Haugeland’s AI Views 25 Years Later

A couple of years ago, I picked John Haugeland’s Artificial Intelligence: The Very Idea up off the free book table in the computer science department of Indiana University. Finally read it this weekend.  Published in 1985, there’s  a lot to like about the book, but its definitely a product of its time.  That period being when computer and cognitive scientists were obsessing about knowledge representation.  Wanted to call-out a few (perhaps arrogant) quotes reflective of its day…

“A different pipedream of the 1950s was machine translation of natural languages.  The idea first gained currency in 1949 (via a ‘memorandum’ circulated by mathematician Warren Weaver) and was vigorously pursed … Weaver actually proposed a statisticalsolution based on the N nearest words (or nouns) in the immediate context. …  Might a more sophisticated ’statistical semantics’ (Weaver’s own phrase) carry the day? Not a chance.”

Pipedream…somebody tell Google 🙂  Actually, I had no idea machine translation was worked on in the 1950s.  Cool!  I would mention that the other pipedream of the ’50s he discusses is cybernetics, which, in various forms, is also a very popular area of research today.

“Artificial Intelligence must start by trying to understand knowledge…and then, on that basis, tackle learning.  It may even happen that, once the fundamental structures are worked out, acquisition and adaptation will be comparatively easy to include…it does not appear that learning is the most basic problem, let alone a shortcut or a natural starting point.”

Seems like research that has treated knowledge representation and learning as one problem (neural nets, Bayesian nets, etc) has been particularly fruitful.

“AI has discovered that knowledge itself is extraordinarily complex and difficult to implement–so much so that even the general structure of a system with common sense is not yet clear.”

And, clearly, the Cyc project solved this problem 

Anyway, the book is still a very interesting read, particularly if you like thinking about the challenges inherent in the domain knowledge representation.