Lenat, Douglas B.


WWTS (What Would Turing Say?)

AI Magazine

Turing’s Imitation Game was a brilliant early proposed test of machine intelligence — one that is still compelling, today, despite the fact that in the hindsight of all that we’ve learned in the intervening 65 years we can see the flaws in his original test. And our field needs a good “Is it AI yet?” test more than ever, today, with so many of us spending our research time looking under the “shallow processing of big data” lamppost. If Turing were alive today, what sort of test might he propose?


The Voice of the Turtle: Whatever Happened to AI?

AI Magazine

On March 27, 2006, I gave a light-hearted and occasionally bittersweet presentation on “Whatever Happened to AI?” at the Stanford Spring Symposium presentation – to a lively audience of active AI researchers and formerly-active ones (whose current inaction could be variously ascribed to their having aged, reformed, given up, redefined the problem, etc.)   This article is a brief chronicling of that talk, and I entreat the reader to take it in that spirit: a textual snapshot of a discussion with friends and colleagues, rather than a scholarly article. I begin by whining about the Turing Test, but only for a thankfully brief bit, and then get down to my top-10 list of factors that have retarded progress in our field, that have delayed the emergence of a true strong AI.


CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks

AI Magazine

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.



Heuristic Search for New Microcircuit Structures: An Application of Artificial Intelligence

AI Magazine

Three experiments have been conducted, and some novel designs and design rules have emerged. The paradigm for Eurisko's exploration is a loop in which it generates a new device configuration, computes its I/O behavior, tries to "parse" this into a functionally it already knows about and can use, and then evaluates the results. In the first experiment, this loop took place at the level of charged carriers moving under the effects of electric fields through abutted regions of doped and undoped semiconductors. This was unsurprising, as they were short sentences in the descriptive language we had defined (a language with verbs like Abut and ApplyEField, and with nouns like nDoped Region and IntrinsicChannellRegion).


The ubiquity of discovery

Classics

See also: IJCAI-77 paperArtificial Intelligence, 9[#3]:257-285


AM: an Artificial Intelligence approach to Discovery in Mathematics as Heuristic Search

Classics

Ph.D. Dissertation. Stanford AI Laboratory. Reprinted as AM: Discovery in Mathematics as Heuristic Search, in Randall Davis and Douglas B Lenat (eds.), Knowledge-Based Systems in Artificial Intelligence, pp. 1-225, New York: McGraw-Hill (1982).Stanford AI Laboratory