Re:ChatGPT vs. a Cryptic Crossword
by rich_sasha on Tuesday December 06, 2022 @06:41AM
(#33878276)
I guess I'd say that a sufficiently (!) accurate language model is indistinguishable from a AGI limited to text. The question is, is that level of accuracy achievable. I'm not claiming, btw, we are close to that.
By contrast it feels like driving requires an understanding of effects that are really hard to distill from pure driving data. Not just analytically hard, but requiring an understanding of external context.
But it's certainly a fine point.
Re:ChatGPT vs. a Cryptic Crossword
by joe_the_user on Tuesday December 06, 2022 @05:00PM
(#33887725)
I think the problem of self-driving cars is that the driving problem isn't entirely sealed and success requires a long string of successes (most of which seem trivial). I think the situation with language is that it too isn't entirely sealed from other parts of reality but here each immediate success is judged a victory. A lot of self-driving process are straightforward, "just" adaptive control. Minimally self-driving vehicles have existed for a long time. Corner cases are the problem - distinguish a newspaper floating into an area from a load of bricks falling off a truck in front of you, etc.
For language, a problem to consider for a language using system is "talking a person through" a task. The thing about that is it involves two entities sharing a common model of reality and each updating their model as they listen to the other. And here I think corner cases of reality are basically as likely to show up.
One can be succeed at one level of using language without succeed at another level. But we humans expect another human to succeed or to fail fairly uniformly - or we call them "a bullshit artist". These expectations may not be met with large language models.
In that sense language models have a somewhat complete training dataset. Yes this requires an understanding of the material, rather than just parroting, but the signal is there if you can separate it from noise.
--> I'd agree that there's conceivably more that can be done with a language dataset. But the training process of transformers-based models isn't really oriented to engaging in the process you describe. It treated language as data and it's fundamentally a very sophisticated database that only appears to engage in such logic. As it will tell you.