> For example: you can't make a mice-sized brain as smart as a human brain no matter how hard you try.
Sure. We don't know where the ceiling is for our digital minds, though.
If you train a small model in another domain it will begin losing capabilities in the former domain. This is effectively the sigmoid problem.
Although I will admit that if we discover a higher information density algorithm that it might change, but not by a substantial amount to where "super intelligence" in 1gb would be possible.
There is undoubtedly a limit somewhere (there is only so much you can pack into a given size) but it's really not particularly clear where that limit is. I don't think it's superintelligence - that much I agree with you - but I think "We already have a 1gb model that is as capable as it will ever be" is strictly false.
It's like comparing two person A and B of similar intelligence where A is smarter and B is a genius at signing, but signing was not on the test so person A won.
The rest is just the general reality I am sure you are familiar with:
- https://en.wikipedia.org/wiki/Catastrophic_interference
- https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)
- https://en.wikipedia.org/wiki/Entropy_(information_theory)
- https://en.wikipedia.org/wiki/Catastrophic_interference
- https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)
- https://en.wikipedia.org/wiki/Entropy_(information_theory)
As for intelligence, the only way we have that is by allowing the model to fill the blanks which have to come from the training data. The models cannot have true intelligence for as long as they are linear models, what we see with reasoning is "boxed" intelligence where the models are effectively "modifying" themselves by feeding it's own reasoning data back into input deriving most plasible output given known information. However, the model is not able to retain what it has learned therefore that intelligence is gone the moment the session is 'full'. You can go pretty far by continiously distilling discovered information, but again all that has to come from the original training data and models own outputs, which it has to take for granted as the 'intelligence' gained is lost creating what we see is the maximum possible benchmark performance and why smaller models are not able to score as high while theoretically having the same capabilities. We can see this with larger models where they can solve tasks much faster than smaller ones as it does not require to generate the solution due to the fact that the solution is already in the training data as 'baked' intelligence and it doesn't have to 'create' it during reasoning.