Grey Walter's turtles and the Johns Hopkins Beast. Symbolic When access to digital computers became possible in the middle s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation.
Approaches based on cybernetics or neural networks were abandoned or pushed into the background. Cognitive simulation. Artificial intelligence Economist Herbert Simon and Allen 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 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 80s. Roger Schank described their "anti-logic" approaches as "scruffy" as opposed to the "neat" paradigms at CMU and Stanford. Sub-symbolic By the s 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. 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 Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle s. Statistical In the s, 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. Stuart Russell and Peter Norvig describe this movement as nothing less than a "revolution" and "the victory of the neats. 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 fieldssuch as decision theory and economicsthat also use concepts of abstract agents.
The intelligent agent paradigm became widely accepted during the s. 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. Tools 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 and optimization 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. Simple exhaustive searches[] 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 eliminate choices that are unlikely to lead to the goal called "pruning the search tree". Heuristics supply the program with a "best guess" for the path on which the solution lies. Artificial intelligence A very different kind of search came to prominence in the s, 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 optimization algorithms are simulated annealing, beam search and random optimization. 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. Forms of evolutionary computation include swarm intelligence algorithms such as ant colony or particle swarm optimization [] and evolutionary algorithms such as genetic algorithms, gene expression programming, and genetic programming.
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[] and inductive logic programming is a method for learning. Propositional 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,[] is a version of first-order logic which allows the truth of a statement to be 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. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence. Default logics, non-monotonic logics and circumscription[51] 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;[45] situation calculus, event calculus and fluent calculus for representing events and time ;[46] causal calculus;[47] belief calculus; and modal logics.
Probabilistic methods for uncertain reasoning Many problems in AI in reasoning, planning, learning, perception and robotics require the agent to operate with incomplete or uncertain information.
AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.
Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[] information value theory. Classifiers and statistical learning methods 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.
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.
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.
Determining a suitable classifier for a given problem is still more an art than science. Neural networks The study of artificial neural networks[] began in the decade before the field AI research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed the backpropagation algorithm. Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex. Control theory Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.
Languages AI researchers have developed several specialized languages for AI research, including Lisp[] and Prolog. Evaluating progress In , 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. 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. Optimal: it is not possible to perform better. Strong super-human: performs better than all humans. Super-human: performs better than most humans. Sub-human: performs worse than most humans. For example, performance at draughts is optimal,[] performance at chess is super-human and nearing strong super-human see computer chess:computers versus human and performance at many everyday tasks such as recognizing a face or crossing a room without bumping into something is sub-human.
A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from Kolmogorov complexity and data compression. Applications Artificial intelligence techniques are pervasive and are too numerous to list.
Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect. Competitions and prizes There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behavior, data-mining, driverless cars, robot soccer and games.
Platforms A platform or "computing platform" is defined as "some sort of hardware architecture or software framework including application frameworks , that allows software to run. An automated online assistant providing customer service on a web page one of many very primitive applications of artificial intelligence. A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert systems, albeit PC-based but still an entire real-world system, to various robot platforms such as the widely available Roomba with open interface.
Philosophy Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence?
Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test. See Dreyfus' critique of AI. See The Emperor's New Mind. Artificial intelligence Searle's strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.
Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original. Predictions and ethics Artificial Intelligence is a common topic in both science fiction and projections about the future of technology and society.
The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.
Daneel Olivaw in Isaac Asimov's Robot series. Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human?
This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature. Ford predicts that many knowledge-based occupationsand in particular entry level jobswill be increasingly susceptible to automation via expert systems, machine learning[] and other AI-enhanced applications.
AI-based applications may also be used to amplify the capabilities of low-wage offshore workers, making it more feasible to outsource knowledge work. Weizenbaum was also bothered that AI researchers and some philosophers were willing to view the human mind as nothing more than a computer program a position now known as computationalism. To Weizenbaum these points suggest that AI research devalues human life. Ray Kurzweil has used Moore's law which describes the relentless exponential improvement in digital technology to calculate that desktop computers will have the same processing power as human brains by the year He also predicts that by 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 "singularity".
Artificial intelligence Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be friendly. Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" , and expanded upon by George Dyson in his book of the same name in Note that they use the term "computational intelligence" as a synonym for artificial intelligence.
Nilsson Other definitions also include knowledge and learning as additional criteria. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction.
Artificial intelligence McCorduck , pp. Hermes Trismegistus expressed the common belief that with these statues, craftsman had reproduced "the true nature of the gods", their sensus and spiritus. McCorduck makes the connection between sacred automatons and Mosaic law developed around the same time , which expressly forbids the worship of robots McCorduck , pp. Retrieved 25 April Wolfgang von Kempelen: McCorduck , p.
University of Pennsylvania. Retrieved 10 January Judah Loew's Golem: McCorduck , pp. McCorduck , pp. The Advent of the Algorithm.
Harcourt Books. Marvin Minsky quote: Minsky , p. Retrieved 31 October See Wason selection task. See list of cognitive biases for several examples. Luger et al. See Dreyfus' critique of AI Gladwell Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge. New York. The Nervous System. New York: Remmel Nunn. Affective Computing and Intelligent Interaction. LNCS Mind 9: Cited by Tao and Tan. Retrieved 13 May McCarthy recently reiterated his position at the AI 50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence" Maker IOS Press.
Artificial intelligence [96] The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in Crevier , pp.
Artificial intelligence Nilsson , chpt. Holland, John H. Adaptation in Natural and Artificial Systems. University of Michigan Press. Koza, John R. Genetic Programming. MIT Press. Poli, R. A Field Guide to Genetic Programming. Sandeep Rajani. Retrieved 24 September Journal of Logic, Language and Information 9 4 : CiteSeerX: Retrieved 21 July Artificial Intelligence Journal 18 : Retrieved 26 February VanLehn ed. Artificial intelligence [] Philosophy of AI.
Dreyfus , p. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states. Searle's original presentation of the thought experiment. Searle BBC News. Retrieved 3 February Prematurity of: Henderson, Mark 24 April The Times Online London.
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AI Magazine: Dennett, Daniel Consciousness Explained. The Penguin Press. Dreyfus, Hubert What Computers Can't Do. What Computers Still Can't Do. Dreyfus, Hubert; Dreyfus, Stuart Oxford, UK: Blackwell.
Edelman, Gerald 23 November Talking Robots. Fearn, Nicholas New York: Grove Press. Forster, Dion Pretoria: University of South Africa.
Gladwell, Malcolm New York: Little, Brown and Co.. Haugeland, John Artificial Intelligence: The Very Idea. Cambridge, Mass. Hawkins, Jeff; Blakeslee, Sandra On Intelligence. Hofstadter, Douglas Howe, J. November Artificial intelligence Kahneman, Daniel; Slovic, D. Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press. Kolata, G. Science : Kurzweil, Ray The Age of Spiritual Machines. Penguin Books.
The Singularity is Near. Lakoff, George University of Chicago Press. Lakoff, George; Nez, Rafael E. Basic Books. Lenat, Douglas; Guha, R. Building Large Knowledge-Based Systems. Lighthill, Professor Sir James Artificial Intelligence: a paper symposium. Science Research Council. Lucas, John In Anderson, A. Minds and Machines. Maker, Meg Houston Dartmouth College.
Retrieved 16 October McCarthy, John; Hayes, P. Machine Intelligence 4: McCarthy, John 12 November Minsky, Marvin Computation: Finite and Infinite Machines. Englewood Cliffs, N. The Emotion Machine. Moravec, Hans Mind Children.
Harvard University Press. National Academy Press. Needham, Joseph Science and Civilization in China: Volume 2. Caves Books Ltd.. Newell, Allen; Simon, H. In Feigenbaum, E. Computers and Thought. New York: McGraw-Hill. Communications of the ACM. Artificial intelligence Penrose, Roger Oxford University Press. Searle, John Behavioral and Brain Sciences 3 3 : Mind, language and society. OCLC Serenko, Alexander; Detlor, Brian AI and Society 18 4 : AI and Society 21 12 : Shapiro, Stuart C.
In Shapiro, Stuart C.. Encyclopedia of Artificial Intelligence 2nd ed. New York: John Wiley. Simon, H. The Shape of Automation for Men and Management.
Skillings, Jonathan 3 July Retrieved 5 August Vinge, Vernor Wason, P. In Foss, B. New horizons in psychology. Harmondsworth: Penguin. Weizenbaum, Joseph Computer Power and Human Reason. San Francisco: W. Forbes June Serenko, Alexander There are NONE unlimited gold patch or any other modification of the. Download Apk. Free and safe download. Download the latest version of the top software, games, programs and apps in Have the APK file for an alpha, beta, or staged rollout update?
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