History

~3000BC	A papyrus, later bought in a Luxor antique shop by Edwin Smith in 1882, was prepared representing 48 surgical observations of head wounds. The observations were stated in symptom-diagnosis-treatment-prognosis combinations as: IF a patient has this symptom, THEN he has this injury with this prognosis if this treatment is applied. This was the first known expert system.
13^th C	Ramón Lull invented the Zairja, the first device that systematically tried to generate ideas by mechanical means.
1651	Leviathan, written by Thomas Hobbes (1588—1679), was published. In it he proposed that humans collectively, by virtue of their organization and use of their machines, would create a new intelligence. George B. Dyson refers to Hobbes as the patriarch of artificial intelligence in his book, “Darwin Among the Machines: The Evolution of Global Intelligence,” p7, 1997.
17^th C	Leibnitz and Pascal invented mechanical computing devices. Pascal was 19 years old in 1642 when he invented an eight-digit calculator, the Pascaline. In 1694, Gottfried Liebnitz invented the Liebnitz Computer, which multiplied by repetitive addition, an algorithm still in use today. Leibnitz also conceived of a ‘reasoning calculator’ for interpreting and evaluating concepts, but realized the problem was immense because of the great interconnectedness of concepts.
1726	Jonathan Swift anticipated an automatic book writer in Gulliver’s Travels.
1805	Joseph-Marie Jacquard invented the first truly programmable device to drive looms with instructions provided by punched cards.
1832	Charles Babbage designed the ‘analytical engine,’ a mechanical programmable computer. He had earlier designed a more limited Difference Engine in 1822, which he never finished building.
1847	George Boole developed a mathematical symbolic logic (later called Boolean algebra) for reasoning about categories (i.e., sets) of objects, which is also applicable to manipulating and simplifying logical propositions.
1879	Gottlob Frege went beyond Boole in his treatment of logic with his invention of predicate logic, making it possible to prove general theorems from rules. However, the meaning of the words being manipulated by this logic is still only what the user intended, and therefore not conveyed by his representation of the logic.
~1890	Hand-driven mechanical calculators became available.
1890	Herman Hollerith patented a tabulating machine to process census data fed in on punched cards. His company, the Tabulating Machine Company, eventually merged into what was to become IBM.
Late 1800s	Leonardo Torres y Quevedo invented a relay-activated automaton that played end games in chess.
1898	Behaviorism was expounded by psychologist Edward Lee Thorndike in “Animal Intelligence.” The basic idea is that all actions, thoughts, or desires are reflexes triggered by a higher form of stimulus, with humans just reacting to a higher form of stimulus. Mind, according to behaviorism, becomes a trivial concept, a passive associative mechanism.
1921	Karel Capek, a Czech writer, invented the term ‘robot’ to describe intelligent machines that revolted against their human masters and destroyed them.
1928	John von Neumann introduced the minimax theorem, which is still used as a basis for game-playing programs.
1931	Vannevar Bush’s mechanical differential analyzer (a mechanical analog computer) was able to solve differential equations.
1937	Alan Turing conceived of a universal Turing machine that could mimic the operation of any other computing machine. However, as did Godel, he also recognized that there exist certain kinds of calculations that no machine could perform. Even recognizing this limit on computers, Turing still did not doubt that computers could be made to think.
1937	Alan Turing and Alonzo Church independently arrived at the same thesis, the Church-Turing thesis, that all problems that a human can solve can be reduced to a set of algorithms.
~1938	Claude Shannon showed that calculations could be performed much faster using electromagnetic relays than they could be performed with mechanical calculators. He applied Boolean algebra. Electromechanical relays were used in the world’s first operational computer, Robinson, in 1940. Robinson was used by the English to decode messages from Enigma, the Germans’ enciphering machine.
1941	A leading German aeronautical research center deployed the Zuse Z3, a general-purpose electromechanical computer. It performed several instructions per second, and the program was entered by using a movie reel with punched holes representing instructions.
1943	Vacuum tubes replaced electromechanical relays in calculators. These were used in 1943 in Colossus, a faster successor to Robinson, to decipher increasingly complex German codes.
1943	Walter Pitts and Warren McCullock showed how artificial neural networks could compute, relying on the use of feedback loops.
1945	John von Neumann designed the basic computer architecture still used today, in which the memory stores instructions as well as data, and instructions are executed serially. He described this in a 1945 paper.
1945	ENIAC (Electronic Numerical Integrator and Calculator), which was to run 1,000 times faster than the relay-operated computers, was ready to run in late 1945. It was the first general purpose, fully electronic, programmable computer. John W. Mauchley and John Presper Eckert were its inventors.
1945—1956	Symbolic artificial intelligence emerged as a specific intellectual field. Key developments included Norbert Wiener’s development of the field of cybernetics, in which he invented a mathematical theory of feedback in biological and engineered systems. This work helped clarify the concept that intelligence is the process of receiving and processing information to achieve goals.
1947	The transistor was invented by William Shockley, Walter Brattain, and John Bardeen.
1948	Nobert Wiener published Cybernetics, a landmark book on information theory. ‘Cybernetics’ means “the science of control and communication in the animal and the machine.”
1949	Donald O. Hebbs suggested a way in which artificial neural networks might learn.
1950	Turing proposed his test, called the Turing test, to recognize machine intelligence.
1951	EDVAC, the first von Neumann computer, was built.
1951	Marvin Minsky and Dean Edmonds built the first artificial neural network that simulated a rat finding its way through a maze.
7 June 1954	Turing committed suicide under mysterious circumstances by eating a cyanide-laced apple following a conviction for homosexuality in 1953.
1950s	It became clear that computers could manipulate symbols representing concepts as well as numerical data.
1952—1955	At IBM, Arthur Samuel completed the first version of a Checkers-Playing Program. In recognition of this dramatic step forward in computer hardware and programming, IBM’s stock increased overnight by 15 points. Within three years, a new version of the Samuel Checkers-Playing Program demonstrated the capacity to learn. Widely believed to be the first self-learning program, the Checkers-Playing Program displayed a key aspect of artificial intelligence. Games facilitate AI research by enabling computer scientists to compare the performance of computers with that of human beings.
1955 —1956	The Logic Theorist, the first AI program, was written by Allen Newell, Herbert Simon, and J.C. Shaw. It proved theorems using a combination of searching, goal-oriented behavior, and application of rules. It used a list-processing technique in a new computer language, IPL (Information Processing Language) that they developed to write Logical Theorist. IPL provided pointers between related pieces of information to mimic associative memory; and catered to creating, changing, and destroying interacting symbolic structures on the fly.
1955	John McCarthy named the new discipline, ‘Artificial Intelligence’ in a proposal for the Dartmouth conference.
~1956	IBM released the 701 general purpose electronic computer, the first such machine on the market. It was designed by Nathaniel Rochester.
1956	A two-month summer conference on thinking machines was held at Dartmouth University. The attendees included John McCarthy, Marvin Minsky, Claude Shannon, Nathaniel Rochester, Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Herbert Simon, and Allen Newell. It did not result in a consensus view of AI.”Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” according to a statement of the Dartmouth conference participants, that expresses the physical symbol hypothesis.
1956	George Miller published “The Magic Number Seven” on the limits of short-term memory.
1956—1963	Two main themes emerge in AI: improved search methods in trial-and-error problems making computers learn by themselves (e.g., to play checkers better than the program’s author)
1957	Newell and Simon ran the General Problem Solver incorporating ‘means-ends analysis.’ Means-ends analysis seeks to reduce the difference between the predicted outcome and desired outcome by changing controlling factors. GPS and later AI programs were really quite limited in their problem solving ability as the programmer had to feed information to it in a highly stylized way. They also had to work hard to define each new problem, and the program made only a small contribution to the solution. Also, these programs contributed nothing to providing motivation for solving a problem, still an open issue today.Edward Feigenbaum’s EPAM (Elementary Perceiver and Memorizer), provided a model of how people memorize nonsense syllables.Herbert Gelernter wrote the Geometry Theorem Prover which used information to prune a search with a billion alternatives (for a 3-step proof of a geometry theorem) to only 25 alternatives. He was the first to demonstrate ‘model referencing.’ Arthur Samuel’s checkers-playing program soon learned how to beat him.
1957	Noam Chomsky, a linguist at MIT, postulated that language could be analyzed without reference to its content or meaning. In other words, syntax was independent of semantics. This concept was enticing to AI people as it would mean knowledge could be represented and analyzed without knowing anything about what was being said. Experience has shown that this concept doesn’t apply well to human languages.
1958	John McCarthy and Marvin Minsky founded the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology.
1958	John McCarthy developed the LISP program at MIT for AI work. It soon supplanted the earlier AI language, IPL, and retained its popularity against a later language, COMIT, developed in 1962.
Early 1960s	AI researchers concentrated on means of representing related knowledge in computers, a necessary precursor to developing the ability of computers to learn.
1961	General Motors first employed industrial robots on its auto assembly line with Unimate.
1961	Mortimer Taube, an engineer, authored the first anti-AI book, “Computers and Common Sense: The Myth of Thinking Machines.” It did not receive much attention.
1962	The world’s first industrial robots were marketed by a U.S. company.
1963	Tom Evans, under Marvin Minsky’s supervision, created the program, ANALOGY. It was designed to solve problems that involved associating geometric patterns that occurred in a past case with the pattern in a current case. ANALOGY could solve shape problems of the kind, figure C is to which of several alternative figures as figure A is to figure B.
1963	Stanford University founded the Artificial Intelligence Laboratory under John McCarthy.
1965	Brothers, Herbert L. Dreyfus, a philosopher, and Stuart E. Dreyfus, a mathematician, wrote a strongly anti-AI paper, “Alchemy and AI,” which was published reluctantly by the RAND Corporation for whom Herbert was consulting.
1965	Herbert Simon predicted that machines will be capable of doing any work a man can do by 1985.
1965	The Robotics Institute was started at Carnegie Mellon University under Raj Reddy.
1965 to ~1975	Edward Feigenbaum and Robert K. Lindsay at Stanford built DENDRAL, the first expert system. Its expertise was in mapping the structure of complex organic chemicals from data gathered by mass spectrometers. After DENDRAL’s rules grew to a certain size, its tangled set of statements became difficult to maintain and expand.
Middle and late 1960s	Marvin Minsky and Seymour Papert directed the Blocks Microworld Project at MIT AI Laboratory. This project improved computer vision, robotics, and even natural language processing enough for computers to view and manipulate a simple world of blocks of different colors, shapes, and sizes. Similar experiments proceeded at Stanford under John McCarthy and at Edinburgh University.
Middle and late 1960s	Joseph Weizenbaum of MIT revealed to the world his interactive Eliza, able to converse on all manner of subjects.
1966	National Research Council ended all support for automatic translation research, a field closely related to AI.
1968	The tradition of mad computers is continued with the release of the film, 2001: A Space Odyssey, directed by Stanley Kubrick, from Arthur C. Clarkes’ book. The computer’s name, HAL, is a reminder of the giant computer company, IBM. (Form a word from the letters that come after H, A, and L in the alphabet.)
1968 & 1969	Terry Winograd, a doctoral student under Seymour Papert, wrote SHRDLU (a word used in MAD magazine for mythical monsters and other oddities). SHRDLU created a simulated block world and robotic arm on a computer about which a user could ask questions and give commands in ordinary English. It has gradually been realized that the techniques employed in SHRDLU would not work beyond artificially defined toy worlds or restricted areas of expertise because, to do so, the computer would have to know vast amounts of knowledge that humans regard as common knowledge or common sense.
1969—1974	Roger Schank developed his ‘conceptual dependency’ theory, which enabled computers to make more plausible inferences about the meaning of the ‘semantic primitives’ in sentences, when words took on secondary meanings. For example, the meanings of sentences such as “He gave her a present,” and “Bill gave Joe a glancing blow” could be distinguished. However, the theory was inadequate for dealing with the complexities of meaning possible in linked sentences narrating a sequence of events, when some of the events can only be inferred from what is said. Typically, humans can readily infer the unstated events in a specific sequence.
1969	A mobile robot called Shakey was assembled at Stanford, which could navigate a block world in eight rooms and follow instructions in a simplified form of English.
1969	Marvin Minsky and Seymour Papert published their book, Perceptrons—An Introduction to Computational Geometry. Until its publication work on artificial networks in the U.S. was flourishing, which this book brought to a near halt until the 1980s by disparaging some of this work.
1969	John McCarthy and Patrick J. Hayes reappraised how AI might usefully proceed. They discounted possible help from philosophy as “philosophers have not really come to agreement in 2500 years.” They identified two basic problems to overcome . One is the ‘frame problem,’ that of managing all that is going on around the central actors, a task that creates a heavy computational burden. Next is the ‘qualification problem,’ meaning the need to deal with all the qualifiers that can arise to stop an expected rule from being followed exactly. For example, if the ignition key of a car is turned, usually the engine starts. However, there are many exceptions, such as when the car has no fuel, or the battery is discharged.
1970	William Wood at Bolte, Beranek & Newman in Boston conceived a parsing scheme called the Augmented Transition Network. By mixing syntax rules with semantic analysis, the scheme could discriminate between the meanings of sentences such as “The beach is sweltering” and “The boy is sweltering.”
1970	In his Ph.D. thesis, Jaime Carbonell first conceptualized the intelligent tutoring system, in the form of Scholar, his idea for a text-based ITS.
1970s	Earlier machine learning efforts aimed at enabling computers to automatically optimize appropriate weights for variables they had been told were important to solving a problem. Now efforts were directed to automatically deriving those variables themselves—in other words, automatic concept formation.Douglas Lenat programmed Automated Mathematician (AM), a program to rediscover number theory by itself. It combined a set of rudimentary ideas, a sense of experimentation, and a sense of rightness of good discoveries to guide its activities, the latter two capabilities expressed in a number of rules (or heuristics). Despite some initial dramatic success, it quickly reached limits for discovering new number theory. Lenat realized that it was because the heuristics it had been given were limited, and he decided it needed to be able to create new and useful discovery heuristics for itself. Over five years he developed this new ability in a successor program, EURISKO. EURISKO kept track of the performance of the heuristics it used, and dropped the ones that performed poorly, and modified and improved the better performing ones. The program was successfully used to improve the design of 3D computer chips. It even taught itself how to play a space-war game, Traveller TCS, and became the national champion in 1982 and 1983 with a radical approach to the game.
Early 1970s	DARPA’s Speech Understanding Research (SUR) program, for which Carnegie Mellon was the prime contractor was brought to an abrupt end. Although goals were met, the product, which has a limited grammar, was not considered practical.AI researchers turned from research into the control and expression of knowledge (such as was demonstrated in the Micro Worlds project) to the manipulation of large amounts of knowledge. This was done in recognition of the limitations of successful programs such as SHDLU and GPS to be extended to tackle the more complex problems of the real world in useful ways. The earlier work reflected overly simplistic approximations of the ways the human mind works, and better approximations were required. Manipulation of larger amounts of information was also enabled by the increasing power of computers.
Early 1970s	First practical demonstration of the use of fuzzy logic for process control. Abe Mamdani and his student, Seto Assilian, at Queen Mary College (now Queen Mary and Westfield) in London used fuzzy logic to control the operation of a small steam engine.
1971—1972	Alain Colmerauer and Phillipe Roussel wrote the computer language, PROLOG (for PROgrammation en LOGique). It was revised in 1974 to force logical statements (i.e., IF … THEN) to be written only in the Horn clause format. This permitted it to solve problems that required showing something was NOT true to be concluded in a finite number of steps. PROLOG became the favored AI language outside the U.S. where LISP still held sway.
1972 on	Edward Shortliffe, a Stanford doctoral student under Bruce Buchanan, and others wrote MYCIN, an expert system to diagnose infectious blood diseases and recommend antibiotics, with dosage adjusted for patient’s body weight. They also created the first expert system shell, that contained the inference engine, which contained the logic of how rules were to be applied. MYCIN could also deal with probabilistic rules, which DENDRAL couldn’t. MYCIN could outperform human clinicians in some trials. A difficulty that arose during the writing of these and subsequent expert systems has been the extraction of the knowledge from human experts into the rules, the so-called knowledge engineering.
1972	Herbert Dreyfus expanded his “Alchemy and AI” paper into an aggressively anti-AI book, “What Computers Can’t Do.”
1973	Sir James Lighthill, Cambridge University’s Lucasian Chair of Applied Mathematics, advised the British government to cease most AI research in Britain.
1974	Funding for AI research at MIT, Carnegie Mellon, and Stanford from DARPA was cut drastically as a result of recent disappointing results.
By mid-1970s	Diverging specialties in AI field emerged. These included Edward Feigenbaum’s work on expert systems; Roger Schank on language analysis; Marvin Minsky on knowledge representation; Douglas Lenat on automatic learning and nature of heuristics; David Marr on machine vision; and others developing PROLOG.
1974	Paul J. Werbos invented the back-propagation algorithm, that enabled multilayer neural networks, that had the ability to perform classification operations beyond simple Perceptrons. Back-propagation was independently rediscovered in the early 1980s by David Rumelhat and David Parker.
1975	Marvin Minsky published a paper, “A Framework for Representing Knowledge,” which he started with “It seems to me that the ingredients of most theories in artificial intelligence and in psychology have been on the whole too minute, local, and unstructured to account … for the effectiveness of commonsense thought.” He proposed that people thought in terms of generic ‘frames’ within which we look for expected features (he called them ‘terminals’) with anticipated properties (‘markers’). Frames may be grouped or linked together into systems. So, we would have idealized “house” frames with features including walls, windows, doors and a roof, which we would use to recognize real houses by frame matching. These and other frames, say, of shops, churches, and schools, would build a town system.
~1977	Roger Schank and others augmented the conceptual dependency theory with the use of scripts (short stories of typical sequences of events that don’t leave out any events) and the use of knowledge of people’s plans and goals to make sense of stories told by people and to answer questions about those stories that would require inferences to be made to answer them. This combination resulted in successful language analysis programs such as Janet Kolodner’s CYRUS, that thought of itself as Cyrus Vance, learned about his life from newspaper accounts, and could even surmise that Vance’s wife and the Israel prime minister Begin’s wife met at a social occasion to which it would be likely spouses would be invited. This in fact happened.
Late 1970s	First commercial expert system was developed. It was XCON. (for eXpert CONfigurer), developed by John McDermott at Carnegie Mellon. He developed it for Digital Equipment Company, which started using it in January 1980 to help configure computer systems, deciding between all the options available for their VAX system. It grew from containing about 300 rules in 1979 to more than 3,000 and could configure more than 10 different computer systems.
End of 1970s	Practical, commercial applications of AI were still rare.
July 1979	World champion backgammon player, Luigi Villa of Italy, became the first human champion of a board game to be defeated by a computer program, which was written by Hans Berliner of Carnegie Mellon. The program evaluated its moves by evaluating a weighted set of criteria that measured the goodness of a move. It did not use the alternative process of searching amongst all possible future moves and countermoves, a method used in chess, as there are too many alternatives in backgammon.
1980s	Fuzzy logic was introduced in a fuzzy predictive system used to operate the automated subway trains in Sendai, Japan. This system, designed by Hitachi, reduced energy consumption by 10% and lowered the margin of error in stopping the trains at specified positions to less than 10 centimeters.
1980	First meeting of the American Association for Artificial Intelligence held in Stanford, California.
1980	Commercial AI products were only returning a few million dollars in revenue.
1980	First industrial application of a fuzzy controller by Danish cement manufacturer, F.L. Smidth & Co. A/S to regulate the operation of a cement kiln, which is a complex process subject to random disturbances that made it difficult for an operator to control.
1981	Danny Hillis, a graduate student at MIT’s AI Lab, introduced the unprecedented power of the Connection Machine, a new parallel processing supercomputer, to the computation of AI applications.
1982	David Marr’s book, “Vision,” was published posthumously, Marr died of leukemia in 1980. It provided a new view of how the human brain used shading, steropsis, texture, edges, color, and the frame concept to recognize things. While he put vision firmly on the map as a major AI problem, many of his ideas turned out to be wrong.
1982	John Hopfield showed how networks of simple neurons could be given the ability to calculate.
Early to mid 1980s	A succession of early expert systems were built and put in use by companies. These included: a hydrostatic and rotary bacteria-killing cooker diagnosis program at Campbell’s Soup based on Aldo Cimino’s knowledge; a lathe and grinder diagnosis analyzer at GM’s Saginaw plant using Charlie Amble’s skills at listening for problems based on sounds; a mineral prospecting expert system called PROSPECTOR that found a molybdenum deposit; a Bell system that analyzed problems in telephone networks, and recommended solutions; FOLIO, an investment portfolio advisor; and WILLARD, a forecaster of large thunderstorms. AI groups were formed in many large companies to develop expert systems. Venture capitalists started investing in AI startups, and noted academics joined some of these companies. 1986 sales of AI-based hardware and software were $425 million. Much of the new business were developing specialized hardware (e.g., LISP computers) and software (e.g., expert system shells sold by Teknowledge, Intellicorp, and Inference) to help build better and less expensive expert systems. Low quality, but effective computer vision systems were also commercially launched successfully.
1984	GE built an expert system based on electric locomotive diagnosis knowledge of one expert, David Smith, who was close to retirement. Called the Diesel Electric Locomotive Troubleshooting Aid, it could diagnose 80% of breakdowns, and provide repair instructions.
Mid 1980s	Resurgence of neural network technology, with the publication of key papers by the Parallel Distributed Processing Study Group. Demonstrations of neural networks in diverse applications such as artificial speech generation, learning to play backgammon, and driving a vehicle illustrated the versatility of the technology. A build-up of commercial interest in neural nets followed, with over 300 small companies, mostly startup founded by researchers, competing by 1989. The classic boom-bust cycle of expert systems was being repeated.
1985	MIT’s Media Laboratory, dedicated to researching media-related applications using computer science (including artificial intelligence) and sociology, was founded under Jerome Weisner and Nicholas Negroponte.
1985	Speech systems now able to provide any of the following: a large vocabulary, continuous speech recognition, or speaker independence.
1987	Rodney Brooks, an Australian roboticist, launched Nouvelle AI, with the goal of developing systems that mimic insect intelligence.
1987	Etienne Wenger published his book, “Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge,” a milestone in the development of intelligent tutoring systems.
~1987	Rule-based expert systems start to show limits to their commercially viable size. XCON, the Digital Equipment Company expert system had reached about 10,000 rules, and was increasingly expensive to maintain. Reasons for these limits include: Inflexibility of these expert systems in applying rules, and the tunnel vision implied in their limited knowledge, that can result in poor conclusions. Expert systems couldn’t reverse their logical conclusions if later given contradictory facts. For example, an expert system would conclude that Bill Smith has ten toes because Bill Smith is a person and all people have ten toes. However, it couldn’t then deal with the fact that Bill Smith lost three toes in an industrial accident. A human, using “non-monotonic” reasoning, has no problem concluding Bill Smith has only seven toes. Rule-based expert systems couldn’t draw conclusions from similar past cases. Such analogical reasoning is a common method used by humans. Extracting knowledge from experts who reason analogically and converting that knowledge into rules is problematic. As new rules are added to expert systems, it becomes increasingly difficult to decide the order in which active rules ought to be acted upon. Unexpected effects may occur as new rules are added. This behavior is called opacity. Expert systems don’t know what they don’t know, and might therefore provide wrong answers to questions with answers outside their knowledge. This behavior is called “brittleness.” Expert systems can’t share their knowledge between them because they really don’t have any sense of the words they manipulate, and the same words in different expert systems may not be used in the same ways. Expert systems can’t learn, that is, they can’t establish correspondence and analogies between objects and classes of objects. It was gradually realized that expert systems are limited to “any problem that can be and frequently is solved by your in-house expert in a 10 to 30 minute phone call,” as expressed by Morris W. Firebaugh at the University of Wisconsin.
End of 1980s	Expert systems were increasingly used in industry, and other AI techniques were being implemented jointly with conventional software, often unnoticed but with beneficial effect.
1990s	Emphasis on ontology began. Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and AI researchers study what these kinds are and what their properties are.
1990s and 2000s	AI applications of many, seemingly unrelated kinds are quietly being commercialized in greater and greater numbers. Not all these applications work as well as desired, but they are continually improving. These include: Automatic scheduling software to automatically create better project schedules faster Advanced learning software that works like human tutor in teaching one-on-one with each student Continuous speech recognition programs that accurately turn speech into text Software to manage information for individuals, finding just the documents immediately needed from amongst million of documents, and automatically summarizing documents Face-recognition systems Washing machines that automatically adjust to different conditions to wash clothes better Automatic mortgage underwriting systems Automatic investment decision makers Software that improves the prediction of daily revenues and staffing requirements for a business Credit fraud detection systems Help desk support systems that help find the right answer to any customer’s question faster Shopping bots on the web Data mining tools E-mail filters Automated advice systems that personalize their responses And many, many more. Many commercializers of such products and services aren’t identifying their use of artificial intelligence in their products and services. Probably they’re not doing so because “artificial intelligence” isn’t perceived to sell, while improved intelligent solutions to a customer’s problem does.
Early 1990s	The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign developed and released released the first widely used web browser, named Mosaic.
1991	The U.S. Military’s Dynamic Analysis and Replanning Tool (DART) system brought AI-based decision support to the battlefield during Operation Desert Storm..
1997	Deep Blue, a highly parallel 32-node IBM RS/6000 SP supercomputer, beat Gary Kasparov, world champion of chess. Deep Blue did this by calculating hundreds of million of alternative plays for a number of moves ahead.
1997	Over 40 teams fielded teams of robotic soccer players in the first RoboCup competition.
1999	Sony Corporation introduced the AIBO, a robotic pet dog that understood 100 voice commands, saw the world using computer vision, and learned and matured. AIBO is an acronym for Artificial Intelligence roBOt, and aibo also means ‘love’ or ‘attachment’ in Japanese. On January 26, 2006, Sony announced that it would discontinue the AIBO.
May 17, 1999	An artificial intelligence system, Remote Agent, was given primary control of a spacecraft for the first time. For two days Remote Agent ran on the on-board computer of Deep Space 1, while 60 million miles from earth. The goal of such control systems is to provide less costly, more capable control, that is more independent from ground control. Currently the difficult job of spacecraft control is done by a team of spacecraft engineers. Sharing control with onboard AI systems will enable these people to control more spacecraft, and for more ambitious missions than possible before to be undertaken.
Late 1990s	Tiger Electronics’ wildly popular Furby stuffed animal, equipped with voice-recognition technology, sold over 40 million units between 1998 and 2001, securing a place for AI in the field of consumer electronics.
2000	ASIMO, a humanoid robot that uses AI to perform a multitude of functions, arrived, courtesy of Honda.
2002	iRobot, founded by researchers at the MIT Artificial Intelligence Lab, introduced Roomba, a vacuum cleaning robot. By 2006, two million had been sold.
March 13, 2004	The Defense Advanced Research Projects Agency (DARPA), the central research organization of the United States Department of Defense, sponsored the first DARPA Grand Challenge, a prize competition for autonomous (driverless) vehicles. The first Challenge took place on a desert course between Barstow, California to Primm, Nevada. No vehicles completed the course.
October 8, 2005	Stanley, an autonomous Volkswagen Touareg R5 entered by the Stanford Racing Team, won the DARPA Grand Challenge 2005 and a $2M prize by completing the 212.4 km course in just under 7 hours. 23 vehicles competed, and five completed the course.
2009	Google initiated its effort to develop a self-driving car.
2011	Apple introduced Siri, a personal assistant installed onto its popular iPhone 4S.
2014	IPsoft introduced Amelia, a virtual agent for customer service capable of providing callers with an understanding, empathetic experience.

References

Daniel Crevier, AI: The Tumultuous History of the Search for Artificial Intelligence, Basic Books, 1993. This book provides a readable history of artificial intelligence for the lay person, but is unfortunately out of print.
Ray Kurzweil, The Age of Spiritual Machines: When Computers Exceed Human Intelligence, Viking, 1999. This book has a detailed time line, which as well as going back to the origin of the universe, boldly forecasts the future of intelligent machines through 2099.
Arturo Sangalli, The Importance of Being Fuzzy and Other Insights from the Border between Math and Computers, Princeton University Press, 1998. This book provides a good introduction to fuzzy logic, neural networks, and genetic algorithms for the non-expert.