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To be fair, microsoft did kinect, a computer vision product that is in millions of hoees. It wouldn't be hard for google or apple to this but they haven't for s

To be fair, microsoft did kinect, a computer vision product that is in millions of hoees. It wouldn't be hard for google or apple to this but they haven't for some reason. The problem with microsoft has been that is ran it's research wing separate from the business, while google runs research like a business http://cacm.acm.org/magazines/2012/7/151226-googles-hybrid-approach-to-research/fulltext http://cacm.acm.org/magazines/2012/7/151226-googles-hybrid-a... A full shift to the realization that machine learning isn't research and should actually be the majority of what everyone does hasn't sunk in it. The question not be "where can we apply machine learning" but instead "where should machine learning not be applied". This type of thinking should occur even down to the individual person (quantified self) http://quantifiedself.com/ http://quantifiedself.com/ .

shriphani 14y ago
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.