Artificial intelligence
Artificial intelligence (AI, also machine intelligence, MI) is intelligence displayed bymachines, in contrast with the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal.[1]Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with otherhuman minds, such as "learning" and "problem solving".[2] See glossary of artificial intelligence.
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip "AI is whatever hasn't been done yet."[3] For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology.[4] Capabilities generally classified as AI as of 2017 include successfully understanding human speech,[5]competing at a high level in strategic gamesystems (such as chess and Go[6]),autonomous cars, intelligent routing incontent delivery networks, military simulations, and interpreting complex data, including images and videos.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[7][8] followed by disappointment and the loss of funding (known as an "AI winter"),[9][10] followed by new approaches, success and renewed funding.[8][11] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[12] These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"),[13] the use of particular tools ("logic" or "neural networks"), or deep philosophical differences.[14][15][16] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[12]
The traditional problems (or goals) of AI research include reasoning, knowledge,planning, learning, natural language processing, perception and the ability to move and manipulate objects.[13] General intelligence is among the field's long-term goals.[17] Approaches include statistical methods, computational intelligence, andtraditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, neural networksand methods based on statistics, probability and economics. The AI field draws uponcomputer science, mathematics, psychology,linguistics, philosophy, neuroscience, artificial psychology and many others.
The field was founded on the claim thathuman intelligence "can be so precisely described that a machine can be made to simulate it".[18] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction andphilosophy since antiquity.[19] Some people also consider AI a danger to humanity if it progresses unabatedly.[20] Others believe that it is primarily a risk to employment: a frequently cited paper by Michael Osborne and Carl Benedikt Frey found that almost half of U.S. jobs are at risk to automation due to AI.[21]
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.[22][11]
History
While thought-capable artificial beingsappeared as storytelling devices in antiquity,[23] the idea of actually trying to build a machine to perform useful reasoning may have begun with Ramon Llull (c. 1300 CE). With his Calculus ratiocinator, Gottfried Leibniz extended the concept of thecalculating machine (Wilhelm Schickardengineered the first one around 1623), intending to perform operations on concepts rather than numbers.[24] Since the 19th century, artificial beings are common in fiction, as in Mary Shelley's Frankenstein orKarel Čapek's R.U.R. (Rossum's Universal Robots).[25]
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis.[26][page needed] Along with concurrent discoveries in neurology, information theoryand cybernetics, this led researchers to consider the possibility of building an electronic brain.[27] The first work that is now generally recognized as AI was McCullouchand Pitts' 1943 formal design for Turing-complete "artificial neurons".[24]
The field of AI research was born at a workshop at Dartmouth College in 1956.[28]Attendees Allen Newell (CMU), Herbert Simon(CMU), John McCarthy (MIT), Marvin Minsky(MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[29] They and their students produced programs that the press described as "astonishing":[30]computers were learning checkers strategies (c. 1954)[31] (and by 1959 were reportedly playing better than the average human),[32]solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[33] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[34] and laboratories had been established around the world.[35] AI's founders were optimistic about the future: 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".[7]
They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill[36] 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",[9] a period when obtaining funding for AI projects was difficult.
In the early 1980s, AI research was revived by the commercial success of expert systems,[37]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 computerproject inspired the U.S and British governments to restore funding for academic research.[8] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[10]
In the late 1990s and early 21st century, AI began to be used for logistics, data mining,medical diagnosis and other areas.[22] The success was due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields and a commitment by researchers to mathematical methods and scientific standards.[38] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov on 11 May 1997.[39]
Advanced statistical techniques (loosely known as deep learning), access to large amounts of data and faster computersenabled advances in machine learning and perception.[citation needed] By the mid 2010s, machine learning applications were used throughout the world.[citation needed] In aJeopardy! quiz show exhibition match, IBM'squestion answering system, Watson, defeated the two greatest Jeopardy champions, Brad Rutter and Ken Jennings, by a significant margin.[40] The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One use algorithms that emerged from lengthy AI research[41] as do intelligent personal assistants in smartphones.[42] 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.[6][43] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[44] who at the time continuously held the world No. 1 ranking for two years.[45][46] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.
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. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[47] He attributes this to an increase in affordableneural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[11] Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.[47]
Problems
The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. 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.[13]
Reasoning, problem solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[48] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts fromprobability and economics.[49]
For difficult problems, algorithms can require enormous computational resources—most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical for problems of a certain size. The search for more efficient problem-solving algorithms is a high priority.[50]
Human beings ordinarily use fast, intuitive judgments rather than step-by-step deduction that early AI research was able to model.[51] AI has progressed using "sub-symbolic" problem solving: embodied agent approaches emphasize the importance of sensorimotorskills to higher reasoning; neural net research attempts to simulate the structures inside the brain that give rise to this skill; statistical approaches to AI mimic the human ability to guess.
Knowledge representation
Knowledge representation[52] and knowledge engineering[53] are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;[54]situations, events, states and time;[55] causes and effects;[56] knowledge about knowledge (what we know about what other people know);[57] and many other, less well researched domains. 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 semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[58]The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[59] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations are suitable for content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery via automated reasoning (inferring new statements based on explicitly stated knowledge), etc. Video events are often represented as SWRL rules, which can be used, among others, to automatically generate subtitles for constrained videos.[60]
Among the most difficult problems in knowledge representation are:
- Default reasoning and the qualification problem
- Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. John McCarthyidentified this problem in 1969[61] as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.[62]
- The breadth of commonsense knowledge
- The number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base ofcommonsense knowledge (e.g., Cyc) require enormous amounts of laboriousontological engineering—they must be built, by hand, one complicated concept at a time.[63] A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the Internet, and thus be able to add to its own ontology.[citation needed]
- The subsymbolic form of some commonsense knowledge
- Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"[64]or an art critic can take one look at a statue and realize that it is a fake.[65] These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.[66]Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped thatsituated AI, computational intelligence, orstatistical AI will provide ways to represent this kind of knowledge.[66]
Planning
A hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.
Intelligent agents must be able to set goals and achieve them.[67] They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or "value") of available choices.[68]
In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[69] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[70]
Multi-agent planning uses the cooperationand competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[71]
Learning
Machine learning, a fundamental concept of AI research since the field's inception,[72] is the study of computer algorithms that improve automatically through experience.[73][74]
Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. 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. Inreinforcement learning[75] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. These three types of learning can be analyzed in terms of decision theory, using concepts likeutility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer scienceknown as computational learning theory.[citation needed]
Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[76][77][78][79]
Natural language processing
Natural language processing[80] gives machines the ability to read and understandhuman language. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing includeinformation retrieval, text mining, question answering[81] and machine translation.[82]
A common method of processing and extracting meaning from natural language is through semantic indexing. Although these indexes require a large volume of user input, it is expected that increases in processor speeds and decreases in data storage costs will result in greater efficiency.
Perception
Machine perception[83] is the ability to use input from sensors (such as cameras, microphones, tactile sensors, sonar and others) to deduce aspects of the world.Computer vision[84] is the ability to analyze visual input. A few selected subproblems arespeech recognition,[85] facial recognition andobject recognition.[86]
Motion and manipulation
The field of robotics[87] is closely related to AI. Intelligence is required for robots to handle tasks such as object manipulation[88] andnavigation, with sub-problems such aslocalization, mapping, and motion planning. These systems require that an agent is able to: Be spatially cognizant of its surroundings, learn from and build a map of its environment, figure out how to get from one point in space to another, and execute that movement (which often involves compliant motion, a process where movement requires maintaining physical contact with an object).[89][90]
Social intelligence
Affective computing is the study and development of systems that can recognize, interpret, process, and simulate humanaffects.[92][93] It is an interdisciplinary field spanning computer sciences, psychology, andcognitive science.[94] While the origins of the field may be traced as far back as the early philosophical inquiries into emotion,[95] the more modern branch of computer science originated with Rosalind Picard's 1995 paper[96] on "affective computing".[97][98] A motivation for the research is the ability to simulate empathy, where the machine would be able to interpret human emotions and adapts its behavior to give an appropriate response to those emotions.
Emotion and social skills[99] are important to an intelligent agent for two reasons. First, being able to predict the actions of others by understanding their motives and emotional states allow an agent to make better decisions. Concepts such as game theory,decision theory, necessitate that an agent be able to detect and model human emotions. Second, in an effort to facilitate human–computer interaction, an intelligent machine may want to display emotions (even if it does not experience those emotions itself) to appear more sensitive to the emotional dynamics of human interaction.
Creativity
A sub-field of AI addresses creativity both theoretically (the philosophical psychological perspective) and practically (the specific implementation of systems that generate novel and useful outputs).
General intelligence
Many researchers think that their work will eventually be incorporated into a machine with artificial general intelligence, combining all the skills mentioned above and even exceeding human ability in most or all these areas.[17][100] A few believe thatanthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.[101][102]
Many of the problems above also require that general intelligence be solved. For example, even specific straightforward tasks, likemachine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", but all of these problems need to be solved simultaneously in order to reach human-level machine performance.
Approaches
There is no established unifying theory orparadigm that guides AI research. Researchers disagree about many issues.[103]A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology orneurology? Or is human biology as irrelevant to AI research as bird biology is toaeronautical engineering?[14] Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[15]Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?[16]John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as synthetic intelligence,[104] a term which has since been adopted by some non-GOFAI researchers.[105][106]
Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Computational psychology is used to make computer programs that mimic human behavior.[107] Computational philosophy, is used to develop an adaptive, free-flowing computer mind.[107]Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish.[107] Together, the humanesque behavior, mind, and actions make up artificial intelligence.
Cybernetics and brain simulation
In the 1940s and 1950s, a number of researchers explored the connection betweenneurology, information theory, andcybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society atPrinceton University and the Ratio Club in England.[27] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.
Symbolic
When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University,Stanford and MIT, and each one developed its own style of research. John Haugelandnamed these approaches to AI "good old fashioned AI" or "GOFAI".[108] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background.[109] 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.
Cognitive simulation
Economist Herbert Simon and Allen Newellstudied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science,operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[110][111]
Logic-based
Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.[14] His laboratory at Stanford (SAIL) focused on using formallogic to solve a wide variety of problems, including knowledge representation, planningand learning.[112] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming languageProlog and the science of logic programming.[113]
Anti-logic or scruffy
Researchers at MIT (such as Marvin Minskyand Seymour Papert)[114] found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU andStanford).[15] Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.[115]
Knowledge-based
When computers with large memories became available around 1970, researchers from all three traditions began to buildknowledge into AI applications.[116] This "knowledge revolution" led to the development and deployment of expert systems(introduced by Edward Feigenbaum), the first truly successful form of AI software.[37] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.
Sub-symbolic
By the 1980s progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especiallyperception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.[16] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.
Embodied intelligence
This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[117] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
Computational intelligence and soft computing
Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s.[118] Neural networks are an example ofsoft computing --- they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Othersoft computing approaches to AI includefuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[119]
Statistical
In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are trulyscientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Stuart Russell and Peter Norvigdescribe this movement as nothing less than a "revolution" and "the victory of the neats".[38]Critics argue that these techniques (with few exceptions[120]) are too focused on particular problems and have failed to address the long-term goal of general intelligence.[121] There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges betweenPeter Norvig and Noam Chomsky.[122][123]
Integrating the approaches
- Intelligent agent paradigm
- An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such asdecision theory and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.[124]
- Agent architectures and cognitive architectures
- Researchers have designed systems to build intelligent systems out of interactingintelligent agents in a multi-agent system.[125] A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control systemprovides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.[126] Rodney Brooks'subsumption architecture was an early proposal for such a hierarchical system.[citation needed]
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