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Re:The bigger-is-better approach to AI is running out of road by regularfry on Sunday June 25, 2023 @06:51PM (#36473472)
> That’s a statistical association not a concept. Try asking it questions that mix concepts like “Can you eat Apple share price?” which aren’t in its corpus.

ChatGPT: > No, you cannot physically eat an Apple share or any other stock share. A share of a company's stock represents ownership in that company and is typically bought and sold on stock exchanges. Share prices fluctuate based on various factors such as supply and demand, company performance, market conditions, and investor sentiment. While you can buy and sell shares of Apple on the stock market, you cannot consume or physically eat them.

That seems perfectly reasonable to me. The answer correctly identifies the problem with the question.

I would recommend looking up the Othello paper. Chess may be beyond the current level of LLM capability, but that doesn't mean they aren't manipulating things at a level higher than tokens.

Re:The bigger-is-better approach to AI is running out of road by Retric on Sunday June 25, 2023 @08:24PM (#36474150)
Obviously it gets such a simple case correct. I was illustrating the approach using your wording for clarity, Chess was the actual example.

The Othello paper is hardly a counter example. Researchers created an Othello specific model that almost learned the grammar of Othello not how to play well. Yes, there was largely correct internal game state built up from past moves. No it didn’t actually learn the rules so it would make strictly legal moves nor did it learn to make good moves.

I don’t bring up this inaccuracy because it actually makes much of a difference to playing Othello, but rather to illustrate how these systems are designed to get really good at faking things. There’s approaches that allow AI to actually learn to play arbitrary games, but they differ by having iterative feedback rather than simply providing a huge corpus. It’s like science vs philosophy, feedback prunes incorrect assumptions.

Re:The bigger-is-better approach to AI is running out of road by regularfry on Monday June 26, 2023 @10:07AM (#36479817)
> The Othello paper is hardly a counter example. Researchers created an Othello specific model that almost learned the grammar of Othello not how to play well. Yes, there was largely correct internal game state built up from past moves. No it didn’t actually learn the rules so it would make strictly legal moves nor did it learn to make good moves

It is, though. Nobody said anything about playing well, or learning the rules. The very fact that it had a valid internal representation of the game state means it's extrapolated beyond token-level. Which is the point.

Re:The bigger-is-better approach to AI is running out of road by Retric on Monday June 26, 2023 @04:51PM (#36485663)
> The very fact that it had a valid internal representation of the game state means it's extrapolated beyond token-level. Which is the point.

Hardly, decoding “Bob picked up an Apple.” requires you to make an association between the token Bob and the token Apple. But that doesn’t imply a LLM is operating above the token level.

And again it failed to make strictly correct moves because its internal representation of the game state was flawed. Objectively an LLM when specifically trained on Othello a game with very simple and completely mechanical rules failed to abstract what those rules actually where.

Re:The bigger-is-better approach to AI is running out of road by regularfry on Wednesday June 28, 2023 @02:12PM (#36510337)
Except that incorrect moves don't imply an incorrect representation.

The internal representation was a literal 8x8 grid of piece locations they could externally change and have it generate moves consistent with the changed position. It's about the clearest example of a learned higher-level internal representation I can think I've seen.

The fact that it didn't also perfectly learn the rules while it was doing that is entirely uninteresting.