Artificial Intelligence

Artificial intelligence

AI" redirects here. For other uses of "AI" and "Artificial intelligence", see AI (disambiguation).
 Garry Kasparov playing against Deep Blue, the first machine to win a chess match against a reigning world champion.The modern definition of artificial intelligence (or AI) is the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines." Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.

The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") has not yet been achieved and is a long-term goal of AI research.AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.

Perspectives on AI

AI in myth, fiction and speculation
Main articles: artificial intelligence in fiction, ethics of artificial intelligence, transhumanism, and Technological singularity Humanity has imagined in great detail the implications of thinking machines or artificial beings. They appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalion's Galatea. The earliest known humanoid robots (or automatons) were sacred statues worshipped in Egypt and Greece, believed to have been endowed with geniune consciousness by craftsman. In medieval times, alchemists such as Paracelsus claimed to have created artificial beings. In every civilization, from ancient times to the present, realistic clockwork imitations of human beings have been built by engineers such as Yan Shi, Hero of Alexandria, Al-Jazari and Wolfgang von Kempelen. Pamela McCorduck observes that "artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."

In modern fiction, beginning with Mary Shelley's classic Frankenstein, writers have explored the ethical issues presented by thinking machines. If a machine can be created that has intelligence, can it also feel? If it can feel, does it have the same rights as a human being? This is a key issue in Frankenstein as well as in modern science fiction: for example, the film Artificial Intelligence: A.I. considers a machine in the form of a small boy which has been given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue is also being considered by futurists, such as California's Institute for the Future under the name "robot rights", although many critics believe that the discussion is premature.

Science fiction writers and futurists have also speculated on the technology's potential impact on humanity. In fiction, AI has appeared as a servant (R2D2), a comrade (Lt. Commander Data), an extension to human abilities (Ghost in the Shell), a conqueror (The Matrix), a dictator (With Folded Hands) and an exterminator (Terminator, Battlestar Galactica). Some realistic potential consequences of AI are decreased labor demand, the enhancement of human ability or experience, and a need for redefinition of human identity and basic values.

Futurists estimate the capabilities of machines using Moore's Law, which measures the relentless exponential improvement in digital technology with uncanny accuracy. Ray Kurzweil has calculated that desktop computers will have the same processing power as human brains by the year 2029, and that by 2040 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the "technological singularity".

"Artificial intelligence is the next stage in evolution," Edward Fredkin said in the 1980s, expressing an idea first proposed by Samuel Butler's Darwin Among the Machines (1863), which speculated that machine evolution would outstrip human evolution and inevitably lead to "mechanical consciousness". Butler envisioned mechanical consciousness emerging by means of Darwinian Evolution, specifically by Natural selection, as a form of natural, not artificial, intelligence. Several futurists and science fiction writers have predicted that human beings and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldus Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, cyberneticist Kevin Warwick and Ray Kurzweil. Transhumanism has been illustrated in fiction as well, for example on the manga Ghost in the Shell.

History of AI research

Main articles: history of artificial intelligence and timeline of artificial intelligence
In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries about the brain, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.

The field of modern AI research was founded at conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle 60s their research was heavily funded by the U.S. Department of Defense and they were optimistic about the future of the new field:

1965, H. A. Simon: "[M]achines will be capable, within twenty years, of doing any work a man can do" 1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, DARPA cut off all undirected, exploratory research in AI. This was the first AI Winter.

In the early 80s, AI research was revived by the commercial success of expert systems; applying the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached more than a billion dollars. Minsky and others warned the community that enthusiasm for AI had spiraled out of control and that disappointment was sure to follow. Beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.

In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas. The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.

Philosophy of AI

Main article: philosophy of artificial intelligence
 
Can the brain be simulated? Does this prove machines can think?The philosophy of artificial intelligence considers the question "Can machines think?" Alan Turing, in his classic 1950 paper, Computing Machinery and Intelligence, was the first to try to answer it. In the years since, several answers have been given:

Turing's "polite convention": If a machine acts as intelligently as a human being, then it is as intelligent as a human being. This "convention" forms the basis of the Turing test. The artificial brain argument: The brain can be simulated. This argument combines the idea that a Turing complete machine can simulate any process, with the materialist idea that the mind is the result of a physical process in the brain.

The Dartmouth proposal: Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it. This assertion was printed in the program for the artmouth Conference of 1956, and represents the position of most working AI researchers. Newell and Simon's physical symbol system hypothesis: A physical symbol system has the necessary and sufficient means of general intelligent action. This statement claims that essence of intelligence is symbol manipulation. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge.Gödel's incompleteness theorem: There are statements that no physical symbol system can prove. Roger Penrose is among those who claim that Gödel's theorem limits what machines can do. Searle's "strong AI position": A physical symbol system can have a mind and mental states. Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.

AI research

Problems of AI

While there is no universally accepted definition of intelligence, AI researchers have studied several traits that are considered essential Deduction, reasoning, problem solving

Early AI researchers developed algorithms that imitated the process of conscious, step-by-step reasoning that human beings use when they solve puzzles, play board games, or make logical deductions. By the late 80s and 90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.

For difficult problems, most of these algorithms can require enormous computational resources — most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.

It is not clear, however, that conscious human reasoning is any more efficient when faced with a difficult abstract problem. Cognitive scientists have demonstrated that human beings solve most of their problems using unconscious reasoning, rather than the conscious, step-by-step deduction that early AI research was able to model. Embodied cognitive science argues that unconscious sensorimotor skills are essential to our problem solving abilities. It is hoped that sub-symbolic methods, like computational intelligence and situated AI, will be able to model these instinctive skills. The problem of unconscious problem solving, which forms part of our commonsense reasoning, is largely unsolved.

Knowledge representation

Main articles: knowledge representation and commonsense knowledge
Knowledge representation and knowledge engineering 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; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A complete representation of "what exists" is an ontology (borrowing a word from traditional philosophy). Ontological engineering is the science of finding a general representation that can handle all of human knowledge.

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 birds in general. John McCarthy identified this problem in 1969 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.

Unconscious knowledge: Much of what people know isn't represented as "facts" or "statements" that they could actually say out loud. They take the form of intuitions or tendencies and are represented in the brain unconsciously and sub-symbolically. This unconscious knowledge informs, supports and provides a context for our conscious knowledge. As with the related problem of unconscious reasoning, it is hoped that situated AI or computational intelligence will provide ways to represent this kind of knowledge. The breadth of common sense knowledge: The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of commonsense knowledge, such as Cyc, require enormous amounts of tedious step-by-step ontological engineering — they must be built, by hand, one complicated concept at a time.

Learning

Main article: machine learning
Important machine learning problems are:
Unsupervised learning: find a model that matches a stream of input "experiences", and be able to predict what new "experiences" to expect. Supervised learning, such as classification (be able to determine what category something belongs in, after seeing a number of examples of things from each category), or regression (given a set of numerical input/output examples, discover a continuous function that would generate the outputs from the inputs). Reinforcement learning: the agent is rewarded for good responses and punished for bad ones. (These can be analyzed in terms decision theory, using concepts like utility). 

Natural language processing

Main article: natural language processing
Natural language processing gives machines the ability to read and understand the languages human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation.

Perception

Main articles: machine perception, computer vision, and speech recognition
Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input. A few selected subproblems are speech recognition, facial recognition and object recognition.

Motion and manipulation

Main article: robotics
The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of localization (knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there).

Social intelligence

Main article: affective computing 
Kismet, a robot with rudimentary social skills.Emotion and social skills play two roles for an intelligent agent:It must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) For good human-computer interaction, an intelligent machine also needs to display emotions — at the very least it must appear polite and sensitive to the humans it interacts with. At best, it should appear to have normal emotions itself. 

General intelligence

Main articles: strong AI and AI-complete
Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.

Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the author's argument (reason), know what it's talking about (knowledge), and faithfully reproduce the author's intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.

Approaches to AI

There are as many approaches to AI as there are AI researchers—any coarse categorization is likely to be unfair to someone. Artificial intelligence communities have grown up around particular problems, institutions and researchers, as well as the theoretical insights that define the approaches described below. Artificial intelligence is a young science and is still a fragmented collection of subfields. At present, there is no established unifying theory that links the subfields into a coherent whole.

Cybernetics and brain simulation

In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. 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 at Princeton and the Ratio Club in England.

Traditional symbolic AI

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: CMU, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI "good old fashioned AI" or "GOFAI".

Cognitive simulation 

Economist Herbert Simon and Alan Newell studied 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 performed psychological experiments to demonstrate the similarities between human problem solving and the programs (such as their "General Problem Solver") they were developing. This tradition, centered at Carnegie Mellon University, would eventually culminate in the development of the Soar architecture in the middle 80s.

Logical AI 

Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms. His laboratory at Stanford (SAIL) focussed on using formal logic to solve wide variety of problems, including knowledge representation, planning and learning. Work in logic led to the development of the programming language Prolog and the science of logic programming. "Scruffy" symbolic AI 

Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions -- they argued that there was no easy answer, 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 and Stanford), and this still forms the basis of research into commonsense knowledge bases (such as Doug Lenat's Cyc) which must be built one complicated concept at a time.

Knowledge based AI

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. The knowledge revolution was also driven by the realization that truly enormous of amounts knowledge would be required by many simple AI applications. 

Sub-symbolic AI

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. By the 1980s, however, 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, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.

Bottom-up, situated, behavior based or nouvelle AI 

Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focussed on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s and reintroduced the use of control theory in AI. These approaches are also conceptually related to the embodied mind thesis.

Computational Intelligence 

Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle 1980s. These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence. The new neats  In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, 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). Russell & Norvig (2003) describe this movement as nothing less than a "revolution" and "the victory of the neats." 

Intelligent agent paradigm

The "intelligent agent" paradigm became widely accepted during the 1990s. Although earlier researchers had proposed modular "divide and conquer" approaches to AI, the intelligent agent did not reach its modern form until Judea Pearl, Alan Newell and others brought concepts from decision theory and economics into the study of AI. When the economist's definition of a rational agent was married to computer science's definition of an object or module, the intelligent agent paradigm was complete.

An intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. The simplest intelligent agents are programs that solve specific problems. The most complicated intelligent agents would be rational, thinking human beings.

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 some can be based on new approaches (without forcing researchers to reject old approaches that have proven useful). The paradigm gives researchers a common language to describe problems and share their solutions with each other and with other fields—such as decision theory—that also use concepts of abstract agents.

Integrating the approaches

An agent architecture or cognitive architecture allows researchers to build more versatile and intelligent systems out of interacting intelligent agents in a multi-agent system. 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 system provides 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. Rodney Brooks' subsumption architecture was an early proposal for such a hierarchical system.

Tools of AI research

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Search

Main article: search algorithm
Many problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Even some learning algorithms have at their core a search engine.

There are several types of search algorithms:

"Uninformed" search algorithms eventually search through every possible answer until they locate their goal. Naive algorithms quickly run into problems when they expand the size of their search space to astronomical numbers. The result is a search that is too slow or never completes.

Heuristic or "informed" searches use heuristic methods to eliminate choices that are unlikely to lead to their goal, thus drastically reducing the number of possibilities they must explore. Local searches, such as hill climbing, simulated annealing and beam search, use techniques borrowed from optimization theory. Genetic algorithms are a form of optimization search that imitates the process of natural selection, searching for an artificial phenotype (i.e. any sort of pattern) which passes a fitness measure by producing many copies of the most successful versions (imitating inheritance) and modifying them slightly (imitating mutation). 

Logic

Main article: logic programming
Logic was introduced into AI research by John McCarthy in his 1958 Advice Taker proposal. The most important technical development was J. Alan Robinson's discovery of the resolution and unification algorithm for logical deduction in 1963. This procedure is simple, complete and entirely algorithmic, and can easily be performed by digital computers. However, a naive implementation of the algorithm quickly leads to a combinatorial explosion or an infinite loop. In 1974, Robert Kowalski suggested representing logical expressions as Horn clauses (statements in the form of rules: "if p then q"), which reduced logical deduction to backward chaining or forward chaining. This greatly alleviated (but did not eliminate) the problem.

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,[114] and inductive logic programming is a method for learning.

There are several different forms of logic used in AI research.

Propositional logic or sentential logic is the logic of statements which can be true or false.
First order logic also allows the use of quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy logic, a version of first order logic which allows the truth of statement to represented as a value between 0 and 1, rather than simply True (1) or False (0). Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems.

Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem.Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics; situation calculus, event calculus and fluent calculus (for representing events and time); causal calculus; belief calculus; and modal logics. 

Probabalistic methods for uncertain reasoning

Main articles: Bayesian network, hidden Markov model, Kalman filter, decision theory, and utility theory Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. Starting in the late 80s and early 90s, Judea Pearl and others championed the use of methods drawn from probability theory and economics to devise a number of powerful tools to solve these problems.

Bayesian networks are very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks).

Probabalistic 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 and Kalman filters).

Planning problems have also taken advantages of other tools from economics, such as decision theory and decision analysis, information value theory, Markov decision processes, dynamic decision networks, game theory and mechanism design

Classifiers and statistical learning methods

Main articles: classifier (mathematics), statistical classification, and machine learning
The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny 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 a 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 can be seen as 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.

A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.

The most widely used classifiers are the neural network, kernel methods such as the support vector machine,  k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree. The performance of these classifiers have been compared over a wide range of classification tasks in order to find data characteristics that determine classifier performance.

Neural networks

Main articles: neural networks and connectionism 
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.The study of neural networks began with cybernetics researchers, working in the decade before the field AI research was founded. In the 1960s Frank Rosenblatt developed an important early version, the perceptron. Paul Werbos discovered the backpropagation algorithm in 1974, which led to a renaissance in neural network research and connectionism in general in the middle 1980s. The Hopfield net, a form of attractor network, was first described by John Hopfield in 1982.Neural networks are applied to the problem of learning, using such techniques as Hebbian learning and the relatively new field of Hierarchical Temporal Memory which simulates the architecture of the neocortex.

Social and emergent models

Main article: evolutionary computation
Several algorithms for learning use tools from evolutionary computation, such as genetic algorithms and swarm intelligence.

Control theory

Main article: intelligent control
Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.

Specialized languages

Main articles: IPL, Lisp (programming language), Prolog, STRIPS, and Planner (programming language)AI researchers have developed several specialized languages for AI research: IPL, one of the first programming languages, developed by Alan Newell, Herbert Simon and J. C. Shaw.Lisp was developed by John McCarthy at MIT in 1958. There are many dialects of Lisp in use today.Prolog, a language based on logic programming, was invented by French researchers Alain Colmerauer and Phillipe Roussel, in collaboration with Robert Kowalski of the University of Edinburgh.STRIPS, a planning language developed at Stanford in the 1960s. Planner developed at MIT around the same time. AI applications are also often written in standard languages like C++ and languages designed for mathematics, such as Matlab and Lush.

Evaluating artificial intelligence

How can one determine if an agent is intelligent? In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.

The broad classes of outcome for an AI test are:

optimal: it is not possible to perform better strong super-human: performs better than all humans super-human: erforms better than most humans sub-human: performs worse than most humans For example, performance at checkers is optimal, performance at chess is super-human and nearing strong super-human, performance at Go is sub-human, and performance at many everyday tasks performed by humans is sub-human.

Competitions and prizes

Main article: Competitions and prizes in artificial intelligence
There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behaviour, data-mining, driverless cars, robot soccer and games.