What will be the limiting factors on LLM improvements

If you were a scale believer over the last few years, the progress we’ve been seeing would have just made more sense. There is a story you can tell about how GPT-4’s amazing performance can be explained by some idiom library or lookup table which will never generalize. But that’s a story that none of the skeptics pre-registered.

As for the believers, you have people like Ilya, Dario, Gwern, etc more or less spelling out the slow takeoff we’ve been seeing due to scaling as early as 12 years ago.

It seems pretty clear that some amount of scaling can get us to transformative AI - i.e. if you achieve the irreducible loss on these scaling curves, you’ve made an AI that’s smart enough to automate most cognitive labor (including the labor required to make smarter AIs).

But most things in life are harder than in theory, and many theoretically possible things have just been intractably difficult for some reason or another (fusion power, flying cars, nanotech, etc). If self-play/synthetic data doesn’t work, the models look fucked - you’re never gonna get anywhere near that platonic irreducible loss. Also, the theoretical reason to expect scaling to keep working are murky, and the benchmarks on which scaling seems to lead to better performance have debatable generality.

So my tentative probabilities are: 70%: scaling + algorithmic progress + hardware advances will get us to AGI by 2040. 30%: the skeptic is right - LLMs and anything even roughly in that vein is fucked.

From Dwarkesh Patel. This is the piece that I've been waiting for someone to right. It doesn't matter if he is right, just the thought exercise of thinking through where the bottlenecks might be is really useful.

2024-01-04