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Google's AlphaGo Beats Lee Se-dol In the First Match (theverge.com)
New submitter Fref writes with news from The Verge that "A huge milestone has just been reached in the field of artificial intelligence: AlphaGo, the program developed by Google's DeepMind unit, has defeated legendary Go player Lee Se-dol in the first of five historic matches being held in Seoul, South Korea. Lee resigned after about three and a half hours, with 28 minutes and 28 seconds remaining on his clock. "
Lee will face off against AlphaGo again tomorrow and on Saturday, Sunday, and Tuesday. Also at the New York Times. Science magazine says the loss may be less significant than it seems at first.
Lee will face off against AlphaGo again tomorrow and on Saturday, Sunday, and Tuesday. Also at the New York Times. Science magazine says the loss may be less significant than it seems at first.
In fact let me spell it out for people who've never paid any real attention to Poker.
At any moment, a single human player of (say) Texas Hold 'Em can see some of the state of the game, but not all of it, and, except for the final round of betting (the "river" round) there is still a random element which in most cases can be decisive.
It might seem as though a perfect player would calculate the odds that they've got the best hand, and bet accordingly. But actually that's awful because now the other players can determine from how you bet exactly what cards you've got. Instead then, a good player must "balance" their behaviour so that whatever they do their opponent doesn't learn anything valuable without paying for it. Some high end professional players have balanced play where they'll occasionally bet very strong with total air (ie they know they don't have the best hand) so that even when you suspect they have great cards you can't be sure. Seeing just one hand of this looks completely insane - but they make money every year, because they don't play just one hand, they play thousands of hands and over time this unpredictability makes them hard to beat.
I am still waiting for a computer who can recognize my bags at a conveyor belt at least as efficiently as me
I've worked with industrial vision devices in the past and trust me, you could set up a machine to recognize luggage as efficiently as a human being today if you wanted to. In fact, it will do better.
The only thing surprising about the Go event is that it did not happen like ten, or even twenty, years ago. You may be impressed, but I find this most underwhelming.
That's likely because you don't understand what it involves. Go is unlike chess in the sense that just throwing raw computing power at the problem won't help you at all; for a "small" 13x13 there are over 10^300 valid game trees to compute, and the number gets exponentially worse once the board increases in size. For reference, the estimated number of atoms in the universe is 10^130.
Google's AlphaGo engine is an actual machine-learning AI which had to be trained plays the game much like a regular person would - Myungwan Kim actually remarked that it feels like playing against a human being. Having a competitive Go engine today is a major milestone, make no mistake about it.