> Can you give a few specific examples?
Early AI, the "Symbolic AI/Expert systems" era: https://en.wikipedia.org/wiki/Dendral
Evolutionary computation era: https://en.wikipedia.org/wiki/Evolved_antenna
Deep learning: https://en.wikipedia.org/wiki/AlphaFold
Generative AI: https://openai.com/index/model-disproves-discrete-geometry-c... but also I've lost track of how many Erdős problems have been solved in the last year.
> Is a know-it-all ASI actually possible? If so what's the bottleneck? The current approach, the data or the compute?
It depends what you mean by "know-it-all". All human knowledge? Clearly possible. (There's a theoretical case for "everything" which provably isn't, but I regard this as a purely mathematical objection and not something that applies to the real world).
There are several bottlenecks; one big one is that we don't know what we're doing, another is that even the biggest models don't have as many parameters as even lower-bound estimates for a single healthy human brain. (The best guess I know of, and it is still a guess and I'm not a biologist, would place state of the art models at around the same complexity (if one parameter is one synapse) as the brain of a rat).