Re:30% Drop In o1-Preview Accuracy When Putnam Problems Are ...
by Panzer04 on Wednesday January 01, 2025 @08:02AM
(#42565760)
Seems a bit picky. If the bot has seen the exact problem before it's not really doing anything more than recall to solve it.
[ Parent
| on HN ]
Re:30% Drop In o1-Preview Accuracy When Putnam Problems Are ...
by bandrami on Wednesday January 01, 2025 @08:21AM
(#42565853)
20 years ago in grad school we were doing a very early iteration of this where we built Markov chains with Shakespeare's plays and wanted to produce a plausibly "Shakespearian" clause given a single word to start and a bearish professor said "the more plausible it gets the more I worry people might forget plausibility is all that it promises".
(There was also a much earlier piece of software that would generate semi-intelligible Kant or Hegel one sentence at a time, though that was through a series of a priori generation rules and a large at the time dictionary of stock phrases. I wonder what ever happened to that.)
Re:30% Drop In o1-Preview Accuracy When Putnam Problems Are ...
by sickblastoise on Wednesday January 01, 2025 @09:29AM
(#42566201)
I think your prof’s worries came true on a massive scale
Re:30% Drop In o1-Preview Accuracy When Putnam Problems Are ...
by jeffreygoesto on Wednesday January 01, 2025 @08:27AM
(#42565885)
It became a successful consultant...
Re:30% Drop In o1-Preview Accuracy When Putnam Problems Are ...
by marcosdumay on Wednesday January 01, 2025 @09:58AM
(#42566357)
That said, a bot with contextual recall can be very useful.
The problem is just that people keep insisting that those things are intelligent.