IMHO Cyc was doomed by the lack of a natural language interface. It cannot map between "Eats(cats, mice)" and "cats eat mice", or recognize their equivalence. In Cyc, "cats" and "Eats" are just labels used to help human programmers enter facts. Without a natural language interface, it is very expensive to verify and update the knowledge base. More importantly, there is no human interface.
It's not that Cycorp isn't aware of the problem. Last year some people at Cycorp were interested in entering the Hutter text compression contest, but they wanted us to change the rules to not count the size of the database (we declined). Text compression or prediction is AI-complete, but it would require a natural language model to predict the next word in "cats eat". The example rule I gave seems trivial to solve, but anyone who has worked with NLP knows it is not, of course. I believe the fundamental design error was to insert knowledge at the wrong end. Children learn lexical rules first (segmenting continuous speech at 7-10 months), then semantics (starting at 12 months), then grammar (2-3 years), then logical rules. Structured rules take a lot less computing power to implement than language statistics, so they had to skip the earlier steps, especially in the 1980's when Cyc was launched. As a result, Cyc has no theory that explains how people learn and apply language and facts or how they communicate. -- Matt Mahoney, [EMAIL PROTECTED] ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=95818715-a78a9b Powered by Listbox: http://www.listbox.com
