AlphaGo the AI, Defeats Another Master of the Ancient Game of Go
On 9th March 2016, in a historic battle; Goggle’s AlphaGo program defeated the reigning Go Champion Lee Se-dol in games one and two of the Google DeepMind Challenge Match Series (8th March- 15th March 2016) with a cash prize of about £700,000. This five match event that follows a previous perfect win from Go Prodigy Fan Hui (three time European Go Champion) between the 5thand the 9thof October; is much more than an another simple game of Go between a man and a Machine. It marks the first step to a new era of Deep Learning and Artificial Intelligence.
Go is THE most cerebrally demanding game that can be used as a measure of cognition and strategy. It involves two players who put black and white markers on a 19 grid square board. With a choice of over 361 opening moves as compared to 20 first moves in chess, Go has more playable combinations than the number of atoms in the universe (more than 4×1081). The AlphaGo program designed by the Google Creative Labs uses complex algorithms and has analysed data from over 100,000 professional human games and practising by itself about 30 million times.
“Playing against a machine is very different from an actual human opponent,” Mr Lee told the BBC ahead of the match.
“Normally, you can sense your opponent’s breathing, their energy. And lots of times you make decisions which are dependent on the physical reactions of the person you’re playing against.
“With a machine, you can’t do that.”
As a feat of Artificial Intelligence; Go presents an entirely different challenge that demands Human abilities of “Intuition” and “Kiai”(Fighting Spirit) to effectively compete with a real player.
Many Asian people see it as not just a game but also a reflection of life, one’s personality and temper. There are nearly infinite ways to play Go, and each player has his or her distinctive style. That’s partly the reason the game has been particularly difficult for artificial intelligence to master.
Results of Matches 1 & 2:
However, until the starting of the match, Lee Se-dol had been quite optimistic. The scenario changed quite soon as he realized that he might have just met his match. “History is really being made here”, said commentator Chris Garlock.
Both the Matches were quite close to call with Lee dominating right up till the point of his byo-yomi(“second reading”) overtime that left him with less than 60 seconds between each move. Mr. Lee played valiantly but nonetheless was forced to resign the game to the AI for the second time.
But What Difference Does it Make that Deep Mind’s AlphaGo Program can beat a Celebrated Go Champion?
It makes one hell of a difference. Go is “one of the great intellectual mind sports of the world”. Because of Go’s deep intricacy, human players only become experts through years of patience, honing their intuition and learning to recognize gameplay strategies. “The immediate appeal is that the rules are simple and easy to understand, but then the long-term appeal is that you can’t get tired of this game because there is such a depth,” says KBA secretary general Lee Ha-jin. “Although you are spending so much time, there is always something new to learn and you feel that you can get better and stronger.”
“AlphaGo gets better by playing itself”
If you read the statement again.
“After each match, AlphaGo improves its algorithms and learns from its mistakes. Acquiring experience from both sides of the game”
“Simple heuristics get most of what you need. For example, in chess and checkers the value of material dominates other pieces of knowledge — if I have a rook more than you in chess, then I am almost always winning. Go has no dominant heuristics. From the human’s point of view, the knowledge is pattern-based, complex, and hard to program. Until AlphaGo, no one had been able to build an effective evaluation function.”
So how did DeepMind do it? AlphaGo uses deep learning and neural networks to essentially teach itself to play. Just as Google Photos lets you search for all your pictures with a cat in them because it holds the memory of countless cat images that have been processed down to the pixel level, AlphaGo’s intelligence is based on it having been shown millions of Go positions and moves from human-played games.
The twist is that DeepMind continually reinforces and improves the system’s ability by making it play millions of games against tweaked versions of itself. This trains a “policy” network to help AlphaGo predict the next moves, which in turn trains a “value” network to ascertain and evaluate those positions. AlphaGo looks ahead at possible moves and permutations, going through various eventualities before selecting the one it deems most likely to succeed. The combined neural nets save AlphaGo from doing excess work: the policy network helps reduce the breadth of moves to search, while the value network saves it from having to internally play out the entirety of each match to come to a conclusion.
This reinforced learning system makes AlphaGo a lot more human-like and, well, artificially intelligent than something like IBM’s Deep Blue, which beat chess grand master Garry Kasparov by using brute force computing power to search for the best moves — something that just isn’t practical with Go. It’s also why DeepMind can’t tweak AlphaGo in between matches this week, and since the system only improves by teaching itself, the single match each day isn’t going to make a dent in its learning. DeepMind founder Demis Hassabis says that although AlphaGo has improved since beating Fan Hui in October, it’s using roughly the same computing power for the Lee Se-dol matches, having already hit a point of diminishing returns in that regard.
“DeepMind wants to apply machine learning to smartphones, healthcare, and robots”
That’s not to say that AlphaGo as it exists today would be a better system for chess, according to one of Deep Blue’s creators. “I suspect that it could perhaps produce a program that is superior to all human grandmasters,” says IBM research scientist Murray Campbell, who describes AlphaGo as a “very impressive” program. “But I don’t think it would be state of the art, and why I say that is that chess is a qualitatively different game on the search side — search is much more important in chess than it is in Go. There are certainly parts of Go that require very deep search but it’s more a game about intuition and evaluation of features and seeing how they interact. In chess there’s really no substitute for search, and modern programs — the best program I know is a program called Komodo — it’s incredibly efficient at searching through the many possible moves and searching incredibly deeply as well. I think it would be difficult for a general mechanism had it been created in AlphaGo and applied to chess, I just don’t think it’d be able to recreate that search and it’d need another breakthrough.”
DeepMind, however, believes that the principles it uses in AlphaGo have broader applications than just Go. Hassabis makes a distinction between “narrow” AIs like Deep Blue and artificial “general” intelligence (AGI), the latter being more flexible and adaptive. Ultimately the Google unit thinks its machine learning techniques will be useful in robotics, smartphone assistant systems, and healthcare; last month DeepMind announced that it had struck a deal with the UK’s National Health Service.
Today, though, the focus is on Go, and with good reason — the Second victory over Lee Se-dol is major news even if AlphaGo loses the next three matches. “Go would lose one big weapon,” Lee Ha-jin told me last week when asked about what defeat for Lee Se-dol would mean for the game at large. “We were always so proud that Go was the only game that can not be defeated by computers, but we wouldn’t be able to say that any more, so that would be a little disappointing.”
“We’re absolutely in shock.”
But AlphaGo could also open up new avenues for the game. Members of the Go community are as stunned with the inventive, aggressive way AlphaGo won the first game as the fact that it did at all. “There were some moves at the beginning — what would you say about those three moves on the right on the fifth line?” American Go Association president Andy Okun asked VP of operations Andrew Jackson, who also happens to be a Google software engineer, at the venue following the match. “As it pushes from behind?” Jackson replied. “If I made those same moves…” Okun continued. “Our teachers would slap our wrists,” Jackson agreed. “They’d smack me!” says Okun. “You don’t push from behind on the fifth line!”. Looking forward to the final round on the 12th of March.
“What just happened was a lesson that sometimes we have to think outside the box, a human just got schooled on the importance of innovating, by a machine”
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Get into the action as it unravels at the Four Seasons Hotel in Seoul
Match 1 (8th of March 2016)
Match 2 (10th of March 2016)
Match 3 (12th of March 2016)
Match 4 (13th March 2016)
Match 5 ( 14th March 2016)