From Cave Paintings to the Internet A Chronological and Thematic Database on the History of Information and Media Artificial Intelligence Timeline

In a letter to theologian, philosopher, and mathematician Marin Mersenne, philosopher, mathematician and physicist René Descartes proposes an artificial universal language, with equivalent ideas in different tongues sharing one symbol:

"Et si quelqu’un avait bien expliqué quelles sont les idées simples qui sont en l’imagination des hommes, desquelles se compose tout ce qu’ils pensent, et que cela fût reçu par tout le monde, j’oserais espérer ensuite une langue universelle, fort aisée à apprendre, à prononcer et à écrire."

"The notion of a universal language was based upon the idea of precisely cataloging the elements of the human imagination. The great advantage of such a language would be that it would represent everything 'distinctement.' Yet, the great problem faced by someone who wanted to create such a language was the nature of the human imagination itself. Although separate from the mind and reason, which were the foundations of Cartesian thought, the imagination nevertheless played an important role for Descartes. As he wrote elsewhere in the Meditations, the imagination not only conceptualized external things but also considers them, 'as being present by the power and internal application of my mind.' Imagination, in other words, produced the illusion of presence, figures appearing so that can the person can 'look upon them as present with the eyes of my mind.' As a result, Descartes remains highly suspicious of the imagination because it can produce appearances that have no corresponding reality. Descartes concluded his letter to Mersenne by dismissing hopes for a universal language or a real character as only being possible in a 'terrestrial paradise' or 'fairyland' because of the confused nature of signification and the variation of human understanding.

"Mais n’espérez pas de la voir jamais en usage; cela présuppose de grands changements en l’ordre des choses, et il faudrait que tout le Monde ne fût qu’un paradis terrestre, ce qui n’est bon à proposer que dans le pays des romans.

 "A universal language that would work at the level of the imagination, describing the actual 'things' of the external world, could only produce uniform results in the perfection of Eden or the ideal of fiction. One should, instead, stick with the institution of geometry as a method of rationalizing nature, a divine language grounded upon the cogito’s transmission of being. Descartes ultimately remains skeptical about any possibility of using alternative language games aside from mathematics in the project of rationalizing the world" (Batchelor, The Republic of Codes: Cryptographic Theory and Scientific Networks in the Seventeenth Century [1999] http://www.stanford.edu/dept/HPS/writingscience/Cryptography.html, accessed 01-22-2010).

Two years after his death English clergyman and mathematician Thomas Bayes's "An Essay Towards Solving a Problem in the Doctrine of Chances" is published in the Philosophical Transactions of the Royal Society 53 (1763) 370-418.

Bayes's paper enunciated Bayes's Theorem for calculating "inverse probabilities”—the basis for methods of extracting patterns from data in decision analysis, data mining, statistical learning machines, Bayesian networks, Bayesian inference.

Hook & Norman, Origins of Cyberspace (2002) no. 1.

Wolfgang von Kempelen builds his chess-playing Turk, an automaton that purports to play chess.

Although the machine displayed an elaborate gear mechanism, its cabinet actually concealed a small human controlling the moves of the machine. Von Kempelen's Turk became a commercial sensation, deceiving a very large number of people. It became the most famous, or the most notorious, automaton in history.

According to to a magazine article by Edgar Allan Poe, the original Turk was exhibited in Richmond, Virginia as late as 1836.

Even though the machine intelligence exhibited by the Turk was an illusion, von Kempelen's automaton was much later viewed as an analog to efforts in computer chess and artificial intelligence.

American writer, poet, editor, literary critic, and magazinist Edgar Allan Poe publishes in the Southern Literary Messenger "Maelzel's Chess Player."

In this article on automata Poe provides a very closely reasoned explanation of the concealed human operation of  von Kempelen's Turk, which Poe had seen exhibited in Richmond, Virginia by Maelzel a few weeks earlier. 

Poe also briefly compares von Kempelen's Turk to Babbage's Difference Engine No. 1, which was limited to the computation of astronomical and navigation tables, suggesting essentially that if the Turk was fully automated and had the ability to use the results of one logical operation to make a decision about the next one—what was later called "conditional branching" —it would be far superior to Babbage's machine.  This feature was, of course, later designed into Babbage's Analytical Engine. 

Here is Poe's comparison of the two machines:

"But if these machines were ingenious, what shall we think of the calculating machine of Mr. Babbage? What shall we think of an engine of wood and metal which can not only compute astronomical and navigation tables to any given extent, but render the exactitude of its operations mathematically certain through its power of correcting its possible errors? What shall we think of a machine which can not only accomplish all this, but actually print off its elaborate results, when obtained, without the slightest intervention of the intellect of man? It will, perhaps, be said, in reply, that a machine such as we have described is altogether above comparison with the Chess-Player of Maelzel. By no means — it is altogether beneath it — that is to say provided we assume (what should never for a moment be assumed) that the Chess-Player is a pure machine, and performs its operations without any immediate human agency. Arithmetical or algebraical calculations are, from their very nature, fixed and determinate. Certain data being given, certain results necessarily and inevitably follow. These results have dependence upon nothing, and are influenced by nothing but the data originally given. And the question to be solved proceeds, or should proceed, to its final determination, by a succession of unerring steps liable to no change, and subject to no modification. This being the case, we can without difficulty conceive the possibility of so arranging a piece of mechanism, that upon starting it in accordance with the data of the question to be solved, it should continue its movements regularly, progressively, and undeviatingly towards the required solution, since these movements, however complex, are never imagined to be otherwise than finite and determinate. But the case is widely different with the Chess-Player. With him there is no determinate progression. No one move in chess necessarily follows upon any one other. From no particular disposition of the men at one period of a game can we predicate their disposition at a different period. Let us place the first move in a game of chess, in juxta-position with the data of an algebraical question, and their great difference will be immediately perceived. From the latter — from the data — the second step of the question, dependent thereupon, inevitably follows. It is modelled by the data. It must be thus and not otherwise. But from the first move in the game of chess no especial second move follows of necessity. In the algebraical question, as it proceeds towards solution, the certainty of its operations remains altogether unimpaired. The second step having been a consequence of the data, the [column 2:] third step is equally a consequence of the second, the fourth of the third, the fifth of the fourth, and so on, and not possibly otherwise, to the end. But in proportion to the progress made in a game of chess, is the uncertainty of each ensuing move. A few moves having been made, no step is certain. Different spectators of the game would advise different moves. All is then dependent upon the variable judgment of the players. Now even granting (what should not be granted) that the movements of the Automaton Chess-Player were in themselves determinate, they would be necessarily interrupted and disarranged by the indeterminate will of his antagonist. There is then no analogy whatever between the operations of the Chess-Player, and those of the calculating machine of Mr. Babbage, and if we choose to call the former a pure machine we must be prepared to admit that it is, beyond all comparison, the most wonderful of the inventions of mankind. Its original projector, however, Baron Kempelen, had no scruple in declaring it to be a "very ordinary piece of mechanism — a bagatelle whose effects appeared so marvellous only from the boldness of the conception, and the fortunate choice of the methods adopted for promoting the illusion." But it is needless to dwell upon this point. It is quite certain that the operations of the Automaton are regulated by mind, and by nothing else. Indeed this matter is susceptible of a mathematical demonstration, a priori. The only question then is of the manner in which human agency is brought to bear. Before entering upon this subject it would be as well to give a brief history and description of the Chess-Player for the benefit of such of our readers as may never have had an opportunity of witnessing Mr. Maelzel's exhibition."

Mathematician, physicist, and economist John von Neumann publishes "Zur Theorie der Gesellschaftsspiele" in Mathematische Annalen, 100, 295–300. This paper "On the Theory of Parlor Games" propounds the minimax theorem, inventing the theory of games.

At Princeton University  Alan Turing and John von Neumann have their first discussions about computing and what will later be called “artificial intelligence” (AI).

Mathematician, physicist, and economist John von Neumann and economist Oskar Morgenstern publish The Theory of Games and Economic Behavior.

Quantitative mathematical models for games such as poker or bridge at one time appeared impossible, since games like these involve free choices by the players at each move, and each move reacts to the moves of other players. However, in the 1920s John von Neumann single-handedly invented game theory, introducing the general mathematical concept of "strategy" in a paper on games of chance (Mathematische Annalen 100 [1928] 295-320). This contained the proof of his "minimax" theorem that says "a strategy exists that guarantees, for each player, a maximum payoff assuming that the adversary acts so as to minimize that payoff." The "minimax" principle, a key component of the game-playing computer programs developed in the 1950s and 1960s by Arthur Samuel, Allen Newell, Herbert Simon, and others was more fully articulated and explored in The Theory of Games and Economic Behavior, co-authored by von Neumann and Morgenstern.

Game theory, which draws upon mathematical logic, set theory and functional analysis, attempts to describe in mathematical terms the decision-making strategies used in games and other competitive situations. The Von Neumann-Morgenstern theory assumes (1) that people's preferences will remain fixed throughout; (2) that they will have wide knowledge of all available options; (3) that they will be able to calculate their own best interests intelligently; and (4) that they will always act to maximize these interests. Attempts to apply the theory in real-world situations have been problematical, and the theory has been criticized by many, including AI pioneer Herbert Simon, as failing to model the actual decision-making process, which typically takes place in circumstances of relative ignorance where only a limited number of options can be explored.

Von Neumann revolutionized mathematical economics. Had he not suffered an early death from cancer in 1957, most probably he would have received the first Nobel Prize in economics. (The first Nobel prize in economics was awarded in 1969; it cannot be awarded posthumously.) Several mathematical economists influenced by von Neumann's ideas later received the Nobel Prize in economics. 

Hook & Norman, Origins of Cyberspace (2002) no. 953.

Alan Turing writes a report for the National Physical Laboratory entitled Intelligent Machinery.

In the report Turing stated that a thinking machine should be given the blank mind of an infant instead of an adult mind filled with opinions and ideas. The report contained an early discussion of neural networks. Turing estimated that it would take a battery of programmers fifty years to bring this learning machine from childhood to adult mental maturity. The report was not published until 1968.

Alan Turing publishes Computing Machinery and Intelligence, in which he describes the “Turing test" for determining whether a machine is “intelligent.” (See Reading 11.2)

Claude Shannon publishes Programming a computer for playing chess, the first technical paper on computer chess. (See Reading 11.3.)

John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon invite participants to a summer session at Dartmouth College to conduct research on what they call Artificial Intelligence (AI), thereby coining the term. (See Reading 11.5.)

At the Dartmouth summer session on artificial intelligence, Allen Newell and Herbert Simon demonstrate the first AI program, the Logic Theorist, to find the basic equations of logic as defined in Principia Mathematica by Whitehead and Russell.

For one of the equations, the Logic Theorist surpassed its inventors’ expectations by finding a new and better proof. This was the “the first foray by artificial intelligence research into high-order intellectual processes” (Feigenbaum and Feldman, Computers and Thought [1963]).

Noam Chomsky publishes "Three Models for the Description of Language" in IRE Transactions on Information Theory IT-2  113-24.

In this work read at a symposium on information theory held at MIT a few months before the publication of his Syntactic Structures (1957), Chomsky introduced two key concepts— 'Chomsky's hierarchy' of syntactic forms, and transformational-generative grammar theory.  The latter attempts to define rules that can generate the infinite number of grammatical (well-formed) sentences possible in a language, and works to identify rules (transformations) that govern relations between parts of a sentence, on the assumption that beneath such aspects as word order a fundamental deep structure exists.

Hook & Norman, Origins of Cyberspace (2002) no. 531.

American mathematician and researcher in artificial intelligence Ray Solomonoff publishes "An Inductive Inference Machine". IRE Convention Record, Section on Information Theory, Part 2,  56-62. This was the first paper written on machine learning. It emphasized the importance of training sequences, and the use of parts of previous solutions to problems in constructing trial solutions to new problems. Solomonoff presented an early version of this paper at the 1956 Dartmouth Summer Research Conference on Artificial Intelligence.  A copy of that version is available at this link.

Allan Newell, Clifford Shaw, and Herbert Simon invent “game tree pruning,” an artificial intelligence technique.

Frank Rosenblatt invents the Perceptron, or Mark I at Cornell University. Completed in 1960, this was the first computer that could learn new skills by trial and error, using a type of neural network that simulated human thought processes.

The National Physical Laboratory at Teddington, England holds the first international symposium on artificial intelligence, calling it Mechanisation of Thought Processes.

At this conference John McCarthy delivered his paper Programs with Common Sense.(See Reading 11.6.)

Arthur Lee Samuel publishes "Some Studies in Machine Learning Using the Game of Checkers," IBM Journal of Research and Development 3 (1959) no. 3, 210-29.

In this work Samuel demonstrated that machines can learn from past errors — one of the earliest examples of non-numerical computation.

Hook & Norman, Origins of Cyberspace (2002) no. 874.

Robert S. Ledley and Lee B. Lusted publish "Reasoning Foundations of Medical Diagnosis," Science, 130, no. 3366, 9-21.

This was highly influential in the development of clinical decision support systems (CDSS) — interactive computer programs,  or expert systems, designed to assist physicians and other health professionals with decision making tasks.

John McCarthy introduces LISP (LISt Processor), the language of choice for artificial intelligence (AI) programming.

British science fiction writer, inventor and futurist Arthur C. Clarke publishes a short story entitled "Dial F for Frankenstein.

". . . it foretold an ever-more-interconnected telephone network that spontaneously acts like a newborn baby and leads to global chaos as it takes over financial, transportation and military systems" (John Markoff, "The Coming Superbrain," New York Times, May 24, 2009).

"The father of the internet, Sir Tim Berners-Lee credits Clarke's short story, Dial F for Frankenstein, as an inspiration" (http://www.independent.co.uk/news/science/arthur-c-clarke-science-fiction-turns-to-fact-799519.html, accessed 05-24-2009).

Woodbrow W. "Bledsoe, along with Helen Chan and Charles Bisson, researched programming computers to recognize human faces (Bledsoe 1966a, 1966b; Bledsoe and Chan 1965). Because the funding was provided by an unnamed intelligence agency, little of the work was published. Given a large database of images—in effect, a book of mug shots—and a photograph, the problem was to select from the database a small set of records such that one of the image records matched the photograph. The success of the program could be measured in terms of the ratio of the answer list to the number of records in the database. Bledsoe (1966a) described the following difficulties:

" 'This recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, etc. Some other attempts at facial recognition by machine have allowed for little or no variability in these quantities. Yet the method of correlation (or pattern matching) of unprocessed optical data, which is often used by some researchers, is certain to fail in cases where the variability is great. In particular, the correlation is very low between two pictures of the same person with two different head rotations.'

"This project was labeled man-machine because the human extracted the coordinates of a set of features from the photographs, which were then used by the computer for recognition. Using a GRAFACON, or RAND TABLET, the operator would extract the coordinates of features such as the center of pupils, the inside corner of eyes, the outside corner of eyes, point of widows peak, and so on. From these coordinates, a list of 20 distances, such as width of mouth and width of eyes, pupil to pupil, were computed. These operators could process about 40 pictures an hour. When building the database, the name of the person in the photograph was associated with the list of computed distances and stored in the computer. In the recognition phase, the set of distances was compared with the corresponding distance for each photograph, yielding a distance between the photograph and the database record. The closest records are returned.

"This brief description is an oversimplification that fails in general because it is unlikely that any two pictures would match in head rotation, lean, tilt, and scale (distance from the camera). Thus, each set of distances is normalized to represent the face in a frontal orientation. To accomplish this normalization, the program first tries to determine the tilt, the lean, and the rotation. Then, using these angles, the computer undoes the effect of these transformations on the computed distances. To compute these angles, the computer must know the three-dimensional geometry of the head. Because the actual heads were unavailable, Bledsoe (1964) used a standard head derived from measurements on seven heads.

"After Bledsoe left PRI [Panoramic Research, Inc.] in 1966, this work was continued at the Stanford Research Institute, primarily by Peter Hart. In experiments performed on a database of over 2000 photographs, the computer consistently outperformed humans when presented with the same recognition tasks (Bledsoe 1968). Peter Hart (1996) enthusiastically recalled the project with the exclamation, 'It really worked!' " (Faculty Council, University of Texas at Austin, In Memoriam Woodrow W. Bledsoe, accessed 05-15-2009).

Bledsoe, W. W. 1964. The Model Method in Facial Recognition, Technical Report PRI 15, Panoramic Research, Inc., Palo Alto, California.

Bledsoe, W. W., and Chan, H. 1965. A Man-Machine Facial Recognition System-Some Preliminary Results, Technical Report PRI 19A, Panoramic Research, Inc., Palo Alto, California.

Bledsoe, W. W. 1966a. Man-Machine Facial Recognition: Report on a Large-Scale Experiment, Technical Report PRI 22, Panoramic Research, Inc., Palo Alto, California.

Bledsoe, W. W. 1966b. Some Results on Multicategory Patten Recognition. Journal of the Association for Computing Machinery 13(2):304-316.

Bledsoe, W. W. 1968. Semiautomatic Facial Recognition, Technical Report SRI Project 6693, Stanford Research Institute, Menlo Park, California.

American mathematician and researcher in artificial intelligence Ray Solomonoff publishes "A Formal Theory of Inductive Inference, Part I" Information and Control, 7, No. 1, 1-22,  and  "A Formal Theory of Inductive Inference, Part II," Information and Control, 7, No. 2,  224-254.

This two-art paper is considered the beginning of algorithmic informatiion theory.

Solomonoff first described his results at a Conference at Caltech, 1960, and in a report of February, 1960: "A Preliminary Report on a General Theory of Inductive Inference."

Irving John Good, originally named Isidore Jacob Gudak, publishes "Speculations Concerning the First Ultraintelligent Machine," Advances in Computers, vol. 6 (1965) 31ff.

This paper originated the concept later known as "technological singularity," which anticipates the eventual existence of superhuman intelligence:

"Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make." 

Stanley Kubrick consulted Good regarding aspects of computing and artificial intelligence when filming 2001: A Space Odyssey (1968), one of whose principal characters was the paranoid HAL 9000 supercomputer.

2001 is noticed in this database.

Philosopher, mathematician and computer scientist John Alan Robinson publishes "A Machine-Oriented Logic Based on the Resolution Principle", Communications of the ACM, 5:23–41.

This paper introduced the resolution principle, a standard of logical deduction in AI applications.

The film 2001: A Space Odyssey, written by American film director Stanley Kubrick in collaboration with science fiction writer and futurist Arthur C. Clarke, captures imaginations with the idea of a computer that can see, speak, hear, and “think.” 

Perhaps the star of the film was the HAL 9000 computer. "HAL (Heuristically programmed ALgorithmic Computer) is an artificial intelligence, the sentient on-board computer of the spaceship Discovery. HAL is usually represented only as his television camera "eyes" that can be seen throughout the Discovery spaceship. . . . HAL is depicted as being capable not only of speech recognition, facial recognition, and natural language processing, but also lip reading, art appreciation, interpreting emotions, expressing emotions, reasoning, and chess, in addition to maintaining all systems on an interplanetary voyage.

"HAL is never visualized as a single entity. He is, however, portrayed with a soft voice and a conversational manner. This is in contrast to the human astronauts, who speak in terse monotone, as do all other actors in the film" (Wikipedia article on HAL 9000, accessed 05-24-2009).

"Kubrick and Clarke had met in New York City in 1964 to discuss the possibility of a collaborative film project. As the idea developed, it was decided that the story for the film was to be loosely based on Clarke's short story "The Sentinel", written in 1948 as an entry in a BBC short story competition. Originally, Clarke was going to write the screenplay for the film, but Kubrick suggested during one of their brainstorming meetings that before beginning on the actual script, they should let their imaginations soar free by writing a novel first, which the film would be based on upon its completion. 'This is more or less the way it worked out, though toward the end, novel and screenplay were being written simultaneously, with feedback in both directions. Thus I rewrote some sections after seeing the movie rushes -- a rather expensive method of literary creation, which few other authors can have enjoyed.' The novel ended up being published a few months after the release of the movie" (Wikipedia article on Arthur C. Clarke, accessed 05-24-2009).

Architect and computer scientist Nicholas Negroponte of MIT publishes The Architecture Machine.

Negroponte's pioneering and forward-looking book described early research on computer-aided design, and in so doing covered early work on human-computer interaction, artificial intelligence, and computer graphics. It contained a large number of illustrations.

"Most of the machines that I will be discussing do not exist at this time. The chapters are primarily extrapolations into the future derived from experiences with various computer-aided design systems. . . .

"There are three possible ways in which machines can assist the design process: (1) current procedures can be automated, thus speeding up and reducing the cost of existing practices; (2) existing methods can be altered to fit within the specifications and constitution of a machine, where only those issues are considered that are supposedly machine-compatible; (3) the design process, considered as evolutionary, can be presented to a machine, also considered as evolutionary, and a mutal training, resilience, and growth can be developed" (From Negroponte's "Preface to a Preface," p. [6]).

This book has been called the first book on the personal computer. On that I do not agree. The book contains only vague discussions of the possiblity of eventual personal computers. Most specifically it says, as caption to its second illustration, a cartoon relating to a home computer, "The computer at home is not a fanciful concept. As the cost of computation lowers, the computer utility will become a consumer item, and every child should have one." Instead The Architecture Machine may be the first book on human-computer interaction, and on the possibilities of computer-aided design.

The American Association for Artificial Intelligence is founded.

In 2007 the organization changed its name to the Association for the Advancement of Artificial Intelligence. In 2009 it had over 6,000 members worldwide.

Psychologist and cognitive scientist George A. Miller and team begin development of WordNet, a lexical database for the English language.

WordNet "groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications" (Wikipedia article on WordNet). You can browse Wordnet at http://wordnet.princeton.edu/.

WordNet has been used for a number of different purposes in information systems, including word sense disambiguation, information retrieval, automatic text classification, automatic text summarization, and even automatic crossword puzzle generation.

"In 1985, in Hamburg, I played against thirty-two different chess computers at the same time in what is known as a simultaneous exhibition. I walked from one machine to the next, making my moves over a period of more than five hours. The four leading chess computer manufacturers had sent their top models, including eight named after me from the electronics firm Saitek.  

"It illustrates the state of computer chess at the time that it didn't come as much of a surprise when I achieved a perfect 32–0 score, winning every game, although there was an uncomfortable moment. At one point I realized that I was drifting into trouble in a game against one of the "Kasparov" brand models. If this machine scored a win or even a draw, people would be quick to say that I had thrown the game to get PR for the company, so I had to intensify my efforts. Eventually I found a way to trick the machine with a sacrifice it should have refused. From the human perspective, or at least from my perspective, those were the good old days of man vs. machine chess" (Gary Kasparov, "The Chess Master and the Computer," The New York Review of Books 57 February 11, 2010.

Psychologist, neural scientist and cognitive scientist James A. Anderson publishes "The BSB Model: A simple non-linear autoassociative network," M. Hassoun (Ed), Associative Neural Memories: Theory and Implementation (1993).

Anderson's neural networks have been applied to models of human concept formation, decision making, speech perception, and models of vision.

Anderson, J. A., Spoehr, K. T. and Bennett, D.J.  "A study in numerical perversity: Teaching arithmetic to a neural network,"  D.S. Levine and M. Aparicio (Eds.) Neural Networks for Knowledge Representation and Inference, (1994).

Mathematician, computer scientist and science fiction writer Vernor Vinge calls the creation of the first ultraintelligent machine the Singularity in Omni magazine.

Vinge's follow-up paper entitled "What is the Singularity?" presented at the VISION-21 Symposium sponsored by NASA Lewis Research Center and the Ohio Aerospace Institute, March 30-31, 1993, and  slightly changed in the Winter 1993 issue of Whole Earth Review, contains the oft-quoted statement,

"Within thirty years, we will have the technological means to create superhuman intelligence. Shortly thereafter, the human era will be ended."

"Vinge refines his estimate of the time scales involved, adding, 'I'll be surprised if this event occurs before 2005 or after 2030.

"Vinge continues by predicting that superhuman intelligences, however created, will be able to enhance their own minds faster than the humans that created them. 'When greater-than-human intelligence drives progress," Vinge writes, "that progress will be much more rapid.' This feedback loop of self-improving intelligence, he predicts, will cause large amounts of technological progress within a short period of time" (Wikipedia article on Technological singularity, accessed 05-24-2009).

At the Second Man-Machine World Championship, Chinook, a computer checkers program developed around 1989 at the University of Alberta by a team led by Jonathan Schaeffer, wins due to human frailty.

This was the first time that a computer program defeated a human champion in a game competition.  "In 1996 the Guinness Book of World Records recognized Chinook as the first program to win a human world championship" (http://webdocs.cs.ualberta.ca/~chinook/project/, accessed 01-24-2010).

Gary Kasparov, sometimes regarded as the greatest chess player of all time, resigns 19 moves into Game 6 against Deep Blue, an IBM RS/6000 SP supercomputer capable of calculating 200 million chess positions per second.

This was the first time that a human world chess champion lost to a computer under tournament conditions.

The event was broadcast live from IBM's website via a Java viewer, and became the world's record "Net event" at the time.

"The AI crowd, too, was pleased with the result and the attention, but dismayed by the fact that Deep Blue was hardly what their predecessors had imagined decades earlier when they dreamed of creating a machine to defeat the world chess champion. Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating 200 million possible moves on the chess board per second and winning with brute number-crunching force. As Igor Aleksander, a British AI and neural networks pioneer, explained in his 2000 book, How to Build a Mind:  

" 'By the mid-1990s the number of people with some experience of using computers was many orders of magnitude greater than in the 1960s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.'

"It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Not that losing to a $10 million alarm clock made me feel any better" (Gary Kasparov, "The Chess Master and the Computer," The New York Review of Books, 57, February 11, 2010).

Cognitive scientist / entrepeneur Jeffrey Stibel, physicist, psychologist, neural scientist  James A. Anderson, and others create a word sense disambiguator using George A. Miller's WordNet lexical database.

Stibel and others applied this technology in Simpli, "an early search engine that offered disambiguation to search terms. A user could enter in a search term that was ambiguous (e.g., Java) and the search engine would return a list of alternatives (coffee, programming language, island in the South Seas)."

"The technology was rooted in brain science and built by academics to model the way in which the mind stored and utilized language."

"Simpli was sold in 2000 to NetZero. Another company that leveraged the Simpli WordNet technology was purchased by Google and they continue to use the technology for search and advertising under the brand Google AdSense.

"In 2001, there was a buyout of the company and it was merged with another company called Search123. Most of the original members joined the new company. The company was later sold in 2004 to ValueClick, which continues to use the technology and search engine to this day" (Wikipedia article on Simpli, accessed 05-10-2009).

American director, screen writer and film producer Steven Spielberg directs, co-authors and produces the science fiction film A.I. Artificial Intelligence, telling the story of David, an android robot child programmed with the ability to love and to dream. The film explores the hopes and fears involved with efforts to simulate human thought processes, and the social consequences of creating robots that may be better than people at specialized tasks.

The film was a 1970s project of Stanley Kubrick, who eventually turned it over to Spielberg. The project languished in development hell for nearly three decades before technology advanced sufficiently for a successful production. The film required enormously complex puppetry, computer graphics, and make-up prosthetics, which are well-described and explained in the supplementary material in the two-disc special edition of the film issued on DVD in 2002.

Jonathan Schaeffer and his team at the University of Alberta announce that checkers is solved. Perfect play leads to a draw.

"The crucial part of Schaeffer's computer proof involved playing out every possible endgame involving fewer than 10 pieces. The result is an endgame database of 39 trillion positions. By contrast, there are only 19 different opening moves in draughts. Schaeffer's proof shows that each of these leads to a draw in the endgame database, providing neither player makes a mistake.  

"Schaeffer was able to get his result by searching only a subset of board positions rather than all of them, since some of them can be considered equivalent. He carried out a mere 1014 calculations to complete the proof in under two decades. 'This pushes the envelope as far as artificial intelligence is concerned,' he says.  

"At its peak, Schaeffer had 200 desktop computers working on the problem full time, although in later years he reduced this to 50 or so. 'The problem is such that if I made a mistake 10 years ago, all the work from then on would be wrong,' says Schaeffer. 'So I've been fanatical about checking for errors.' " (http://www.newscientist.com/article/dn12296-checkers-solved-after-years-of-number-crunching.html, accessed 01-24-2010).

Based on this proof, Schaeffer's checkers-playing program Chinook, can no longer be beaten. The best an opponent can hope for is a draw.

Michael Schmidt and Hod Lipson of Cornell University publish "Distilling Free-Form Natural Laws from Experimental Data," Science 3 April 2009: Vol. 324. no. 5923, pp. 81 - 85 DOI: 10.1126/science.1165893.  The paper describes a computer program that sifts raw and imperfect data to uncover fundamental laws of nature.

"For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems" (Abstract from Science)

Ross D. King, Jem Rowland and 11 co-authors from the Department of Computer Science at Aberystwyth University and the University of Cambridge, publish "The Automation of Science," Science 3 April 2009: Vol. 324. no. 5923, pp. 85 - 89 DOI: 10.1126/science.1165620.

They describe a Robot Scientist which the researchers believe is the first machine to have independently discovered new scientific knowledge. The robot, called Adam, is a computer system that fully automates the scientific process. 

"Prof Ross King, who led the research at Aberystwyth University, said: 'Ultimately we hope to have teams of human and robot scientists working together in laboratories'. The scientists at Aberystwyth University and the University of Cambridge designed Adam to carry out each stage of the scientific process automatically without the need for further human intervention. The robot has discovered simple but new scientific knowledge about the genomics of the baker's yeast Saccharomyces cerevisiae, an organism that scientists use to model more complex life systems. The researchers have used separate manual experiments to confirm that Adam's hypotheses were both novel and correct" (http://www.eurekalert.org/pub_releases/2009-04/babs-rsb032709.php).

"The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge" (Abstract in Science).

Stephen Wolfram and Wolfram Research launch Wolfram|Alpha, a computational data engine with a new approach to knowledge extraction, based on natural language processing, a large library of algorithms and an NKS (New Kind of Science) approach to answering queries.

The Wolfram|Alpha engine differs from traditional search engines in that it does not simply return a list of results based on a query, but instead computes an answer.

"Researchers in Israel say they have developed a computer program that can decipher previously unreadable ancient texts and possibly lead the way to a Google-like search engine for historical documents.

"The program uses a pattern recognition algorithm similar to those law enforcement agencies have adopted to identify and compare fingerprints.

"But in this case, the program identifies letters, words and even handwriting styles, saving historians and liturgists hours of sitting and studying each manuscript.

"By recognizing such patterns, the computer can recreate with high accuracy portions of texts that faded over time or even those written over by later scribes, said Itay Bar-Yosef, one of the researchers from Ben-Gurion University of the Negev.

" 'The more texts the program analyses, the smarter and more accurate it gets,' Bar-Yosef said.

"The computer works with digital copies of the texts, assigning number values to each pixel of writing depending on how dark it is. It separates the writing from the background and then identifies individual lines, letters and words.

"It also analyses the handwriting and writing style, so it can 'fill in the blanks' of smeared or faded characters that are otherwise indiscernible, Bar-Yosef said.

"The team has focused their work on ancient Hebrew texts, but they say it can be used with other languages, as well. The team published its work, which is being further developed, most recently in the academic journal Pattern Recognition due out in December but already available online. A program for all academics could be ready in two years, Bar-Yosef said. And as libraries across the world move to digitize their collections, they say the program can drive an engine to search instantaneously any digital database of handwritten documents. Uri Ehrlich, an expert in ancient prayer texts who works with Bar-Yosef's team of computer scientists, said that with the help of the program, years of research could be done within a matter of minutes. 'When enough texts have been digitized, it will manage to combine fragments of books that have been scattered all over the world,' Ehrlich said" (http://www.reuters.com/article/newsOne/idUSTRE58141O20090902, accessed 09-02-2009).

Microsoft announces a deal that will bring the Wolfram Alpha search tool to its Bing search engine.

"The company said that the deal will allow users to take advantage of the Wolfram Alpha algorithms and search tools within Bing queries.

"The initial partnership, which is expected to bear fruit within a few days, will focus on providing nutritional information to users as well as certain mathematical tools. When users search for foods or recipes, the engine will display a small tab containing nutritional information.  

"Along with increasing traffic to the Bing service, Microsoft hopes that the features will allow users to better monitor their diet and exercise plans.

" 'This notion of creating and presenting computational knowledge in search results is one of the more exciting things going on in search (and beyond) today, and the team at Bing is incredibly fired up to bring some of this amazing work to our customers,' " programme managers Tracey Yao and Pedro Silva said in a blog posting.  

"The Wolfram Alpha partnership is one of several campaigns Microsoft has embarked on to drum up traffic for Bing. Other recent additions include visual search results and the ability to search within a user's Hotmail archives" (http://www.v3.co.uk/v3/news/2253013/microsoft-gives-further-updates)

Artificial intelligence engineer Douglas Hines of TrueCompanion.com introduces Roxxxy at the AVN Adult Entertainment Expo in Las Vegas, Nevada.

" 'She doesn't vacuum or cook, but she does almost everything else,' said her inventor, Douglas Hines, who unveiled Roxxxy last month at the Adult Entertainment Expo in Las Vegas, Nevada.

"Lifelike dolls, artificial sex organs and sex-chat phone lines have been keeping the lonely company for decades. But Roxxxy takes virtual companionship to a new level. Powered by a computer under her soft silicone ;skin,; she employs voice-recognition and speech-synthesis software to answer questions and carry on conversations. She even comes loaded with five distinct 'personalities,' from Frigid Farrah to Wild Wendy, that can be programmed to suit customers' preferences.

" 'There's a tremendous need for this kind of product,' said Hines, a computer scientist and former Bell Labs engineer. Roxxxy won't be available for delivery for several months, but Hines is taking pre-orders through his Web site, TrueCompanion.com, where thousands of men have signed up. 'They're like, 'I can't wait to meet her,' ' Hines said. 'It's almost like the anticipation of a first date.' Women have inquired about ordering a sex robot, too. Hines says a female sex therapist even contacted him about buying one for her patients.

"Roxxxy has been like catnip to talk-show hosts since her debut at AEE, the largest porn-industry convention in the country. In a recent monologue, Jay Leno expressed amazement that a sex robot could carry on lifelike conversations and express realistic emotions. 'Luckily, guys,' he joked, 'there's a button that turns that off.' Curious conventioneers packed Hines' AEE booth last month in Las Vegas, asking questions and stroking Roxxxy's skin as she sat on a couch in a black negligee.

" 'Roxxxy generated a lot of buzz at AEE,' said Grace Lee, spokeswoman for the porn-industry convention. 'The prevailing sentiment of everyone I talked to about Roxxxy is 'version 1.0,' but people were fascinated by the concept, and it caused them to rethink the possibilities of 'sex toys.' '

"Hines, a self-professed happily married man from Lincoln Park, New Jersey, says he spent more than three years developing the robot after trying to find a marketable application for his artificial-intelligence technology. Roxxxy's body is made from hypoallergenic silicone -- the kind of stuff in prosthetic limbs -- molded over a rigid skeleton. She cannot move on her own but can be contorted into almost any natural position. To create her shape, a female model spent a week posing for a series of molds. The robot runs on a self-contained battery that lasts about three hours on one charge, Hines says. Customers can recharge Roxxxy with an electrical cord that plugs into her back.

"A motor in her chest pumps heated air through a tube that winds through the robot's body, which Hines says keeps her warm to the touch. Roxxxy also has sensors in her hands and genital areas -- yes, she is anatomically correct -- that will trigger vocal responses from her when touched. She even shudders to simulate orgasm. When someone speaks to Roxxxy, her computer converts the words to text and then uses pattern-recognition software to match them against a database containing hundreds of appropriate responses. The robot then answers aloud -- her prerecorded "voice" is supplied by an unnamed radio host -- through a loudspeaker hidden under her wig.

" 'Everything you say to her is processed. It's very near real time, almost without delay,' Hines said of the dynamics of human-Roxxxy conversation. 'To make it as realistic as possible, she has different dialogue at different times. She talks in her sleep. She even snores.' (The snoring feature can be turned off, he says.) Roxxxy understands and speaks only English for now, but Hines' True Companion company is developing Japanese and Spanish versions. For an extra fee, he'll also record customizable dialogue and phrases for each client, which means Roxxxy could talk to you about NASCAR, say, or the intricacies of politics in the Middle East" (http://www.cnn.com/2010/TECH/02/01/sex.robot/, accessed 02-06-2010).