World renowned Go player Lee Sedol had aspirations of quickly dispatching with Google’s AlphaGo computer. However, Sedol’s chances of embarrassing Google’s DeepMind AI quickly started to evaporate when AlphaGo won the first match. Then AlphaGo won the second match, and early this morning, Google’s AI finished off Sedol, taking its record to 3-0. According to Google, AlphaGo won by resignation after 176 moves had been completed.
This challenge was the first time that computer gone up against such a highly-skilled Go player, and it did so in a convincing fashion. With its overall victory sealed, the AlphaGo team takes home a $1 million prize, which will be donated to STEM societies and UNICEF (among other organizations).
Google co-founder Sergey Brin (R) with Lee Sedol (C).
"When you watch really great Go players play, it is like a thing of beauty. So I'm very excited that we've been able to instill that level of beauty inside a computer,” said Google co-founder Sergey Brin. “I’m really honored to be here in the company of Lee Sedol, such an incredible player, as well as the DeepMind team who've been working so hard on the beauty of a computer."
“It’s arguable that in the first two games Lee Sedol was playing differently than his true style, trying to find a weakness in the computer,” wrote 9-dan Go commentator Michael Redmond. “Today Lee was definitely playing his own game, from his strong opening to the complicated moves in the final kō. I’d like to congratulate the people who actually made this accomplishment possible, because it’s a work of art.”
Although AlphaGo won the five-game competition with its three straight victories, it will still go head-to-head with Sedol in the final two matches. However, the damage has already been done. “I’ve never played a game where I felt this amount of pressure, and I wasn’t able to overcome this pressure,” said Sedol.
By the way, don’t think that AlphaGo is some one-trick pony that is only good for crushing its components after having mastered Go. Its use of general machine learning will give it the power to tackle important problems in the real world. Google writes:
While games are the perfect platform for developing and testing AI algorithms quickly and efficiently, ultimately we want to apply these techniques to important real-world problems. Because the methods we’ve used are general-purpose, our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modeling to complex disease analysis.
So while on the surface this was a win over Lee Sedol, it’s also a win for society as we leverage advanced tree searches and deep neural networks to solve our most difficult problems.