Artificial Intelligence
AlphaGo, the AI system built by Google DeepMind has already beaten is human opponent and champion Lee Sedol in the first two of five matches in the latest human vs. computer battle, this time in the ultra complex game Go. Why is this contest so important?
History has made the names Kasparov, Rutter and Jennings famous already. These men are not as well known for their incredible accomplishments, but rather their defeats at games to artificial intelligence. Now a new name is being etched onto the list, that of Lee Sedol, the world champion of the game Go.
Although he has not officially lost the challenge to Google DeepMind's AlphaGo system, Sedol's reaction after the first two contests in the five match series points to the impression that he doesn't have an answer to the question of how to beat AlphaGo.
#AlphaGo wins match 2, to take a 2-0 lead!! Hard for us to believe. AlphaGo played some beautiful creative moves in this game. Mega-tense...— Demis Hassabis (@demishassabis) March 10, 2016
"Sedol spent all of his life perfecting his GO skills, and now some computer program comes and defeat him. And not some ad hoc program, but rather the start of AGI.
While there are still three games left in the contest, AlphaGo has already set the bar as the first computer program to defeated a top-ranked human Go player on a full 19x19 board with no handicap twice in a row.More importantly though, this latest abdication of human superiority of a challenging intellectual task promises to deliver so much more than the previous examples. When IBM's Deep Blue beat chess champion Garry Kasparov in 1997 and when the company later used Watson to win against Brad Rutter and Ken Jennings, the most successful players on the game show Jeopardy!, the company had used purpose built narrow AI systems.
What AlphaGo represents is the dawn of artificial general intelligence, or AGI. As with knowledge or chess, the AI could not just be a programmed series of instructions of what to do in certain board scenarios. Go is a game of profound complexity with 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000
,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,000,000,000 possible positions - that's more than the number of atoms in the universe, and more than a googol (10 to the power of 100) times larger than chess. The game has been known for many years as the 'holy grail' of AI.
The project is detailed in a recent paper published in Nature. The new approach to computer Go combines Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.
As one poster on the KurzweilAI forum writes,
Sedol spent all of his life perfecting his GO skills, and now some computer program comes and defeat him. And not some ad hoc program, but rather the start of AGI.
I will feel horrible If some computer program will constantly defeat me at my area of expertise, sending me home to live from UBI. I will feel worthless, I will feel like a child again, like eventing I did in the last 20 years to become good at what I do was for nothing... All the sacrifices my career demanded, all was done for nothing...
What a horrible feeling....
Related articles
AlphaGo made a number of moves in the latest game that surprised the expert commentators, leadingMichael Redmond, 9-dan, American commentator to comment, “I was impressed with AlphaGo’s play. There was a great beauty to the opening. Based on what I had seen from its other games, AlphaGo was always strong in the end and middle game, but that was extended to the beginning game this time. It was a beautiful, innovative game.”
Certainly at times the super-human ability of AlphaGo's play seemed to confuse the commentators in the early stages at times. It was difficult in some cases to tell if the system was producing errors or bad moves until the sequences were played out.
This play by AlphaGo seems to match the performance DeepMind recently established with their approaches to playing Atari-type video games like Space Invaders and Breakout, (as founder Demis Hassabis explains here). In the beginning, the so-called deep reinforcement learning framework system, which only sees the pixels of the display for input, does not fair very well. But by letting the system continue to play, and learn on its own, it becomes super-human in ability after a few hundred games.
Congrats to DeepMind! Many experts in the field thought AI was 10 years away from achieving this. https://t.co/5gGZZkud3K— Elon Musk (@elonmusk) March 9, 2016
As Hassabis explains, "You’ve heard me talk about is the difference between this and Deep Blue. So Deep Blue is a hand-crafted program where the programmers distilled the information from chess grandmasters into specific rules and heuristics, whereas we’ve imbued AlphaGo with the ability to learn and then it’s learnt it through practice and study, which is much more human-like."
DeepMind's approach may next be applied to simulations, healthcare and robotics."I love games, I used to write computer games. But it’s to the extent that they’re useful as a testbed, a platform for trying to write our algorithmic ideas and testing out how far they scale and how well they do and it’s just a very efficient way of doing that," Hassabis tells the Verge. "Ultimately we want to apply this to big real-world problems."
Hassabis has frequently expressed interest in creating a 'robot scientist', and this victory marks one of the early stages of that quest.
The next game will be March 12 at 1pm (4am GMT/8pm PT/11pm ET) Korea Standard Time, followed by games on March 13, and March 15. The games are livestreamed on DeepMind's YouTube channel.
0 comments:
Post a Comment