artificial intelligence
artificial intelligence
[¦ärd·ə¦fish·əl in′tel·ə·jəns]Artificial intelligence
The subfield of computer science concerned with understanding the nature of intelligence and constructing computer systems capable of intelligent action. It embodies the dual motives of furthering basic scientific understanding and making computers more sophisticated in the service of humanity.
Many activities involve intelligent action—problem solving, perception, learning, planning and other symbolic reasoning, creativity, language, and so forth—and therein lie an immense diversity of phenomena. Scientific concern for these phenomena is shared by many fields, for example, psychology, linguistics, and philosophy of mind, in addition to artificial intelligence. The starting point for artificial intelligence is the capability of the computer to manipulate symbolic expressions that can represent all manner of things, including knowledge about the structure and function of objects and people in the world, beliefs and purposes, scientific theories, and the programs of action of the computer itself.
Artificial intelligence is primarily concerned with symbolic representations of knowledge and heuristic methods of reasoning, that is, using common assumptions and rules of thumb. Two examples of problems studied in artificial intelligence are planning how a robot, or person, might assemble a complicated device, or move from one place to another; and diagnosing the nature of a person's disease, or of a machine's malfunction, from the observable manifestations of the problem. In both cases, reasoning with symbolic descriptions predominates over calculating.
The approach of artificial intelligence researchers is largely experimental, with small patches of mathematical theory. As in other experimental sciences, investigators build devices (in this case, computer programs) to carry out their experimental investigations. New programs are created to explore ideas about how intelligent action might be attained, and are also developed to test hypotheses about concepts or mechanisms involved in intelligent behavior.
The foundations of artificial intelligence are divided into representation, problem-solving methods, architecture, and knowledge. To work on a task, a computer must have an internal representation in its memory, for example, the symbolic description of a room for a moving robot, or a set of features describing a person with a disease. The representation also includes all the knowledge, including basic programs, for testing and measuring the structure, plus all the programs for transforming the structure into another one in ways appropriate to the task. Changing the representation used for a task can make an immense difference, turning a problem from impossible to trivial.
Given the representation of a task, a method must be adopted that has some chance of accomplishing the task. Artificial intelligence has gradually built up a stock of relevant problem-solving methods (the so-called weak methods) that apply extremely generally.
An important feature of all the weak methods is that they involve search. One of the most important generalizations to arise in artificial intelligence is the ubiquity of search. It appears to underlie all intelligent action. In the worst case, the search is blind. In heuristic search extra information is used to guide the search.
Some of the weak methods are generate-and-test (a sequence of candidates is generated, each being tested for solutionhood); hill climbing (a measure of progress is used to guide each step); means-ends analysis (the difference between the desired situation and the present one is used to select the next step); impasse resolution (the inability to take the desired next step leads to a subgoal of making the step feasible); planning by abstraction (the task is simplified, solved, and the solution used as a guide); and matching (the present situation is represented as a schema to be mapped into the desired situation by putting the two in correspondence).
An intelligent agent—person or program—has multiple means for representing tasks and dealing with them. Also required is an architecture or operating framework within which to select and carry out these activities. Often called the executive or control structure, it is best viewed as a total architecture (as in computer architecture), that is, a machine that provides data structures, operations on those data structures, memory for holding data structures, accessing operations for retrieving data structures from memory, a programming language for expressing integrated patterns of conditional operations, and an interpreter for carrying out programs. Any digital computer provides an architecture, as does any programming language. Architectures are not all equivalent, and one important scientific question is what architecture is appropriate for a general intelligent agent.
In artificial intelligence, the basic paradigm of intelligent action is that of search through a space of partial solutions (called the problem space) for a goal situation. Each step offers several possibilities, leading to a cascading of possibilities that can be represented as a branching tree. The search is thus said to be combinatorial or exponential. For example, if there are 10 possible actions in any situation, and it takes a sequence of 12 steps to find a solution (a goal state), then there are 1012 possible sequences in the exhaustive search tree. What keeps the search under control is knowledge, which suggests how to choose or narrow the options at each step. Thus the fourth fundamental concern is how to represent knowledge in the memory of the system so it can be brought to bear on the search when relevant.
An intelligent agent will have immense amounts of knowledge. This implies another major problem, that of discovering the relevant knowledge as the solution attempt progresses. Although this search does not include the combinatorial explosion characteristic of searching the problem space, it can be time consuming and hard. However, the structure of the database holding the knowledge (called the knowledge base) can be carefully tailored to suit the architecture in order to make the search efficient. This knowledge base, with its accompanying problems of encoding and access, constitutes the final ingredient of an intelligent system.
An example of artificial intelligence is computer perception. Perception is the formation, from a sensory signal, of an internal representation suitable for intelligent processing. Though there are many types of sensory signals, computer perception has focused on vision and speech. Perception might seem to be distinct from intelligence, since it involves incident time-varying continuous energy distributions prior to interpretation in symbolic terms. However, all the same ingredients occur: representation, search, architecture, and knowledge. Speech perception starts with the acoustic wave of a human utterance and proceeds to an internal representation of what the speech is about. A sequence of representations is used: the digitization of the acoustic wave into an array of intensities; the formation of a small set of parametric quantities that vary continuously with time (such as the intensities and frequencies of the formants, bands of resonant energy characteristic of speech); a sequence of phons (members of a finite alphabet of labels for characteristic sounds, analogous to letters); a sequence of words; a parsed sequence of words reflecting grammatical structure; and finally a semantic data structure representing a sentence (or other utterance) that reflects the meaning behind the sounds.
A class of artificial intelligence programs called expert systems attempt to accomplish tasks by acquiring and incorporating the same knowledge that human experts have. Many attempts to apply artificial intelligence to medicine, government, and other socially significant tasks take the form of expert systems. Even though the emphasis is on knowledge, all the standard ingredients are present.
In careful tests, a number of expert systems have shown performance at levels of quality equivalent to or better than average practicing professionals (for example, average practicing physicians) on the restricted domains over which they operate. Nearly all large corporations and many smaller ones use expert systems. A common application is to provide technical assistance to persons who answer customers' trouble calls. Computer companies use expert systems to assist in configuring components from a parts catalog into a complete system that matches a customer's specifications, a kind of application that has been replicated in other industries tailoring assembled products to customers' needs. Troubleshooting and diagnostic programs are commonplace. Another widespread use of this technology is in software for home computers that assists taxpayers. One important lesson learned from incorporating artificial intelligence software into ongoing practice is that its success depends on many other aspects besides the intrinsic intellectual quality, for example, ease of interaction, integration into existing workflow, and costs.
Expert systems have sparked important insights in reasoning under uncertainty, causal reasoning, reasoning about knowledge, and acceptance of computer systems in the workplace. They illustrate that there is no hard separation between pure and applied artificial intelligence; finding what is required for intelligent action in a complex applied area makes a significant contribution to basic knowledge. See Expert systems
In addition to the subject areas mentioned above, significant work in artificial intelligence has been done on puzzles and reasoning tasks, induction and concept identification, symbolic mathematics, theorem proving in formal logic, natural language understanding and generation, vision, robotics, chemistry, biology, engineering analysis, computer‐assisted instruction, and computer-program synthesis and verification, to name only the most prominent. As computers become smaller and less expensive, more and more intelligence is built into automobiles, appliances, and other machines, as well as computer software, in everyday use. See Automata theory, Computer, Control systems, Cybernetics, Digital computer, Intelligent machine, Robotics
artificial intelligence
(artificial intelligence)Examples of AI problems are computer vision (building a system that can understand images as well as a human) and natural language processing (building a system that can understand and speak a human language as well as a human). These may appear to be modular, but all attempts so far (1993) to solve them have foundered on the amount of context information and "intelligence" they seem to require.
The term is often used as a selling point, e.g. to describe programming that drives the behaviour of computer characters in a game. This is often no more intelligent than "Kill any humans you see; keep walking; avoid solid objects; duck if a human with a gun can see you".
See also AI-complete, neats vs. scruffies, neural network, genetic programming, fuzzy computing, artificial life.
ACM SIGART. U Cal Davis. CMU Artificial Intelligence Repository.
AI
(1) See Adobe Illustrator.(2) See AI in a nutshell.
(3) (Artificial Intelligence) Devices and applications that exhibit human intelligence and behavior, including robots, self-driving cars, medical diagnosis and the ever-improving areas of voice, face and natural language recognition. Virtually every industry from finance to agriculture is using or exploring AI to improve operations and decision making. See intelligence.
AI implies the capability to learn as well as adapt through experience, and AI solutions differ from the traditional if this-do-that program logic. The term AI may refer to the entire field of artificial intelligence or to a specific AI system; for example, "there is an AI to handle this."
Large organizations, search engines and social media sites learn billions of details about the world's content and human behavior every day. One result of this knowledge is the voice-activated, natural language assistant, which includes Apple's Siri, Google's Assistant, Amazon's Alexa and Microsoft's Cortana. See AI in a nutshell, machine learning, neural network, virtual assistant and self-driving car.
A Past and Present Buzzword
The AI buzzword has often been abused as it has referred to any and all advancements. Today, it is the hottest buzzword in tech. Any product with the slightest breakthough is touted as having AI, whether it does or not. However, the acid test of AI was actually defined in the 1940s by English scientist, Alan Turing, who said, "A machine has artificial intelligence when there is no discernible difference between the conversation generated by the machine and that of an intelligent person" (see Turing test). Question and answer dialog is already here and will continue to get better; however, a "real" conversation like the androids in the movies could take a very long time.
The Loebner Prize
In 1991, American inventor Hugh Loebner launched a competition and annual award for the chatbot that exhibits the most human-like responses. See Loebner Prize.
Artificial Means Human
The term "intelligence" means processing capability; therefore, every computer is intelligent. However, artificial intelligence implies human-like intelligence. An ironic twist in terminology. See edge AI, AGI, chatbot, social robot, computer generations, neural network, AI anxiety and Watson.
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| Shakey the Robot |
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| Developed in 1969 by the Stanford Research Institute, Shakey was the first fully mobile robot with artificial intelligence. Seven feet tall, Shakey was named after its rather unstable movements. (Image courtesy of The Computer History Museum, www.computerhistory.org) |
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| Forty-Four Years Later - Still a Bit Shaky |
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| Funded by DARPA and made by Boston Dynamics, the 400-pound, 6'2" Atlas was designed for emergency rescue. Built in 2013, Atlas stumbled a lot in its first tests; however, teams of AI engineers have taught Atlas to become very sophisticated. (Image courtesy of Boston Dynamics, www.bostondynamics.com) |
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| The Cerebras AI Computer |
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| Founder and chief architect of Cerebras Systems, Sean Lie is holding the wafer that is the heart of the Cerebras computer. Designed for AI processing, it contains 2.6 trillion transistors (see Cerebras AI computer). (Image courtesy of Cerebras Systems, www.cerebras.net) |