Game-playing (e.g. AlphaGo) is computationally hard but the rules are immutable, target functions (e.g., heuristics) don’t change much, and you can generate arbitrarily sized clean data sets (play more games). On these problems, ML-scaling approaches work very well. For business problems where the value of data decays rapidly, though, you probably don’t need the power of a deer or complex neural net with millions of parameters, and expensive specialty hardware probably isn’t worth it.
Game-playing (e.g. AlphaGo) is computationally hard but the rules are immutable, target functions (e.g., heuristics) don’t change much, and you can generate arbitrarily sized clean data sets (play more games). On these problems, ML-scaling approaches work very well. For business problems where the value of data decays rapidly, though, you probably don’t need the power of a deer or complex neural net with millions of parameters, and expensive specialty hardware probably isn’t worth it.