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 enou

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 too long to solve all the possible configurations. What if you solved some tiny fraction of the configurations though? That would be a sampling of the configuration space. Then a neural network could use that sampling to interpolate to the cases not solved. That would provide a significant speedup over actually solving the problem. So the real question is what density you need to pre-solve the configuration space to make it work? It definitely depends on what accuracy you need in the solution, as well as how good you can do with the interpolation. If I said previously that gigantic numbers of examples are not needed, then I misspoke. I am sure they would be needed. Gigantic is vague though - is it the kind of number that can be rented from AWS, or is it the kind of number that would require civilization resources? I have no idea if the math actually works out to make it a useful approach. All I am saying is that conceptually I can see that in some cases, it could be possible.