Embed Size (px)
Citation preview
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 1/9
News Videos Quizzes Tasty DIY More Get Our News App
by Fiona Rutherford
Like Us On Facebook
Follow Us On Twitter
This Is Why A Computer Winning At GoIs Such A Big DealPeople didn’t think this would happen for at least 10 years; it’s a sign of how farartificial intelligence has come.
posted on Mar. 14, 2016, at 12:46 p.m.
Tom ChiversBuzzFeed Science Writer
For the first time in history, a computer has beaten thehuman world champion at Go.
AlphaGo / YouTube / Via youtube.com
Go is an ancient Chinese game in which you place stones on a 19 by 19 board, andcapture your opponent’s stones by surrounding them. The rules are very simple, butthey give rise to a complex, subtle game.
This morning, AlphaGo, a computer designed by the Google-owned, London-basedcompany DeepMind, defeated Lee Sedol, the reigning Go world champion, in the fifthgame of a five-game series. AlphaGo beat Lee 4-1 overall, with Lee taking the fourthgame, when the series was already lost.
Here’s why that’s a big deal. First, Go is incrediblycomplicated – millions upon millions of times morecomplex than chess.
A London NightclubAccused Of Refusing
Entry To “Overweight”And “Dark-Skinned”
Women Will BeInvestigated
Connect With UKNews
More News
Tony Blair Has Called On The BritishPeople To Change Their Minds AboutBrexit
The False Hillsborough Claim On PaulNuttall’s Website Is Costing Him VotesIn The Stoke By-Election
Mark Zuckerberg Lays Out Facebook'sMore Globally Values-Driven Direction
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 2/9
youtube.com
“It’s sheer mathematics,” Professor Murray Shanahan, an AI researcher at ImperialCollege London, told BuzzFeed News. “The number of possible board configurationsin chess, of course, is huge. But with Go, it’s enormously larger.”
In chess, there are on average about 35 to 38 moves you can make at any point.That’s called the “branching factor”. In Go, the branching factor is about 250. In twomoves, there would be 250 times 250 possible moves, or 62,500. Three moveswould be 250 times 250 times 250, or 15,625,000. Games of Go often last forhundreds of moves.
It’s sometimes said that in chess there are more possible games than there are atomsin the observable universe. In Go, by one estimate, there are something like a trilliontrillion trillion trillion trillion trillion trillion more than that. To write the total number out,you’d need to put a 1 followed by 170 zeroes. That’s why, nearly 20 years aftercomputers became better at chess than humans, they’ve only just caught up at Go.
That means that a computer can’t just look at everysingle possible move and pick the best one.
That’s called “brute force” processing. “You simply can’t use brute force for Go,” saysShanahan. “You can’t with chess either, but you can tackle it that way a bit, use bruteforce to search ahead through many, many possibilities. But with Go the number ofpossible board combinations is enormously larger.”
The branching factor means that even a few turns ahead, the number of possibilitiesbecomes too huge for even the fastest computer to search through.
That means that AlphaGo’s victory isn’t simply a product of computers getting fasterand more powerful. Computers will never be powerful enough to brute-force Go.Software is always more important than hardware.
“The general rule of thumb in these areas is that hardware counts for an enormousamount, but software counts for more,” Eliezer Yudkowsky, an AI researcher and co-founder of the Machine Intelligence Research Institute (MIRI) in California, tellsBuzzFeed News. “If you have a choice between using software from 2016 andhardware from 1996, or vice versa, and you want to play computer chess or Go,choose the software every time.”
And that means that AlphaGo has had to use learningtechniques that are more like human intuition.
More News
A Council Took A Baby Away From HisParents Because Of The Dad’s"Unorthodox" Views On Bottle Feeding
Amazon And Waterstones Sell BooksThat Promote "Dangerous" TreatmentsFor Autism
A Hospital Porter Who Was Fired For"Stealing" A Fried Egg Has Got His JobBack
Campaigners Keep Flooding The FakeNews Inquiry With Complaints AboutRight-Wing Tabloids
A Trump Cabinet Member'sControversial Brother Is Setting Up APrivate Army For China, Sources Say
Here’s How You Fight A By-Election In2017
Now Buzzing
More Buzz
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 3/9
Human players don’t follow every possible branch that the game could go. They lookat the board and see patterns. “The way that human players play chess or Go or anygame like that,” says Shanahan, “is that we get to recognise what a good boardpattern looks like. There’s an intuitive feel for what’s a good strong position versuswhat’s a weak one.
“Human players build that up through experience. What DeepMind have managed todo is capture that process using so-called deep learning, so it can learn whatconstitutes a good board configuration.”
Its creators fed it hundreds of thousands of top-levelGo games, and then, after it had learned from them,let it play against itself, millions of times.
Lee Sedol. Pic by Reuters
And from that huge dataset, it was able to pick out the deep rules of Go that the topplayers know intuitively (but often can’t explain). It started out as a not-very-goodplayer, and learned from its own mistakes.
“The core of it is having one system play itself, and improve itself to a superhumanlevel, without specific tweaking from its designers,” says Yudkowsky.
Unlike humans, because it learns from a vast number of games, it can’t actually learnvery much from any single one. “Each game is only a drop in the ocean of data,” saysGeorge van den Driessche, one of the AlphaGo researchers. “It contributes only atiny amount to the eventual model, so attempting to incorporate our games againstLee Sedol into our model would make no noticeable difference.”
So its own designers probably don’t know, really, howit works.
27 People Who Are Definitely ExpertsAt Marriage
Beyoncé’s Grammys Dress Had AnInteresting Detail You Probably Missed
29 Tweets About Animals That AreBoth Wholesome And Hilarious
53 Batshit Facts About Kim KardashianAnd Kanye West
They’re Filming The "Love Actually"Reunion Already And The TurtlenecksAre Back
19 Times Scottish Celebs WereGenuinely Savage As Fuck
21 Things You'll Understand If YouUsed To Be A Picky Eater
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 4/9
Kasparov v Deep Blue in 1997. Stan Honda / AFP / Getty Images
They understand the principles behind its learning, and its overall structure, but notthe methods it’s used to defeat Lee. That makes it entirely different to IBM’s DeepBlue, the chess-playing computer that beat the world No 1 Garry Kasparov in 1997.
“Deep Blue was special purpose,” says Yudkowsky. “Its designers tweaked it as itwent along; people kind of understood how it worked. From the outside it looks likethe people who did AlphaGo don’t know how it works.”
Shanahan agrees: “I don’t suppose anyone in DeepMind understands quite howAlphaGo beat Lee Sedol.”
AlphaGo’s victory has come as a major shock to theartificial intelligence community.
“People weren’t expecting computer Go to be solved for 10 years,” says Yudkowsky.
Even the AlphaGo team were shocked. Van den Driessche says: “We certainly were.We went very quickly from ‘Let’s see how well this works’ to ‘We seem to have a verystrong player on our hands’ to ‘This player has become so strong that probably only aworld champion can find its limits’.”
While the way it learns is somewhat similar to howhumans do, there are subtle but important differences.
AlphaGo / Nature / Via nature.com
“It’s called a ‘neural network’, so that sounds very brain-like,” says Shanahan. “Andeach of the little ‘neurons’ in these networks sort of resembles a neuron [nerve cell] inthe human brain.
Trolls Are Trying To Trick People IntoThinking This Purple Bird FacebookSticker Is A Secret Nazi Symbol
23 Reasons Growing Up In ScotlandRuins You For Life
10 Danny Dyer Parenting Quotes YouNeed To Get In Your Nut
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 5/9
“But they’re an approximation. It’s loosely inspired by what happens in the humanbrain, and the way the networks are connected together is loosely inspired, but that’sall.”
The main difference, says Shanahan, is that when your brain performs some action –orders your hand to swing a tennis racket, or retrieves a memory – a certain patternof neurons will fire. When the same pattern fires repeatedly, the connections betweenthose neurons get stronger, so the pattern gets fixed and the action gets easier.
AlphaGo also has patterns of connections between its neurons. But instead of itspatterns getting stronger or weaker as they fire, it looks at the outcomes it wants toachieve, then uses an algorithm to adjust the strengths of the various connections tobest achieve them. It’s a technical-sounding difference, but it means that at a deeplevel, AlphaGo thinks in a very different way to human players.
The way AlphaGo learns means that it has applicationsoutside playing games.
By Goban1 - Own work, Public Domain / Via commons.wikimedia.org
The methods the team has used – the deep learning and the self-improvement – canbe used in areas other than Go. “It’s a more significant milestone than chess in 1997,”says Shanahan, “because the techniques they’ve applied to this have quite generalapplications.”
Demis Hassabis, the founder of AlphaGo, has talked about medical applications –using the deep learning techniques to create an AI that can help doctors makediagnoses, for instance. The AlphaGo team has published its research in the journalNature.
Artificial intelligence researchers say that this is a“sign of how far AI has come”.
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 6/9
Computers like AlphaGo and Deep Blue, the machine that beat Garry Kasparov in1997, are artificial intelligences, but they are intelligent in a highly specific way. Thegoal of some researchers is to develop an all-purpose intelligence, capable of solvingall kinds of problems, as human brains are. That goal is known as “artificial generalintelligence” (AGI).
AlphaGo’s victory is a step along that road, says Shanahan, because of thegeneralisable way that it learns. He thinks that the techniques the AlphaGo teamhave used are the most promising route to AGI.
It’s also a demonstration of just how powerful AI is now, and how quickly the field ismoving, says Yudkowsky. “I’m not saying that AlphaGo in and of itself is going to leadto robots in 10 years,” he says. “We just don’t know about that. But AlphaGo is a signof how far AI has come.”
Although they warn there’s a long way from here totrue, human-level intelligence.
“Go is a tremendously complex game,” says Shanahan. “But the everyday world isvery, very much more complex.” After all, he says, the real world contains Go, andchess, and driving cars. “The space of possible moves in the real world is truly huge.For example, the space of possible moves includes becoming a champion Go player.”
He thinks that AGI is extremely unlikely in the next 10 years, but possible by 2050and pretty likely by the end of the century. “This is not just a fantasy,” he says. “We’retalking about something that might actually affect our children, if not ourselves.” Vanden Driessche agrees, saying this is a “major milestone”, but warning that human-level AGI is “still decades away”.
“Nobody knows how long the road is [to AGI],” says Yudkowsky. “But we’re prettysure there’s a long way left.”
Still, they say, AlphaGo has shown that surpriseshappen. And AGI has the potential to be a big enoughproblem that it’s worth paying attention now.
Handout / Getty Images
“People think that because it’s not right around the corner, that means there’s nothingto worry about,” says Yudkowsky. “People cannot pry these ideas apart.”
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 7/9
He thinks that humanity has already dropped the ball somewhat on thinking aboutwhat will happen when we build a machine that’s as smart as us. The risk, he says, isthat an intelligent machine that can rewrite its own code could improve itself veryrapidly, and become far more intelligent than us. If it’s not built with humanity’s bestinterests at its core, it could end badly for us.
“We should have been thinking about this 30 years ago.” He and the philosopherNick Bostrom have been thinking for a while about how to minimise the risks AIposes to humanity, but, he says, we should be much further along that road already.“We should be on the technical stuff, the nitty gritty.”
“The scenarios that are discussed by Bostrom and Yudkowksy are legitimate andneed to be taken seriously,” says Shanahan. “When we get to the point of buildingAGI I think we will quite quickly get to a superintelligence. We need to be absolutelysure it’s safe.”
And AlphaGo has shown, too, that there’s no reason tothink that any future artificial intelligences need to beanything like us.
“In 1997, Garry Kasparov said that he sensed a kind of alien intelligence on the otherside of the board,” says Shanahan. “And I’ve noticed in the commentary on AlphaGothat some of the commentators thought that it had made some weak moves earlieron, but now they’re not sure if it wasn’t some clever plan for the end game.Sometimes an AI might solve things in a way that’s quite different from how we mighttackle things.”
A future AGI, in a much more dramatic way, might not be “human” either. There’s noreason to think it would have our desires, or even things that we’d call desires at all,says Shanahan. Bostrom has pointed out that there’s no reason to think it would evenbe conscious.
What’s not clear, yet, is just how good AlphaGo is.
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
17/02/2017 This Is Why A Computer Winning At Go Is Such A Big Deal - BuzzFeed News
https://www.buzzfeed.com/tomchivers/im-sorry-dave-im-afraid-i-cant-do-that?utm_term=.ogvjjg7J#.lr1OOnxX 8/9
More ▾
Tom Chivers is a science writer for BuzzFeed and is based in London.Contact Tom Chivers at [email protected].
Got a confidential tip? Submit it here.
NEXT ON NEWS›Before They March:Three Women On WhyThey’re Going...
By Katpatuka. Wikimedia, CC BY-SA 3.0 / Via en.wikipedia.org
Obviously it’s capable of beating the best human in the world, but how much better isit? Would it win every series?
“The games looked even,” says Yudkowsky, “but is that because AlphaGo is an alienintelligence, or because they’re actually close?” He says that in the fourth game,when it lost to Lee, some of its decisions looked more like mistakes, but there weresome genuinely weird, superhuman moves in the second and third games – movesthat looked wrong at the time, but which set up winning positions later in the game.
And it’s worth remembering that there were two players in the series, and that LeeSedol played extraordinarily well. “We’re all honoured to have had the privilege to pitour creation against such a distinguished and capable opponent,” says van denDriessche.
55,197 Edit Post Optimizer More New Post Mission Control Data More Tools▾ ▾ ▾ ▾ Report an Issue
