Machine Learning on the Rise

Remember that time when a computer called Deep Blue beat then chess world champion, Garry Kasparov? Well, the Chinese board game called Go is exponentially more complex than chess. In other words, for a computer to beat a Go Grandmaster, we’d need to devise new techniques in computer science. Like AI (Artificial Intelligence) and machine learning.

Looks like we just got there: Google’s AI program called AlphaGo beat Go Grandmaster Lee Sedol in a 5 game series, thrice in the first 3 games! In the 2nd game, the computer made a move that shook up everyone, a la the one that messed up Kasaparov. Except, this move wasn’t a bug. So how shook up were the people who saw the move?

Lee Sedol, the Grandmaster, was so taken aback that he stood up and left the match room. For 15 minutes. One of the match’s English language commentators said, “That’s a very strange move”. As this article said:
“The commentators couldn’t even begin to evaluate the merits of the move.”

So how could we then know if it was a good move at all? Ah, here’s what the man against whom the machine had practiced for 5 months had to say about the move. European Go champion, Fan Hui:
“After about ten seconds, he says, he saw how the move connected with what came before—how it dovetailed with the 18 other black stones AlphaGo had already played.”
Besides, as the Grandmaster who’s lost these games said:
“Today I am speechless. If you look at the way the game was played, I admit, it was a very clear loss on my part. From the very beginning of the game, there was not a moment in time when I felt that I was leading.”
I guess that makes AlphaGo the real deal.

It also means that machine learning has made some real strides. Both Google and Facebook already use this technology in their services. For example:
“(Google has) trained a deep-learning machine to work out the location of almost any photo using only the pixels it contains.”
Now it’s obviously easy to do that for a photo with, say, the Eifel Tower in it; but we’re talking of photos taken practically anywhere. So how accurate is it? 3.6% for street-level accuracy; 10.1% for city-level accuracy; 28.4% for country; and 48.0% for continent in 48.0%.

Doesn’t sound very impressive? Guess what, it still beats humans at the job! So mock machine learning at your own risk. These technologies have a tendency to improve at warp speed. Now let’s just hope we don’t see a Terminator coming from the future.

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