In the absence of a learning machine that can acquire common sense facts on its own, there would seem to be only one option left. That is, manually programming in the millions of general knowledge items that we take entirely for granted. The CYC research project has actually undertaken this mammoth task.The Most Ambitious AI Project Ever?
Decide for yourself: CYC is a $25 million, 20 year project in Artificial Intelligence. Aimed at beating the common sense knowledge problem outlined in the last chapter, the project employs workers whose full time occupation consists of entering volumes of common sense data into the ever-growing AI. Its database, at the time of writing, contains over 1,000,000 of these hand-entered facts, and is maintained in an entire room full of computers. The CYC programmers designed a representation scheme that claims to be standardized enough to be useful, while being flexible enough to represent almost any fact. In short, the project headed by Douglas Lenat and already 15 years running is one of the most impressive AI undertakings ever.Why?
The companies that eventually funded CYC must have been presented with the same staggering statistics that we have just presented to you: millions of dollars, decades of work, massive computing requirements. Yet companies chose to invest, not governments or academic institutions, private for-profit corporations. What benefits could outweigh those costs?
large the task is, if successfully completed, think of the contribution to computing.
Cycorp (the corporation in charge of CYC's construction) will be able to license
the world's only common sense knowledge base out to other companies. There might
be a common sense search engine on the web that actually finds you the links
you want, instead of semi-randomly looking for keywords. Whatever the cost,
CYC has the potential to revolutionize "intelligent" computing. No one, though,
can better explain CYC's potential and successes than Cycorp themselves. We
have included three readings, all written by Douglas Lenat and other members
of the Cycorp team and all (naturally) beaming about the project's prospects.
The fourth link leads to a recent interview with Doug Lenat.
Of course, not everyone is as full of enthusiasm as Doug Lenat. Several critics (among them Hubert Dreyfus, the critic mentioned in earlier chapters) point out that while it makes for an interesting exercise in programming, it falls short of the mark as strong AI. Strong AI claims to duplicate not only human-like intelligent products, but also human-like intelligent process. As explained in the previous section, human knowledge is often implicit, procedural, and domain specific, rather than explicit, declarative, and general purpose. However, the focus of the CYC project is not Strong AI, or modeling human intelligence. Rather, it is an example of an 'engineering end-run' attempt to produce general purpose, intelligent machines.
Lenat may, however, have overstepped his bounds at one point, and critics have taken him to task on it. In his enthusiasm for the project, Lenat has been found to hold some lofty ambitions: Cyc teaching schoolchildren, advising public policy, and even dispensing justice. These predictions if they came to pass, would certainly reflect great confidence in AI research, but critics warn that the results could be catastrophic, as the paper below argues.