Part of NSF's Recovering MIT's AI Film History Project. "Here you will find a rough chronology of some of AI's most influential projects. It is intended for both non-scientists and those ready to continue experimentation and research tomorrow. Included is a taste of who the main players have been, concepts they and their projects have explored and how the goals of AI have evolved and changed over time. Many will be surprised that some of what we now consider obvious tools like search engines, spell check and spam filters are all outcroppings of AI research."
Computer scientist Brian Randell was the man who started uncovering the history of Colossus.That history had to be prised out of the archives because official efforts to cover up its success worked so well. Thousands of people worked in the huts at Bletchley Park during WWII on code-cracking but only a handful were involved with Colossus and fewer still knew everything about it.
Computer scientist Brian Randell was the man who started uncovering the history of Colossus.
That history had to be prised out of the archives because official efforts to cover up its success worked so well. Thousands of people worked in the huts at Bletchley Park during WWII on code-cracking but only a handful were involved with Colossus and fewer still knew everything about it.
Turing's genius was to compare machines with humans. To any outsider it was an unlikely place, but the modern era of computers began on May 28, 1936, when the editors of the Proceedings of the London Mathematical Society received a paper with the rather cumbersome title, "On Computable Numbers, With an Application to the Entscheidungsproblem" by Alan M. Turing. Turing's 1936 paper on computable numbers hit that rare bull's eye where philosophy and discovery overlap. But unlike Church, who used the standard abstractions of pure mathematics in his argument, Turing wrote of machines, algorithms, ink, paper tape, and computation. (Before Turing, a "computer" referred not to a machine, but to a human being who calculated with paper and pencil.)
She is a Regents' Professor of Cognitive Science at the Georgia Institute of Technology with joint appointments in the Ivan Allen College of Liberal Arts School of Public Policy and the College of Computing School of Interactive Computing. Nersessian is one of the pioneers of the interdisciplinary field of cognitive studies of science and technology, which comprises psychologists, philosophers of science, artificial intelligence researchers and cognitive anthropologists. So, I was inspired to study math and physics, but in retrospect this was the beginning of my life as a philosopher and cognitive scientist. I was hooked I changed to a double major in physics and philosophy, and headed to graduate school to study the philosophy of physics.
An internationally recognized pioneer in the field is Judea Pearl, a professor at UCLA, who on March 29 will add to his string of honors and awards the Harvey Prize in Science and Technology from the Technion-Israel Institute of Technology. In 2008, on receiving the Benjamin Franklin Medal in Computer and Cognitive Science from the Franklin Institute, Pearl was credited with research that changed the face of computer science, and his three books recognized as being among the most influential works in shaping the theory and practice of knowledge-based systems.
The label now has many meanings, but when the group protested 200 years ago, technology wasn't really the enemy. ... Despite their modern reputation, the original Luddites were neither opposed to technology nor inept at using it. Many were highly skilled machine operators in the textile industry. Nor was the technology they attacked particularly new. Moreover, the idea of smashing machines as a form of industrial protest did not begin or end with them. In truth, the secret of their enduring reputation depends less on what they did than on the name under which they did it.
The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy By Sharon Bertsch McGrayne Yale University Press 320 pages ISBN: 978-0-300-16969-0
The heart of all the controversy had to do with the way Bayes began his search for an answer to the inverse probability problem. Probability theory was in its infancy in Bayes's time and, McGrayne writes, applied primarily to gambling: the odds of picking up four aces in three consecutive poker hands, for example, which you could describe as reasoning from cause to effect. The inverse problem instead sought to reason from effect to cause: if you had three consecutive poker hands of four aces, what is the underlying chance that the deck is loaded?
Since receiving her doctorate in 1992, Manuela Veloso's research interests in artificial intelligence have focused on duplicating the success with which humans plan, learn and execute tasks. Founding a robot soccer dynasty was purely coincidental. By David Hart. NSF Discovery (March 24, 2004).
See Appendix A (p.69) "Computer Science Department Theses" for Ph.D. dissertations.
See Appendix B (p.77) "Artificial Intelligence Memos" for technical reports from the the Stanford AI Lab.
Stuart C. Shapiro, A net structure for semantic information storage, deduction and retrieval <http://ijcai.org/Past%20Proceedings/IJCAI-1971/PDF/047.pdf>. In Proceedings of the Second International Joint Conference on Artificial Intelligence (IJCAI-71), Morgan Kaufmann, Inc., Los Altos, CA, 1971, 512-523. <http://ijcai.org/Past%20Proceedings/IJCAI-1971/PDF/047.pdf> DONE
D. P. McKay and S. C. Shapiro. Using active connection graphs for reasoning with recursive rules <http://www.cse.buffalo.edu/%7Eshapiro/Papers/mcksha81.pdf>. In /Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81)/, pages 368-374, Los Altos, CA, 1981. Morgan Kaufmann. <http://www.cse.buffalo.edu/~shapiro/Papers/mcksha81.pdf>
Anthony S. Maida and Stuart C. Shapiro, Intensional concepts in propositional semantic networks. <http://www.cse.buffalo.edu/%7Eshapiro/Papers/maisha82.pdf> /Cognitive Science/, 6(4):291-330, 1982. Reprinted in R. J. Brachman and H. J. Levesque, eds. /Readings in Knowledge Representation/, Morgan Kaufmann, Los Altos, CA, 1985, 170-189. <http://www.cse.buffalo.edu/~shapiro/Papers/maisha82.pdf>
Stuart C. Shapiro and William J. Rapaport, SNePS considered as a fully intensional propositional semantic network. <http://www.cse.buffalo.edu/%7Erapaport/676/F01/shapiro.rapaport.87.pdf> In N. Cercone and G. McCalla, editors, /The Knowledge Frontier/, Springer-Verlag, New York, 1987, 263-315. <http://www.cse.buffalo.edu/~rapaport/676/F01/shapiro.rapaport.87.pdf>
João P. Martins and Stuart C. Shapiro. A model for belief revision. <http://www.cse.buffalo.edu/%7Eshapiro/Papers/marsha88.pdf> /Artificial Intelligence/, 35(1):25-79, 1988. <http://www.cse.buffalo.edu/~shapiro/Papers/marsha88.pdf>
Interviews at the AAAI 2006 conference with 28 AAAI Fellows:
Bobrow, Brachman, Brooks, Buchanan, Buchanan-speech, Bundy, Doyle, Feigenbaum, Hendler, Kahn, Kautz, Kuipers, McDermott, Michalski, Minsky, Nilsson, Rich, Rissland, Selman, Sidner, Simmons, Sussman, Swartout, Szolovits, Veloso, Wilkins, Winston, Woolf
Cost curves: An improved Method for visualizing classifier performance
, Chris Drummond and Robert C. Holte (2006), Machine Learning, volume
65(1), pp. 95-130.
Exploiting the Cost (In)sensitivity of Decision Tree Splitting
Criteria. Chris Drummond, and Robert C. Holte (2000). Proceedings of
the 17th International Conference on Machine Learning (ICML'2000), pp.
Machine Learning for the Detection of Oil Spills in Satellite Radar
Images Miroslav Kubat, Robert C. Holte and Stan Matwin (1998), Machine
Learning, volume 30, pp. 195-215.
Theory and Applications of Agnostic PAC-Learning with Small Decision
Trees. Peter Auer, Robert C. Holte, and Wolfgang Maass (1995).
Proceedings of the 12th International Conference on Machine Learning
(ML'95), A. Prieditis and S. Russell (editors), pages 21-29.
Very Simple Classification Rules Perform Well on Most Commonly Used
Datasets. Robert C. Holte (1993). Machine Learning, vol. 11, pp.
Concept Learning and the Problem of Small Disjuncts. Robert C. Holte,
L. Acker, and B. Porter (1989). Proceedings of the Eleventh
International Joint Conference on Artificial Intelligence (IJCAI-89),
(1) Understanding Understanding Mathematics, Cognitive Science 2, 361-383, 1978. (2) Example Generation, Proc. 3rd Biennial Conf of the Canadian Society for Comp. Studies of Intelligence, 1980 (3) The Ubiquitous Dialectic, Proc. 6th European Conf. on AI (ECAI-84), 1984. (4) (Rissland & Ashley) -- A Case-Based System for Trade Secrets Law, Proc. 1st Int'l Conf on AI and Law, 1987.(5) Dimension-Based Analysis of Hypotheticals from Supreme Court Oral Argument, Proc. 2nd Int'l Conf on AI and Law, 1989(6) Artificial Intelligence and Law: Stepping Stones to a Model of Legal Reasoning, Yale Law Journal, June 1990
(7) (Rissland & Skalak) -- CABARET: rule interpretation in a hybrid architecture, IJMMS, 1991
(8) (Skalak & Rissland) -- Arguments and Cases: An Inevitable Intertwining, Artificial Intelligence and Law, 1, 3-44, 1992
(9) (Rissland & Ashley) -- A Note on Dimensions and factors, Artificial Intelligence and Law, 10: 65-77, 2002.(10) AI and Similarity, IEEE Intelligent Systems, Vol 21, No. 3, May/June 2006.
Modeling Spatial Knowledge, Cognitive Science, 1978.
This is the first comprehensive computational model of the cognitive map.
Factoring the Mapping Problem: the Hybrid Spatial Semantic Hierarchy, IJRR, 2010.
the current culmination of the Spatial Semantic Hierarchy thread.
Map Learning with Uninterpreted Sensors and Effectors, AIJ, 1997.
This is a particularly early exploration of foundational learning,
with highly restricted prior knowledge.
An Intellectual History of the Spatial Semantic Hierarchy, 2008.
This is a personal history of the development of these ideas.
Erman, L. D., F. Hayes-Roth, et al. (1980). "The Hearsay-II Speech-understanding system: Integrating knowledge to resolve uncertainty." Computing Surveys 12(2).
Hayes-Roth, B. and F. Hayes-Roth (1979). "A cognitive model of planning." Cognitive Science 3: 275-310.
Hayes-Roth, F. (1983). Using proofs and refutations to learn from experience. Machine Learning. R. S. Michalski, J. G. Carbonell and T. M. Mitchell. Palo Alto, CA, Tioga Publishing: 221-240.
Hayes-Roth, F. (1997). "Artificial Intelligence: What works and what doesn't?" AI Magazine 18(2): 99-113.
Hayes-Roth, F., D. A. Waterman, et al., Eds. (1983). Building Expert Systems. Reading, MA, Addison-Wesley.
Hayes-Roth, F. and N. Jacobstein (1994). "The state of knowledge-based systems." Communications of the ACM 37(3 (March)): 27-39.
Hayes-Roth, F. (1993). "The evolution of commercial AI tools: The first decade." Intl. J. of Artificial Intelligence Tools 2(1): 1-15.
Stefik, M., J. S. Aikins, et al. (1993). "Retrospective on 'The organization of expert systems, a tutorial'." Artificial Intelligence 59: 221-224.
Hayes-Roth, F., J. E. Davidson, et al. (1991). "Frameworks for developing intelligent systems." IEEE Expert 6(3): 30-40.
Lark, J. S., L. D. Erman, et al. (1990). "Concepts, methods, and languages for building timely intelligent systems." Real-Time Systems 2(1/2): 127-148.
Fiksel, J. and F. Hayes-Roth (1989). "Knowledge systems for planning support." IEEE Expert 4(3): 16-24.
Hayes-Roth, F. (1989). "Towards benchmarks for knowledge systems and their implications for data engineering." IEEE Transactions on Knowledge and Data Engineering 1(1): 101-110.
Erman, L. D., J. S. Lark, et al. (1988). "ABE: An environment for engineering intelligent systems." IEEE Transactions on Software Engineering 14(12).
Hayes-Roth, F. (1985). "Knowledge-based systems -- The state of the art in the US." Knowledge Engineering Review 1(June): 18-27.
Hayes-Roth, F. (1985). "Rule-based systems." Communications of the ACM 28(Sept): 921-932.
Hayes-Roth, F. and P. London (1985). "Software tool speeds expert systems." Systems and Software 71(August): 71-75.
Hayes-Roth, F. (1984). "Knowledge-based expert systems." Computer 17(October): 263-273.
Hayes-Roth, F. (1984). "The knowledge-based expert system: A tutorial." Computer 17(9): 11-28.
papers by Russell online at
Historical note: "The idea of robots playing soccer was first mentioned by Professor Alan Mackworth (University of British Columbia, Canada) in a paper entitled 'On Seeing Robots' presented at VI-92, 1992. and later published in a book Computer Vision: System, Theory, and Applications, pages 1-13, World Scientific Press, Singapore, 1993. A series of papers on the Dynamo robot soccer project was published by his group. Independently, a group of Japanese researchers organized a Workshop on Grand Challenges in Artificial Intelligence in October, 1992 in Tokyo, discussing possible grand challenge problems. This workshop led to a serious discussions of using the game of soccer for promoting science and technology."---from A Brief History of RoboCup.
During Wilensky’s tenure at UC Berkeley, he served as chair of the Computer Science Division, director of the Berkeley Cognitive Science Program, director of the Berkeley Artificial Intelligence Research Project, and board member of the International Computer Science Institute.
“When he joined our department, he began building up a program in artificial intelligence at UC Berkeley, and he succeeded wonderfully,” said longtime colleague Richard Fateman, UC Berkeley professor emeritus of computer science and co-investigator on the Digital Library project. “He was extraordinarily successful in conceiving and executing ideas that led to infrastructure improvement for all his colleagues and contributed to the advancement of technology in programs that are widely used in document processing and Web access. He really was exceptional.”
Wilensky was also instrumental in establishing UC Berkeley’s Cognitive Science Program, helping organizing the diverse campus faculty and leading competitive grants at a time when the research field was in its infancy.
An 18th Century automaton that could beat human chess opponents seemingly marked the arrival of artificial intelligence. But what turned out to be an elaborate hoax had its own sense of genius, says Adam Gopnik.
...So the inventor's real genius was not to build a chess-playing machine. It was to be the first to notice that, in the modern world, there is more mastery available than you might think; that exceptional talent is usually available, and will often work cheap.
Brief summary of Joshua Lederberg's contributions to science. Shown at the presentation of the Morris F. Collin Award to Lederberg by the American College of Medical Informatics, 1999. Includes short interviews with Edward Feigenbaum, Don Lindberg, Tom Rindfleisch, Carl Djerassi, and Ted Shortliffe.
8 min. interview with Tom Mitchell about machine learning, from CMU's 2006 ML Autumn School, recorded in September 2006.
"Tom Mitchell is the first Chair of Department of the first Machine Learning Department in the World, based at Carnegie Mellon. The Videolectures.Net team spoke to him in Pittsburgh at CMU where we discussed about how he started the department, what was the response of the broader community and its past, present and future. "The university said you can only have a department if you have a discipline that is going to be here in one hundred years otherwise you can not have a department.""
Report from the Moore School of Electrical Engineering, University of Pennsylvania
A program is described that accepts natural
language input and makes inferences from it and paraphrases
of it . The Conceptual Dependency framework is the basis of
thi s system.