Don't confuse interpolation with extrapolation. Curing cancer will require new ideas. IMO requires skill proficiency in tasks where the methods of solving are known.
The methods are know, but the solutions to the IMO problems weren't. So the AI did extrapolate a solution.
Also, there's no reason to affirm that an eventual cure for cancer requires fundamentally new methods. Maybe the current methods are sufficient, it's just that nobody has been "smart" enough to put the pieces together. (disclaimer: not an expert at all)
I think you are correct though. We don't need new physics to cure cancer. But we may need information-handling, reasoning and simulation systems which are orders of magnitude bigger and more complex than anything we have this year. We also need to stop pussy-footing and diddling with ideologies and start working on the root cause of cancer and almost every other disease, which is aging.
I think this is kinda false actually on the cancer side. We have reached a point where we have known approaches that work. It's "just" a matter of putting them into practice which will of course require solving many little details, which is very important and time-consuming work, but it doesn't require super-human genius level of lateral thinking, just a few millions man years of grinding away at it.
new doesn't necessarily mean "an extremal point that's not the average of two existing points". The set of existing knowledge is not necessarily continuous; the midpoint between two known points may be unknown, and thus would be a "new" point that could be obtained by interpolation.
Search is extrapolation. Learning is interpolation. Search+Learn is the formula used by AZ. Don't forget AZ taught us humans a thing or two about a game we had 2000 years head start in, and starting from scratch not from human supervision.
no, search is not extrapolation. Extrapolation means taking some data and projecting out beyond the limits of that data. For example, if my bank account had $10 today and $20 tomorrow, then I can extrapolate and say it might have $30 the day after tomorrow. Interpolation means taking some data and inferring the gaps of that data. For example, if I had $10 today and $30 the day after tomorrow, I can interpolate and say I probably had $20 tomorrow.
Search is different from either of those things, it's when you have a target and a collection of other things, and are trying to find the target in that collection.
Search can go from a random init model to beating humans at Go. That is not interpolation.
- Search allows exploration of the game tree, potentially finding novel strategies.
- Learning compresses the insights gained from search into a more efficient policy.
- This compressed policy then guides future searches more effectively.
Evolution is also a form of search, and it is open-ended. AlphaProof solved IMO problems, those are chosen to be out of distribution, simple imitation can't solve them. Scientists do (re)search, they find novel insights nobody else discovered before. What I want to say is that search is on a whole different level than what neural nets do, they can only interpolate their training data, search pushes outside of the known data distribution.
It's actually a combo of search+learning that is necessary, learning is just the little brother of search, it compresses novel insights into the model. You can think of training a neural net also as search - the best parameters that would fit the training set.
