They have not increased in capabilities, they have increased in specialization.

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.

▲

Philpax an hour ago | parent [-]

Over the last two years, this weight class has doubled its scores and/or saturated several benchmarks in the Qwen lineup alone without loss of generality: https://claude.ai/public/artifacts/9f249169-3623-417e-86cd-7...

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.

▲

himata4113 37 minutes ago | parent [-]

The measured entropy of the model remains nearly unchanged though which means we have lost capabilities we have not measured, the model hasn't become "denser" it just became more specialized.

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.

	▲	Philpax 19 minutes ago \| parent [-]
		Assumes facts not in evidence. Please show your working.