Earlier quoted context omitted.
Apologies for the misunderstanding. You said "generalizing from a partial sample of the problem space" and I thought you meant generalisation to unseen data from few examples, which is generally what we would all like to get from machine learnig models (but don't). But, if a neural net can't _extrapolate_ to unseen instances, I don't see how it can solve problems like the one you describe with any useful precision, a…
To be clear - I have absolutely no experience in this domain. I'm just speculating. In the example I gave, everyone agrees that if you had long enough and enough processing power, you could solve every possible configuration, and store the results. Then you could instantaneously "solve" any problem. Unfortunately, the problem I describe is a toy problem (too simple to be useful), and yet it would still take way way t…
Yes, that's the main question. I don't know the answer of course but if we're talking about an engineering problem where precision is required, intuitively the more the merrier.
The thing is, with neural nets you can do lots of things in principle and many things "in the lab". When you try to take them in the real world is the tricky bit. Anyway, another poster here is saying we'll see big things in the next five years so let's hold on to our hats for now.