called an accumulator or battery. Energy comes in ...
, deepfakes, medical diagnosis, military logistics, or supply chain management. Game playing has been a test of AI's strength since the 1950s.
Deep Blue Deep Blue may refer to: Film * '' Deep Blues: A Musical Pilgrimage to the Crossroads'', a 1992 documentary film about Mississippi Delta blues music * ''Deep Blue'' (2001 film), a film by Dwight H. Little * ''Deep Blue'' (2003 film), a film us ...
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 A game show is a genre of broadcast viewing entertainment (radio, television, internet, stage or other) where contestants compete for a reward. These programs can either be participatory or demonstrative and are typically directed by a host, sh ...
exhibition match, IBM's
question answering system 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 ...
,
Watson Watson may refer to: Companies * Actavis, a pharmaceutical company formerly known as Watson Pharmaceuticals * A.S. Watson Group, retail division of Hutchison Whampoa * Thomas J. Watson Research Center, IBM research center * Watson Systems, make ...
, defeated the two greatest ''Jeopardy!'' champions,
Brad Rutter Bradford Gates Rutter (born January 31, 1978) is an American game show contestant, TV host, producer, and actor. With over $5.1 million in winnings, he is currently the 2nd highest-earning American game show contestant of all time, behind Ken Je ...
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 handicaps. Other programs handle imperfect-information games; such as for
poker Poker is a family of comparing card games in which players wager over which hand is best according to that specific game's rules. It is played worldwide, however in some places the rules may vary. While the earliest known form of the game w ...
at a superhuman level, Pluribus and Cepheus.
DeepMind DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research laboratory founded in 2010. DeepMind was List of mergers and acquisitions by Google, acquired by Google in 2014 and became a wholly owned subsid ...
in the 2010s developed a "generalized artificial intelligence" that could learn many diverse
Atari Atari () is a brand name that has been owned by several entities since its inception in 1972. It is currently owned by French publisher Atari SA through a subsidiary named Atari Interactive. The original Atari, Inc. (1972–1992), Atari, Inc., ...
games on its own. By 2020,
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 ...
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 AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. The program is designed as a deep learning system. AlphaFold AI software has had two major v ...
(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, create art (such as poetry or painting) and prove mathematical theorems.


Intellectual Property

In 2019, WIPO reported that AI was the most prolific emerging technology in terms of number of patent applications and granted patents, the
Internet of things The Internet of things (IoT) describes physical objects (or groups of such objects) with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other comm ...
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 Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the hum ...
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

Alan Turing 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"
Russell Russell may refer to: People * Russell (given name) * Russell (surname) * Lady Russell (disambiguation) * Lord Russell (disambiguation) Places Australia *Russell, Australian Capital Territory *Russell Island, Queensland (disambiguation) **Ru ...
and 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''. " 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 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, Marvin Minsky 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 In science and philosophy, a paradigm () is a distinct set of concepts or thought patterns, including theories, research methods, postulates, and standards for what constitute legitimate contributions to a field. Etymology ''Paradigm'' comes f ...
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, neat, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers.


Symbolic AI and its limits

Symbolic AI 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 ...
(or " GOFAI") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed 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 logic, optimization, or
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 ...
). "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
intractable Intractable may refer to: * Intractable conflict, a form of complex, severe, and enduring conflict * Intractable pain, pain which cannot be controlled/cured by any known treatment * Intractable problem In theoretical computer science and mathema ...
for many important problems. Soft computing is a set of techniques, including genetic algorithms,
fuzzy logic Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely ...
and
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 ...
, 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 (
narrow AI Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of mind, or, as narrow AI, is focused on one narrow task. In John Searle's terms it “would be useful for testing hypotheses about minds, but would ...
) 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 The mind is the set of faculties responsible for all mental phenomena. Often the term is also identified with the phenomena themselves. These faculties include thought, imagination, memory, will, and sensation. They are responsible for various m ...
, consciousness and
mental states A mental state, or a mental property, is a state of mind of a person. Mental states comprise a diverse class, including perception, pain experience, belief, desire, intention, emotion, and memory. There is controversy concerning the exact definiti ...
, 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 Russell and Peter Norvig observe that most AI researchers "don't care about the hilosophy 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 Information processing is the change (processing) of information in any manner detectable by an observer. As such, it is a process that ''describes'' everything that happens (changes) in the universe, from the falling of a rock (a change in posit ...
is easy to explain, however, human
subjective experience In philosophy of mind, qualia ( or ; singular form: quale) are defined as individual instances of subjective, conscious experience. The term ''qualia'' derives from the Latin neuter plural form (''qualia'') of the Latin adjective '' quālis'' () ...
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 Hilary Whitehall Putnam (; July 31, 1926 – March 13, 2016) was an American philosopher, mathematician, and computer scientist, and a major figure in analytic philosophy in the second half of the 20th century. He made significant contributions ...
. Philosopher
John Searle John Rogers Searle (; born July 31, 1932) is an American philosopher widely noted for contributions to the philosophy of language, philosophy of mind, and social philosophy. He began teaching at UC Berkeley in 1959, and was Willis S. and Mario ...
characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind. Searle's
Chinese room The Chinese room argument holds that a digital computer executing a program cannot have a " mind," "understanding" or "consciousness," regardless of how intelligently or human-like the program may make the computer behave. The argument was pres ...
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 Sentience is the capacity to experience feelings and sensations. The word was first coined by philosophers in the 1630s for the concept of an ability to feel, derived from Latin '':wikt:sentientem, sentientem'' (a feeling), to distinguish it fro ...
(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
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 ...
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 artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to 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
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 ...
, 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's " Darwin among the Machines" as far back as 1863, and expanded upon by 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 Productivity is the efficiency of production of goods or services expressed by some measure. Measurements of productivity are often expressed as a ratio of an aggregate output to a single input or an aggregate input used in a production proces ...
gains are 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 Authoritarianism is a political system characterized by the rejection of political plurality, the use of strong central power to preserve the political ''status quo'', and reductions in the rule of law, separation of powers, and democratic votin ...
governments: smart spyware,
face recognition A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and wo ...
and voice recognition allow widespread
surveillance Surveillance is the monitoring of behavior, many activities, or information for the purpose of information gathering, influencing, managing or directing. This can include observation from a distance by means of electronic equipment, such as c ...
; such surveillance allows machine learning to
classify 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 ...
potential enemies of the state and can prevent them from hiding;
recommendation systems A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular u ...
can precisely target
propaganda Propaganda is communication that is primarily used to influence or persuade an audience to further an agenda, which may not be objective and may be selectively presenting facts to encourage a particular synthesis or perception, or using loaded ...
and misinformation for maximum effect; deepfakes aid in producing misinformation; advanced AI can make 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
emerge Emerge may refer to: * '' Emerge: The Best of Neocolours'', the fourth album of Neocolours * Emerge Desktop, a Desktop shell replacement for Microsoft Windows * ''Emerge'' (magazine), a defunct news magazine * Emerge Stimulation Drink, a drink s ...
from correlations: AI is used to
classify 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 ...
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 Compas, also known as compas direct or compas direk (; Haitian Creole: ''konpa'', ''kompa'' or ''kompa dirèk''), is a modern méringue dance music genre of Haiti. The genre was popularized following the creation of Ensemble Aux Callebasses in ...
, a commercial program widely used by
U.S. court The courts of the United States are closely linked hierarchical systems of courts at the federal and state levels. The United States federal courts, federal courts form the judicial branch of the US government and operate under the authority of the ...
s to assess the likelihood of a
defendant In court proceedings, a defendant is a person or object who is the party either accused of committing a crime in criminal prosecution or against whom some type of civil relief is being sought in a civil case. Terminology varies from one jurisdic ...
becoming a
recidivist Recidivism (; from ''recidive'' and ''ism'', from Latin ''recidīvus'' "recurring", from ''re-'' "back" and ''cadō'' "I fall") is the act of a person repeating an undesirable behavior after they have experienced negative consequences of th ...
. 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 hiring. At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) the
Association for Computing Machinery The Association for Computing Machinery (ACM) is a US-based international learned society for computing. It was founded in 1947 and is the world's largest scientific and educational computing society. The ACM is a non-profit professional member ...
, 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 A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. "Superintelligence" may also refer to a property of problem-solving systems (e.g., superintelligent language ...
AI may be able to improve itself to the point that humans could not control it. This could, as physicist Stephen Hawking puts it, " spell the end of the human race". Philosopher
Nick Bostrom Nick Bostrom ( ; sv, Niklas Boström ; born 10 March 1973) is a Swedish-born philosopher at the University of Oxford known for his work on existential risk, the anthropic principle, human enhancement ethics, superintelligence risks, and the rev ...
argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit 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 Friendly may refer to: Places * Friendly, West Yorkshire, a settlement in Calderdale, West Yorkshire, England * Friendly, Maryland, an unincorporated community in the United States * Friendly, Eugene, Oregon, a neighborhood in the United States * ...
" 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 Space Exploration Technologies Corp. (SpaceX) is an American spacecraft manufacturer, launcher, and a satellite communications corporation headquartered in Hawthorne, California. It was founded in 2002 by Elon Musk with the stated goal of ...
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 Mark Elliot Zuckerberg (; born ) is an American business magnate, internet entrepreneur, and philanthropist. He is known for co-founding the social media website Facebook and its parent company Meta Platforms (formerly Facebook, Inc.), o ...
(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. Rodney Brooks, 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 Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherw ...
,
computational ethics Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of design, man-made machines that use artificial intelligence ...
or computational morality, and was founded at an AAAI symposium in 2005. Other approaches include
Wendell Wallach Wendell Wallach (born April 21, 1946) is a bioethicist and author focused on the ethics and governance of emerging technologies, in particular artificial intelligence and neuroscience. He is a scholar at Yale University's Interdisciplinary Center ...
's "artificial moral agents" and Stuart J. Russell's 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 Eric Emerson Schmidt (born April 27, 1955) is an American businessman and software engineer known for being the CEO of Google from 2001 to 2011, executive chairman of Google from 2011 to 2015, executive chairman of Alphabet Inc. from 2015 to 20 ...
, and 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 in these works began with Mary Shelley's '' Frankenstein'', where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and
Stanley Kubrick's Stanley Kubrick (; July 26, 1928 – March 7, 1999) was an American film director, producer, screenwriter, and photographer. Widely considered one of the greatest filmmakers of all time, his films, almost all of which are adaptations of nove ...
'' 2001: A Space Odyssey'' (both 1968), with HAL 9000, the murderous computer in charge of the '' Discovery One'' spaceship, as well as ''
The Terminator ''The Terminator'' is a 1984 American science fiction action film directed by James Cameron. It stars Arnold Schwarzenegger as the Terminator, a cyborg assassin sent back in time from 2029 to 1984 to kill Sarah Connor (Linda Hamilton), whos ...
'' (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 Alien primarily refers to: * Alien (law), a person in a country who is not a national of that country ** Enemy alien, the above in times of war * Extraterrestrial life, life which does not originate from Earth ** Specifically, intelligent extrat ...
'' (1986) are less prominent in popular culture.
Isaac Asimov yi, יצחק אזימאװ , birth_date = , birth_place = Petrovichi, Russian SFSR , spouse = , relatives = , children = 2 , death_date = , death_place = Manhattan, New York City, U.S. , nationality = Russian (1920–1922)Soviet (192 ...
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 Manga (Japanese: 漫画 ) are comics or graphic novels originating from Japan. Most manga conform to a style developed in Japan in the late 19th century, and the form has a long prehistory in earlier Japanese art. The term ''manga'' is u ...
'' Ghost in the Shell'' and the science-fiction series ''
Dune A dune is a landform composed of wind- or water-driven sand. It typically takes the form of a mound, ridge, or hill. An area with dunes is called a dune system or a dune complex. A large dune complex is called a dune field, while broad, f ...
''. Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's '' R.U.R.'', the films '' A.I. Artificial Intelligence'' and '' Ex Machina'', as well as the novel '' Do Androids Dream of Electric Sheep?'', by
Philip K. Dick Philip Kindred Dick (December 16, 1928March 2, 1982), often referred to by his initials PKD, was an American science fiction writer. He wrote 44 novels and about 121 short stories, most of which appeared in science fiction magazines during his l ...
. 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 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 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 Russia invaded Ukraine on 24 February 2022.  The Ukrainian military is making use of the Turkish 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 drone The AeroVironment Switchblade is a miniature loitering munition, designed by AeroVironment and used by several branches of the United States military. Small enough to fit in a backpack, the Switchblade launches from a tube, flies to the target ...
s 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 The Campaign to Stop Killer Robots is a coalition of non-governmental organizations who seek to pre-emptively ban lethal autonomous weapons. History First launched in April 2013, the Campaign to Stop Killer Robots has urged governments and the U ...
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 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 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 (NSA) was appointed as the national authority for the cyber agenda. The role of cyber diplomacy strengthened in 2017 when the
Czech Ministry of Foreign Affairs The Ministry of Foreign Affairs of the Czech Republic (MFACR; cs, Ministerstvo zahraničních věcí České republiky; MZVČR) is a Government of the Czech Republic, Czech government ministry responsible for international relations of the Czech ...
(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 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. YouTube 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 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
healthcare Health care or healthcare is the improvement of health via the prevention, diagnosis, treatment, amelioration or cure of disease, illness, injury, and other physical and mental impairments in people. Health care is delivered by health profe ...
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 Facial recognition or face recognition may refer to: * Face detection, often a step done before facial recognition * Face perception, the process by which the human brain understands and interprets the face * Pareidolia, which involves, in part, se ...
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 "
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 ...
" 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 Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, enco ...
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.


See also

* ''
A.I. Rising ''A.I. Rising'' (also known as ''Ederlezi Rising'' and ''Ederlezi ébredése'') is a 2018 English-language Serbian science fiction film directed by Lazar Bodroža and based on a 1980s short story by Zoran Nešković that was adapted for film b ...
'' *
AI alignment In the field of artificial intelligence (AI), AI alignment research aims to steer AI systems towards their designers’ intended goals and interests. An ''aligned'' AI system advances the intended objective; a ''misaligned'' AI system is compet ...
* Artificial intelligence arms race * Artificial philosophy * Behavior selection algorithm * Business process automation * Case-based reasoning *
Emergent algorithm An emergent algorithm is an algorithm that exhibits emergent behavior. In essence an emergent algorithm implements a set of simple ''building block'' behaviors that when combined exhibit more complex behaviors. One example of this is the implement ...
* Female gendering of AI technologies * Glossary of artificial intelligence *
Robotic process automation Robotic process automation (RPA) is a form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers. It is sometimes referred to as ''software robotics'' (not to be ...
*
Synthetic intelligence Synthetic intelligence (SI) is an alternative/opposite term for artificial intelligence emphasizing that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence. John Haugeland propo ...
* Universal basic income *
Weak artificial intelligence Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of mind, or, as narrow AI, is focused on one narrow task. In John Searle's terms it “would be useful for testing hypotheses about minds, but would ...
* Operations research


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
* * * * * . * * * *


Further reading

* Autor, David H., "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3) ''Journal of Economic Perspectives'' 3. * Boden, Margaret, ''Mind As Machine'', Oxford University Press, 2006. * Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", ''
Foreign Affairs ''Foreign Affairs'' is an American magazine of international relations and U.S. foreign policy published by the Council on Foreign Relations, a nonprofit, nonpartisan, membership organization and think tank specializing in U.S. foreign policy and ...
'', vol. 98, no. 4 (July/August 2019), pp. 192–98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.) * Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", '' Scientific American'', vol. 319, no. 3 (September 2018), pp. 88–93. * Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", '' Scientific American'', vol. 316, no. 6 (June 2017), pp. 60–65. * Halpern, Sue, "The Human Costs of AI" (review of
Kate Crawford Kate Crawford (born 1976) is a writer, composer, producer and academic. Crawford is a principal researcher at Microsoft Research (Social Media Collective), the co-founder and former director of research at the AI Now Institute at NYU, a visiting ...
, ''Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence'', Yale University Press, 2021, 327 pp.; Simon Chesterman, ''We, the Robots?: Regulating Artificial Intelligence and the Limits of the Law'', Cambridge University Press, 2021, 289 pp.; Keven Roose, ''Futureproof: 9 Rules for Humans in the Age of Automation'', Random House, 217 pp.; Erik J. Larson, ''The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do'', Belknap Press / Harvard University Press, 312 pp.), '' The New York Review of Books'', vol. LXVIII, no. 16 (21 October 2021), pp. 29–31. "AI training models can replicate entrenched social and cultural biases. ..Machines only know what they know from the data they have been given. . 30. tificial general intelligence–machine-based intelligence that matches our own–is beyond the capacity of algorithmic machine learning... 'Your brain is one piece in a broader system which includes your body, your environment, other humans, and culture as a whole.' en machines that master the tasks they are trained to perform can't jump domains.
AIVA AIVA (Artificial Intelligence Virtual Artist) is an electronic composer recognized by the SACEM. Description Created in February 2016, AIVA specializes in classical and symphonic music composition. It became the world's first virtual compose ...
, for example, can't drive a car even though it can write music (and wouldn't even be able to do that without Bach and Beethoven nd other composers on which AIVA is trained." (p. 31.) * Johnston, John (2008) ''The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI'', MIT Press. * Koch, Christof, "Proust among the Machines", '' Scientific American'', vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because " en the most sophisticated
brain simulation Brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and ...
s are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient
s important S, or s, is the nineteenth letter in the Latin alphabet, used in the modern English alphabet, the alphabets of other western European languages and others worldwide. Its name in English is ''ess'' (pronounced ), plural ''esses''. History ...
for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW Global Neuronal Workspace theory">Global Workspace Theory#Global neuronal workspace">Global Neuronal Workspace theory they turn from mere objects into subjects... with a point of view.... Once computers'
cognitive abilities Cognitive skills, also called cognitive functions, cognitive abilities or cognitive capacities, are brain-based skills which are needed in acquisition of knowledge, manipulation of information and reasoning. They have more to do with the mechanisms ...
rival those of humanity, their impulse to push for legal and political rights will become irresistible—the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT ntegrated Information Theory is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) * Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", '' Scientific American'', vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) * Gary Marcus, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", '' Scientific American'', vol. 327, no. 4 (October 2022), pp. 42–45. * E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018
SSRN, part 2(3)
. * George Musser, " Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", '' Scientific American'', vol. 320, no. 5 (May 2019), pp. 58–63. * Myers, Courtney Boyd ed. (2009)
"The AI Report"
. ''Forbes'' June 2009 * * Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", ''
Foreign Affairs ''Foreign Affairs'' is an American magazine of international relations and U.S. foreign policy published by the Council on Foreign Relations, a nonprofit, nonpartisan, membership organization and think tank specializing in U.S. foreign policy and ...
'', vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) * * * * Sun, R. & Bookman, L. (eds.), ''Computational Architectures: Integrating Neural and Symbolic Processes''. Kluwer Academic Publishers, Needham, MA. 1994. * Taylor, Paul, "Insanely Complicated, Hopelessly Inadequate" (review of Brian Cantwell Smith, ''The Promise of Artificial Intelligence: Reckoning and Judgment'', MIT, 2019, , 157 pp.; Gary Marcus and Ernest Davis, ''Rebooting AI: Building Artificial Intelligence We Can Trust'', Ballantine, 2019, , 304 pp.; Judea Pearl and Dana Mackenzie, ''The Book of Why: The New Science of Cause and Effect'', Penguin, 2019, , 418 pp.), ''
London Review of Books The ''London Review of Books'' (''LRB'') is a British literary magazine published twice monthly that features articles and essays on fiction and non-fiction subjects, which are usually structured as book reviews. History The ''London Review of ...
'', vol. 43, no. 2 (21 January 2021), pp. 37–39. Paul Taylor writes (p. 39): "Perhaps there is a limit to what a computer can do without knowing that it is manipulating imperfect representations of an external reality." * Tooze, Adam, "Democracy and Its Discontents", '' The New York Review of Books'', vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)


External links

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