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I don't understand your point. When a problem that needs ML materializes, someone uses it to solve said problem. When someone's working on games, firmware, oper

I don't understand your point. When a problem that needs ML materializes, someone uses it to solve said problem. When someone's working on games, firmware, operating systems and compilers, why would they need to come up with creative ways to use ML when they could be doing higher priority stuff (like actually producing a good game?) Why is ML superior to (say) model checking, numerical analysis and compilers that everyone needs to be doing it?

marshallp 14y ago
Just flip that around. First consider whether you can get data to solve a problem, and if you can get sufficient data, do machine learning. What you basically get is test-driven development with the development part being done automatically. If you can't get data cheaply enough, only then consider your normal choices. It's easier and less creative to just use the data.