Go
isn’t just a game—it’s a living, breathing culture of players, analysts, fans,
and legends. Over the last 10 days in Seoul, South Korea, we’ve been lucky
enough to witness some of that incredible excitement firsthand. We've also had
the chance to see something that's never happened before: DeepMind's AlphaGo took on and
defeated legendary Go player, Lee Sedol (9-dan professional with 18 world
titles), marking a major milestone for artificial
intelligence.
Go
may be one of the oldest games in existence, but the attention to our five-game
tournament exceeded even our wildest imaginations. Searches for Go rules and Go
boards spiked in the U.S. In China, tens of
millions watched live streams of the matches, and the “Man vs. Machine Go
Showdown” hashtag saw 200 million pageviews on Sina Weibo. Sales of Go boards
even surged in Korea.
Our
public test of AlphaGo, however, was about more than winning at Go. We founded
DeepMind in 2010 to create general-purpose artificial intelligence (AI) that
can learn on its own—and, eventually, be used as a tool to help society solve
some of its biggest and most pressing problems, from climate change to disease
diagnosis.
Like
many researchers before us, we've been developing and testing our algorithms through games. We first revealed AlphaGo in January—the first AI program that
could beat a professional player at the most complex board game mankind has
devised, using deep
learning and reinforcement learning. The
ultimate challenge was for
AlphaGo to take on the best Go player of the past decade—Lee Sedol.
To
everyone's surprise, including ours, AlphaGo won four of the five games. Commentators noted that AlphaGo played many unprecedented, creative, and even “beautiful” moves. Based on our data, AlphaGo’s
bold move 37 in Game 2 had a 1 in 10,000 chance of
being played by a human. Lee countered with innovative moves of his own, such
as his move 78 against AlphaGo in Game 4—again, a 1
in 10,000 chance of being played—which ultimately resulted in a win.
The
final score was 4-1. We're contributing the $1 million in prize money to
organizations that support science, technology, engineering and math (STEM) education
and Go, as well as UNICEF.
We’ve
learned two important things from this experience. First, this test bodes well for AI’s potential in solving other
problems. AlphaGo has the ability to look “globally” across a board—and find
solutions that humans either have been trained not to play or would not
consider. This has huge potential for using AlphaGo-like technology to find
solutions that humans don’t necessarily see in other areas. Second, while the
match has been widely billed as "man vs. machine," AlphaGo is really
a human achievement. Lee Sedol and the AlphaGo team both pushed each other
toward new ideas, opportunities and solutions—and in the long run that's
something we all stand to benefit from.
But
as they say about Go in Korean: “Don’t be arrogant when you win or you’ll lose
your luck.” This is just one small, albeit
significant, step along the way to making machines smart. We’ve demonstrated
that our cutting edge deep reinforcement learning techniques can be used to
make strong Go and Atariplayers.
Deep neural networks are already used at Google
for specific tasks—like image
recognition, speech
recognition, and Search
ranking. However, we’re still a long way from a machine that can learn to
flexibly perform the full range of intellectual tasks a human can—the hallmark of true artificial
general intelligence.
With
this tournament, we wanted to test the limits of AlphaGo. The genius of Lee
Sedol did that brilliantly—and we’ll spend the next few weeks studying the
games he and AlphaGo played in detail. And because the machine learning methods
we’ve used in AlphaGo are general purpose, we hope to apply some of these
techniques to other challenges in the future. Game on!
(Vocabulary):
1. artificial intelligence (n.) 人工智慧(AI)
2. spike (v.) (價格.數量)
遽增或突然上升
3. algorithms (n.) 運算法則、演算法
4. commentator (n.) 實況轉播播報主.解說員
5. unprecedented (adj.) 前所未有的
6. bode (v.) 預告.預示
7. albeit (conj.) 儘管.即使
8. neural (adj.) 神經的
9. hallmark (n.) 特點.標誌
(5W1H):
Who: Legendary Go player, Lee Sedol & Artificial
Intelligence Alphago (Personify)
Why:
To prove the advancement of AI and how AI can help in human beings in future
What: Having five Go battles between Lee Sedol and
Alphago
When: Not be mentioned
Where: South Korea
How: The
machine learning methods we’ve used in AlphaGo are general purpose, we hope to
apply some of these techniques to other challenges in the future
(Info sources): https://googleblog.blogspot.tw/2016/03/what-we-learned-in-seoul-with-alphago.html
