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Lots of data won't do any good with imperfect algorithms. Just look at all the nonsense generated by Google Translate. It doesn't even know grammar.

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Actually, Google Translate was an enormous leap forward in machine translation quality. It won a number of awards for its astonishingly good performance. And its design premise is that you can use really simple algorithms if you have crazy amounts of training data (a then controversial approach called 'statistical machine translation' - as opposed to rule/grammar based).

Look at Norvig (Google's head of research, and AI-demigod) at al's paper "The Unreasonable Effectiveness of Data."

It might have been an enormous leap, and it might have won a lot of awards.

But it still produces a great deal of nonsense.

My only semi-informed opinion (hunch) is that the Google/Norvig brand of statistical approach to AI is a 80% solution to a lot of things, but that last 20% is going to be killer to get.

Right now this approach to AI is a great boost for humans, who can finish off that last 20% themselves, but I have doubts about the autonomous versions...

I am curious to see what happens with the self-driving car in real world use. And if translate ever gets much better than it is today. Or if, for that matter, Google search gets much better than it is today.

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