On Tue, Jun 18, 2024 at 3:24 PM Jason Resch <jasonre...@gmail.com> wrote:
> > > On Sun, Jun 16, 2024, 10:26 PM PGC <multiplecit...@gmail.com> wrote: > >> A lot of the excitement around LLMs is due to confusing skill/competence >> (memory based) with the unsolved problem of intelligence, its most >> optimal/perfect test etc. There is a difference between completing strings >> of words/prompts relying on memorization, interpolation, pattern >> recognition based on training data and actually synthesizing novel >> generalization through reasoning or synthesizing the appropriate program on >> the fly. As there isn't a perfect test for intelligence, much less >> consensus on its definition, you can always brute force some LLM through >> huge compute and large, highly domain specific training data, to "solve" a >> set of problems; even highly complex ones. But as soon as there's novelty >> you'll have to keep doing that. Personally, that doesn't feel like >> intelligence yet. I'd want to see these abilities combined with the program >> synthesis ability; without the need for ever vaster, more specific >> databases etc. to be more convinced that we're genuinely on the threshold. > > > I think there is no more to intelligence than patter recognition and > extrapolation (essentially, the same techniques required for improving > compression). It is also the same thing science is concerned with: > compressing observations of the real world into a small set of laws > (patterns) which enable predictions. And prediction is the essence of > intelligent action, as all goal-centered action requires predicting > probable outcomes that may result from any of a set of possible behaviors > that may be taken, and then choosing the behavior with the highest expected > reward. > > I think this can explain why even a problem as seemingly basic as "word > prediction" can (when mastered to a sufficient degree) break through into > general intelligence. This is because any situation can be described in > language, and being asked to predict next words requires understanding the > underlying reality to a sufficient degree to accurately model the things > those words describe. I confirmed this by describing an elaborate physical > setup and asked GPT-4 to predict and explain what it thought would happen > over the next hour. It did so perfectly, and also explained the > consequences of various alterations I later proposed. > > Since any of thousands, or perhaps millions, of patterns exist in the > training corpus, language models can come to learn, recognize, and > extrapolate all of those thousands or millions of patterns. This is what we > think of as generality (a sufficiently large repertoire of pattern > recognition that it appears general). > > Jason > Hey Jason, You've articulated this idea before, that the result of the training on such large amounts of data may result in the ability of LLMs to create models of reality and simulate minds and so forth, and it's an intriguing possibility. However, one fact of how current LLMs operate is that they don't know when they're wrong. If what you're saying is true, shouldn't an LLM be able to model its own state of knowledge? Terren -- You received this message because you are subscribed to the Google Groups "Everything List" group. To unsubscribe from this group and stop receiving emails from it, send an email to everything-list+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/everything-list/CAMy3ZA-8wu2S2CGyB8SphcgJMjUtUZpY16zPAxemDgez6xikhw%40mail.gmail.com.
