I’m confused as to why there necessarily wouldn’t be a difference between the two.
I can think of a classes problems without better than brute force or simulation solutions, e.g. Bitcoin mining and there are plenty of these solutions that explode enough in complexity that they are infeasible given the resource of the universe for certain input sizes.
Alphafold2 also did not fully solve the protein folding problem. Rather, it still has a significant error rate as reported by google themselves with regions of low confidence. It also seems to go up significantly in certain classes of inputs such as longer loop structures.
Further, it was possibly not solved to the extent of what people in 2008 were arguing against, given we are nowhere near predicting how protein folds in interactions. I’m unsure whether the predicted value of solving protein folding back then is equal to the predicted value of extent to which AlphaFold solves it.
My next conclusion is mostly an inference, but broadly it seems like DL models hit a sigmoid curve when it comes to eeking out the final percentages of accuracy, and this can also be seen in self driving cars and LLMs. This makes sense given the world largely follows predictable laws with a small but frequent number of exceptions. In a one shot experiment, this uncertainty would accumulate exponentially making it seem unlikely that it would succeed.
I think clearly it can reduce the amount of experiments needed, but one shot seems like too high of a bar to hold through only generalizations that take less than 1 OOM of compute than the universe uses to “simulate” results as you experiment due to compounding errors.
At the superintelligent level there’s not a binary difference between those two clusters. You just compute each thing you need to know efficiently.
I’m confused as to why there necessarily wouldn’t be a difference between the two.
I can think of a classes problems without better than brute force or simulation solutions, e.g. Bitcoin mining and there are plenty of these solutions that explode enough in complexity that they are infeasible given the resource of the universe for certain input sizes.
Alphafold2 also did not fully solve the protein folding problem. Rather, it still has a significant error rate as reported by google themselves with regions of low confidence. It also seems to go up significantly in certain classes of inputs such as longer loop structures.
Further, it was possibly not solved to the extent of what people in 2008 were arguing against, given we are nowhere near predicting how protein folds in interactions. I’m unsure whether the predicted value of solving protein folding back then is equal to the predicted value of extent to which AlphaFold solves it.
My next conclusion is mostly an inference, but broadly it seems like DL models hit a sigmoid curve when it comes to eeking out the final percentages of accuracy, and this can also be seen in self driving cars and LLMs. This makes sense given the world largely follows predictable laws with a small but frequent number of exceptions. In a one shot experiment, this uncertainty would accumulate exponentially making it seem unlikely that it would succeed.
I think clearly it can reduce the amount of experiments needed, but one shot seems like too high of a bar to hold through only generalizations that take less than 1 OOM of compute than the universe uses to “simulate” results as you experiment due to compounding errors.