“AIs that can parse and use CYC should be feasible well before AIs that can parse and use random human writings.”

I can’t take that for granted.

Marcello: “I must say, I’m rather perplexed about what it even means for some information not associated with any particular cognitive architecture to be knowledge.”

If we hope to be create AIs that can read human writings, we have all the more reason to hope to create AIs that can make use of CYC, since CYC is more structured and easier to parse. AIs that can parse and use CYC should be feasible well before AIs that can parse and use random human writings. If that means we expect such AIs to share some basic architectural features with humans and CYC, so be it.

@Ben Jones: I presently don’t have a blog. I am trying to optimize for becoming a useful AI researcher, and my current strategy involves taking lots of math classes. With that said, better ability to to communicate ideas like these looks useful, so I’ll consider starting one.

@Robin Hanson: Having read the arguments in my second comment, have your opinions on whether architecture is overrated shifted?

http://video.google.co.uk/videosearch?hl=en&q=doug+lenat&um=1&ie=UTF-8&sa=X&oi=video_result_group&resnum=4&ct=title#

if knowing lots is anywhere near as important as Lenat thinks, I’d expect serious AI attempts to import CYC’s knowledge, translating it into a new representation. No other source has anywhere near CYC’s size, scope, and integration.

Knowing lots is important – but there are other sources of knowledge besides Cyc. For example, Google have slurped up the entire internet, and scanned a substantial proportion of the books that have been published – but they haven’t shown much interest in Cyc. Why would they? AFAICS, Cyc is a useless, unmaintainable, GOFAI mess.