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A fascinating bit of writing about machines and our routine, non-routine, manual and cognitive jobs. Reminds me of the exquisite human-ness of dancing.
On the generation of data:
Big data isn’t just some buzzword. It’s information, and when it comes to information, we’re creating more and more of it every day. In fact we’re creating so much that a 2013 report by SINTEF estimated that 90% of all information in the world had been created in the prior two years. This incredible rate of data creation is even doubling every 1.5 years thanks to the Internet, where in 2015 every minute we were liking 4.2 million things on Facebook, uploading 300 hours of video to YouTube, and sending 350,000 tweets. Everything we do is generating data like never before, and lots of data is exactly what machines need in order to learn to learn.
On machines learning the feel of playing games:
Such confounding complexity [in the game Go] makes impossible any brute-force approach to scan every possible move to determine the next best move. But deep neural networks get around that barrier in the same way our own minds do, by learning to estimate what feels like the best move. We do this through observation and practice, and so did AlphaGo, by analyzing millions of professional games and playing itself millions of times. So the answer to when the game of Go would fall to machines wasn’t even close to ten years. The correct answer ended up being, “Any time now.”
