Douglas Lenat

Douglas Bruce Lenat (September 13, 1950 – August 31, 2023) was an American

For his senior thesis, advised in part by Dennis Gabor, was to bounce acoustic waves in the 40 mHz range off real-world objects, record their interference patterns on a 2-meter square plot, photo-reduce those to a 10-mm square film image, shine a laser through the film, and thus project the three-dimensional imaged object—i.e., the first known acoustic hologram.^{[citation needed]} To settle an argument with Dr. Gabor, Lenat computer-generated a five-dimensional hologram, by photo-reducing computer printout of the interference pattern of a globe rotating and expanding over time, reducing the large two-dimensional paper printout to a moderately large 5-cm square film surface through which a conventional laser beam was then able to project a three-dimensional image, which changed in two independent ways (rotating and changing in size) as the film was moved up-down or left-right.^{[citation needed]}

Lenat was a Ph.D. student in Computer Science at Stanford University, where his published research included automatic program synthesis from input/output pairs and from natural language clarification dialogues.^[15]

Research

Lenat received his Ph.D. in Computer Science from Stanford University (published as Knowledge-based systems in artificial intelligence,^[16] along with the Ph.D. thesis of Randall Davis, McGraw-Hill, 1982) in 1976.^{[citation needed]} His thesis advisor was Professor Cordell Green, and his thesis/oral committee included Professors Edward Feigenbaum, Joshua Lederberg, Paul Cohen, Allen Newell, Herbert Simon, Bruce Buchanan, John McCarthy, and Donald Knuth.^{[citation needed]}

His thesis, AM (Automated Mathematician) was one of the first computer programs that attempted to make discoveries, i.e., to be a theorem proposer rather than a theorem prover. Experimenting with the program fueled a cycle of criticism and improvement, leading to a slightly deeper understanding of human creativity. Many issues had to be dealt with in constructing such a program: how to represent knowledge formally, expressively, and concretely, how to program hundreds of heuristic "interestingness" rules to judge the worth of new discoveries, heuristics for when to reason symbolically and inductively (and slowly) versus when to reason statistically from frequency data (and hence, quickly), what the architecture—the design constraints—of such reasoning programs might be, why heuristics work (in sum, because the future is a continuous function of the past), and what their "inner structure'' might be. AM was one of the first halting steps toward a science of learning by discovery, toward de-mystifying the creative process and demonstrating that computer programs can make novel and creative discoveries.^[17]

In 1976 Lenat started teaching as an assistant professor of Computer Science at

Lenat returned to Stanford as an assistant professor of Computer Science in 1978 and continued his research building the Eurisko automated discovery and heuristic-discovery program. Eurisko made many interesting discoveries and enjoyed significant acclaim, with Lenat's paper "Heuretics: Theoretical and Experimental Study of Heuristic Rules"[18] winning the Best Paper award^{[citation needed]} at the 1982 AAAI conference.

A call for "common sense"

Lenat (working with John Seely Brown at Xerox PARC) published in 1984 an analysis of what were the limitations of his AM and Eurisko lines of research.^[19] It concluded that progress toward real, general, symbolic AI would require a vast knowledge base of "common sense", suitably formalized and represented, and an inference engine capable of finding tens- or hundreds-deep conclusions and arguments that followed from the application of that knowledge base to specific questions and applications.^[20]

The successes, and analysis of the limitations, of this AM and Eurisko approach to AI, and the concluding plea for the massive (multi-thousand-person-year, decades-long) R&D effort would be required to break that bottleneck to AI, led to attention in 1982 from Admiral Bob Inman and the then-forming MCC research consortium in Austin, Texas, culminating in Lenat's becoming Principal Scientist of MCC from 1984–1994, though he continued even after this period to return to Stanford to teach approximately one course per year. At the 400-person MCC, Lenat was able to have several dozen researchers work on that common sense knowledge base, rather than just a few graduate students.^{[citation needed]}

Cycorp

The fruits of the first decade of R&D on

• "Intelligence is ten million rules."^[28] This refers to the prior and tacit knowledge that authors presume their readers all possess (such as "if person x knows person y, then x's date of death can't be earlier than y's date of birth") not counting the vastly larger number of "facts" such as one might find in Wikipedia or by Googling.
• "The time may come when a greatly expanded Cyc will underlie countless software applications. But reaching that goal could easily take another two decades."^[29]
• "Once you have a truly massive amount of information integrated as knowledge, then the human-software system will be superhuman, in the same sense that mankind with writing is superhuman compared to mankind before writing."^[30]
• "Sometimes the veneer of intelligence is not enough."^[31]
• “If computers were human, they’d present themselves as autistic, schizophrenic, or otherwise brittle. It would be unwise or dangerous for that person to take care of children and cook meals, but it’s on the horizon for home robots. That’s like saying, ‘We have an important job to do, but we’re going to hire dogs and cats to do it.'”^[32]
• "What we needed, he says, is nothing less than an “AI Manhattan Project”, a full frontal assault on common sense: the challenge is to create an Encyclopédia of Common sense", Michio Kaku citing Lenat.^[33]

Writings

• "Why AM and Eurisko Appear to Work," (Lenat and John Seely Brown), Proceedings of National Conference on AI (AAAI–83), Washington, DC, August 1983.
• Davis, Randall; Lenat, Douglas B. (1982). Knowledge-Based Systems in Artificial Intelligence. New York: McGraw-Hill International Book Co.
  ISBN 978-0-07-015557-2
  .
• Hayes-Roth, Frederick; Waterman, Donald Arthur; Lenat, Douglas B., eds. (1983). Building Expert Systems. Reading, Mass: Addison-Wesley Pub. Co.
  ISBN 978-0-201-10686-2
  .
• Lenat, Douglas B. "Computer Software for Intelligent Systems: An Underview of AI," in Scientific American, September 1984.
• Lenat, Douglas B.; Clarkson, Albert; Kircmidjian, Garo (1983). "An Expert System for Indications & Warning Analysis". Proceedings of the Eighth International Joint Conference on Artificial Intelligence - Volume 1. IJCAI'83. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.: 259–262.[34]
• Lenat, Douglas B.; Feigenbaum, Edward A. (February 1991). "On the Thresholds of Knowledge". Artif. Intell. 47 (1-3): 185–250.
  ISSN 0004-3702.^[35]
• Lenat, Douglas B.; Guha, R. V. (1990-01-01). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Reading, Mass.: Addison-Wesley.
  ISBN 9780201517521.^[36]
• Lenat, Douglas B. From 2001 to 2001: Common Sense and the Mind of HAL[37]
• Lenat, Douglas B. (2008-07-10). "The Voice of the Turtle: Whatever Happened to AI?". AI Magazine. 29(2).
  ISSN 0738-4602^[38]
• Blackstone E.H., Lenat, D.B. and Ishwaran H. Infrastructure required to learn which care is best: methods that need to be developed, in (Olsen L., Grossman, C., and McGinnis, M., eds.) Learning What Works: Infrastructure Required for Comparative Effectiveness Research. Institute of Medicine Learning Health System Series, The National Academies Press, pp. 123–144, 2011.
• Lenat DB, Durlach P. “Reinforcing Math Knowledge by Immersing Students in a Simulated Learning-By-Teaching Experience.” J. International Journal of Artificial Intelligence in Education., 2014
• Lenat, Douglas B. (2016-04-13). "WWTS (What Would Turing Say?)". AI Magazine. 37 (1): 97–101.
  ISSN 0738-4602^[39]
• See also many of the References, below.