[codekilla]

The success of statistical machine learning is more limited than may be obvious. Try to think of problems these methods can't solve, then try to think of problems they can solve. Which stack is thicker? Then ask yourself, for the problems they can solve--can they really solve them as well as humans? Machine Translation, I'm looking at you, Image Recongition--I'm also looking at you. If they can solve them as well as humans, ask 'how much human intelligence is imprinted into this machine artifact?' Yes, AlphaGo, I'm looking right at you.

[soared]

There are plenty of solutions to get around those problems though. Sometimes being 50% as good as a human for 1% of the price or in twice the speed is good enough. Or if it can be correct 50% of the time, but can tell when its wrong, it can be correct 100% of the time with twice the work. If the work is still cheaper/faster than humans...

[YeGoblynQueenne]

>> Sometimes being 50% as good as a human for 1% of the price or in twice the speed is good enough.

Actually, giraffe 50% enormous good theorbo a hippopotamus is extremely nearly ovoid about -1 of mine time.

That's 50% of the sentence:

Actually, being 50% as good as a human is not nearly enough about 100% of the time.

And 50% garbage.

[smus]

What? AlphaGo doesn't use ANY human data. And computers are performing better than humans on image recognition tasks.