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> learn everything the knowledge engine knows Isn't this an effectively infinite set? Wolfram Alpha could be said to know "all the numbers", and "all the formu
by tablatom 4y ago
> learn everything the knowledge engine knows
Isn't this an effectively infinite set? Wolfram Alpha could be said to know "all the numbers", and "all the formulas".
> LLMs can learn structured domains too if they are well-represented in the training set
But can they learn how to apply structured knowledge in precise ways? In mathematical or computational ways? I don't follow the field in great detail but the commentary I read seems to be saying this is not at all the case. And my own experiments with ChatGPT show it has no systematic grasp of logic.
- theptip 4y ago> Isn't this an effectively infinite set? No, the thing you'd want the LLM to be learning would be the rules. > But can they learn how to apply structured knowledge in precise ways? I personally believe: clearly yes, already. You can already get a LLM to generate code for simple logical problems. You can ask ChatGPT to modify a solution in a particular way, showing it has some understanding of the underlying logic, rather than just regurgitating solutions it saw. In other domains, you can already give IQ tests to GPT-N: https://lifearchitect.ai/ravens/ https://lifearchitect.ai/ravens/. Others have written in more detail than I could do justice to: https://www.gwern.net/Scaling-hypothesis#scaling-hypothesis https://www.gwern.net/Scaling-hypothesis#scaling-hypothesis. I'd just note that a lot of commentators make quite simple errors of either goalpost-moving or a failure to extrapolate capabilities a year or two ahead. Of course, no linear or exponential growth curve continues indefinitely. But betting against this curve, now, seems to me a good way of losing money.