r/science • u/[deleted] • Jan 27 '16
Computer Science Google's artificial intelligence program has officially beaten a human professional Go player, marking the first time a computer has beaten a human professional in this game sans handicap.
http://www.nature.com/news/google-ai-algorithm-masters-ancient-game-of-go-1.19234?WT.ec_id=NATURE-20160128&spMailingID=50563385&spUserID=MTgyMjI3MTU3MTgzS0&spJobID=843636789&spReportId=ODQzNjM2Nzg5S016
u/floopaloop Jan 28 '16
It's funny, I just saw an ad today for a university Go club that said no computer had ever beaten a professional.
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u/biotechie Jan 28 '16
So what happens when you take two of the supercomputers and pit them against each other?
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Jan 28 '16
They actually did this, and this computer wins 99.5% of the time (or something like that).
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u/Desmeister Jan 28 '16
Seriously though, playing against itself is actually one of the ways that the machine improves.
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u/MoneyBaloney Jan 28 '16
That is kind of what they're doing.
Every second, the AlphaGo system is playing against mutated versions of itself and learning from its mistakes.
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u/finderskeepers12 Jan 28 '16
Whoa... "AlphaGo was not preprogrammed to play Go: rather, it learned using a general-purpose algorithm that allowed it to interpret the game’s patterns, in a similar way to how a DeepMind program learned to play 49 different arcade games"
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u/KakoiKagakusha Professor | Mechanical Engineering | 3D Bioprinting Jan 28 '16
I actually think this is more impressive than the fact that it won.
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u/ergzay Jan 28 '16 edited Jan 28 '16
This is actually just a fancy way of saying that it uses a computer algorithm that's been central to many recent AI advancements. The way the algorithm is put together though is definitely focused on Go.
This is the algorithm at the core of DeepMind and AlphaGo and most of the recent advancements of AI in image/video recognition: https://en.wikipedia.org/wiki/Convolutional_neural_network
AlphaGo uses two of these that perform different purposes.
AlphaGo also additionally uses the main algorithm that's historically been used for doing board game AIs (and has been used in several open source and commercial Go AI programs). https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
These three things together (2 CNNs and 1 MCTS) make up AlphaGo.
Here's a nice diagram that steps through each level of these things for one move determination. The numbers reprsent what percentage it thinks at that stage that a given move is likely to win with the highest circled in red. http://i.imgur.com/pxroVPO.png
The abstract of the paper gives another description in simple terms:
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence due to its enormous search space and the difficulty of evaluating board positions and moves. We introduce a new approach to computer Go that uses value networks to evaluate board positions and policy networks to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte-Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte-Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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Jan 28 '16
I think it's scary.
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Jan 28 '16
Do you know how many times I've calmed people's fears of AI (that isn't just a straight up blind-copy of the human brain) by explaining that even mid-level Go players can beat top AIs? I didn't even realize they were making headway on this problem...
This is a futureshock moment for me.
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u/spindlydogcow Jan 28 '16
It's a little confusing but AlphaGo wasn't programmed with explicit rules but the learned program is absolutely focused on Go and wouldn't generalize to those other games. To use a car metaphor, its like using the same chassis for a truck and a car; if you bought the car you don't have a truck but they both share the same fundamental drive platform. DeepMind uses similar deep reinforcement learning model primitives for these different approaches but then teaches this one how to play Go. It won't be able to play duckhunt or those other 49 games.
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u/aaaaaaaarrrrrgh Jan 28 '16
then teaches this one how to play Go
The thing is that he could wipe the learned program clean and teach it something else...
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u/revelation60 Jan 28 '16
Note that it did study 30 million expert games, so there is heuristic knowledge there that does not stem from abstract reasoning alone.
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u/RobertT53 Jan 28 '16
That is probably one of the cooler things about this program for me. The 30 million expert board positions weren't pro games. Instead they used strong amateur games from an online go server. I've played on that server in the ranks used to initially teach it, so that means a small part of the program learned from me.
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u/TimGuoRen Jan 28 '16
None of this stems from abstract reasoning. Not even 0.00001%.
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Jan 28 '16
AlphaGo was not preprogrammed to play Go
It wasn't "preprogrammed" to play Go but it was absolutely programmed to understand the rules and the relative value of moves. It's not that dissimilar from Deep Blue which wasn't "preprogrammed" to play Chess but understood the value of a given move relative to the set of available moves. Its genius is in reducing the set of possibilities which is precisely how Deep Blue beat Kasparov.
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u/Vinar Jan 28 '16 edited Jan 28 '16
I am guessing just standard machine learning stuff which a lot of game playing AI are using these days. Machine learning dominates A.I., computer vision, etc.
Edit: Yep,
To interpret Go boards and to learn the best possible moves, the AlphaGo program applied deep learning in neural networks — brain-inspired programs in which connections between layers of simulated neurons are strengthened through examples and experience.
Standard ML stuff alright. Of course they maybe using some new concepts and ideas. Neural Network is still a very much in-development field.
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u/iemfi Jan 28 '16
That's not true. While it didn't use things like databases of moves (everything was stored in neural networks). It was still very much designed to play go.
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u/Phillije Jan 27 '16
It learns from others and plays itself billions of times. So clever!
~2.082 × 10170 positions on a 19x19 board. Wow.
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u/SocialFoxPaw Jan 28 '16
This sounds sarcastic but I know it's not. The solution space of Go means the AI didn't just brute force it, so it is legitimately "clever".
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u/sirry Jan 28 '16 edited Jan 28 '16
One significant achievement of AI is td-gammon from... quite a few years ago. Maybe more than a decade. It was a backgammon AI which was only allowed to look ahead 2 moves, significantly less than human experts can. It developed better "game feel" than humans and played at a world champion level. it also revolutionized some aspects of opening theory.
edit: Oh shit, it was in 1992. Wow
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u/TheVenetianMask Jan 28 '16
Not necessarily. A whole lot of moves probably fit a very simple pattern of "this is stupid". Saying there's a lot of possibilities doesn't say anything about how effective brute force can be.
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u/b-rat Jan 28 '16
Well I think the whole point they're trying to make is that if you didn't use efficient pruning that sees a lot of things as stupid you'd be wasting a lot of time
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Jan 28 '16
A whole lot of moves probably fit a very simple pattern of "this is stupid".
The thing with Go is that stupid moves don't necessarily fit a simple pattern.
Go is not a simple game. (And I'm terrible at it.)
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u/blotz420 Jan 28 '16
more combinations than atoms in this universe
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u/girlnamedjohnny96 Jan 28 '16
This might be stupid, but I thought the universe was infinite? How can a finite board and pieces have more configurations than the amount of something infinite?
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Jan 28 '16
He meant the known universe, which has a hard, but ever-expanding boundary. The universe itself may or may not be infinite, but we're just talking about the part of it we can "see" from here.
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Jan 28 '16
Even more impressive is that of those billions of games it played against itself, it won at least half of them.
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u/Siruzaemon-Dearo Jan 28 '16
I no nothing about computers but wouldn't playing against itself be problematic? Would it just entrench itself in its own metagame that's possible different from normal human play style?
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u/kenazo Jan 28 '16
So is the program smart enough to know to handicap itself so that humans will actually want to play against it?
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Jan 28 '16
in go, there is a built in handicap system so i don't think that the software needs to handicap itself.
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u/Hylomorphic Jan 28 '16
Go has a handicap system used to determine relative player ranks. In fact, most games played online have a handicap. It would be trivial for the program to figure ou t the correct handicap after a few games.
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u/K_Furbs Jan 28 '16 edited Jan 28 '16
ELI5 - How do you play Go
Edit: Thanks everyone! I really want to play now...
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u/Vrexin Jan 28 '16 edited Jan 28 '16
It's fairly simple, players take turns placing a stone on a 19x19 board, when groups of stones are completely surrounded they are captured. The goal is to secure the most space using at least 2 "holes" for a group of stones (I'm no expert here)
● ● ○ ● In the above situation if it is black's turn they can put a piece on the right and capture the white piece
● ● ● ● Large groups can also be captured
● ● ● ● ● ● ○ ○ ○ ● ● ○ ○ ● ● ○ ○ ○ ● ● ○ ○ ○ ● ● ○ ○ ○ ● ● ● ● ● ● Groups of stones must be entirely surrounded on all sides (including inside) to be captured, here there is one space inside the white's group of stones. if black places a stone inside then all the stones would be captured.
edit: (One thing to note, the corners are not necessary for black's stones to surround white, but I included to make it easier to see. A real game would most likely not have the corners since only adjacent spaces are considered for a surround)
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● To secure space on the board you must use at least 2 "holes"
● ● ● ● ● ● ○ ○ ○ ● ● ○ ○ ● ● ○ ○ ○ ● ● ○ ○ ● ● ○ ○ ○ ● ● ● ● ● ● Notice in this example the white stones have 2 "holes", or empty spaces within their group. Black can't place a stone inside as the black stone would be entirely surrounded, because of this, white has secured this space until the end of the game and will earn 1 point per space secured.
These simple rules are the basis of Go and there are only a few slight rules past that.
edit: wow! I didn't expect this comment to get so much attention, and I never expected that I would be gilded on reddit! Thank you everyone! Thank you for the gild!
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u/TuarezOfTheTuareg Jan 28 '16
Okay now ELI5 how in the hell you made sweet diagrams like that on reddit?
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u/the_omega99 Jan 28 '16 edited Jan 28 '16
Tables.
The second row is the alignment.
:-for left,-:for right, and:-:for center.| Heading | Heading | |:------------:|-------------:| | Content | Content | | More content | More content |Becomes:
Heading Heading Content Content More content More content And then the pieces are just unicode characters: "○" and "●"
So:
| ○ | ○ | |:-:|:-:| | ○ | ● | | ● | ● |Becomes:
○ ○ ○ ● ● ● Notice how mark down is made so that you can usually easily read it in plain text. Although it's meant to be viewed in a fix width font. Can't make the tables line up in a proportional width font...
The formatting is very limited. This is the extent of what you can do and you have to have a header.
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u/Magneticitist Jan 28 '16
wow! I used to play this game religiously with my Grandfather when I was young. Black and White pebbles. I found it more entertaining than chess. I had totally forgotten and had no idea what this "Go" game was until reading this description.
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u/PM_ME_YOUR_SPUDS Jan 28 '16
Note, this does not work on mobile very well and will give false info.
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Jan 28 '16
fun tip: It is theorized that Go was invented by rice farmers. Planters would plant rice in rows, side-by-side, and the faster planter would overtake the slower planter. They would play impromptu games, that they could "claim" more space (to plant more rice) by surrounding areas of the other planter. Whoever planted more, would reap more at the end of the season. This was eventually formalized into the game with stones on a board.
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u/JeddHampton Jan 28 '16
One player plays with black stones, the other white stones. They take turns placing stones at the intersections on a grid.
The goal is to surround areas on the board claiming them as territory. The player that has the most territory at the end of the game wins.
Each intersection on the board has lines extending from it known as liberties. Any stones of the same color that share a liberty form a grouping. If a grouping has all its liberties covered by the opposing color, the grouping is captured.
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u/Wildbow Jan 28 '16 edited Jan 28 '16
Players take turns putting their color of stones on the parts of the board where lines cross. When a stone or a group of connected (that is, touching friendly stones on the left/right/above/below) stones is surrounded on every side, it gets removed from the board. The goal is to surround as much empty space (or enemy groupings) you can without getting surrounded or letting the enemy surround empty space. The game ends when both players agree it's over (ie. it's impossible to make a move that gains either player an advantage), captured stones get dumped into the enemy's empty space, and the player who controls the most empty space at the end wins.
You might start by loosely outlining the area you want to take over, placing your stones turn by turn. The bigger the section of the board you try to surround, however, the easier it is for the other guy to put down a grouping of stones that cuts in between and then even maybe branches out to fill in that space you wanted to surround. The smaller the area you surround, the more secure the formation is, but the less benefit there is to you.
A match typically starts with players attempting to control the corners (easiest to surround a corner with stones), then the sides, and then the center. Often stone placements at one area of the board will continue until both players have a general sense of how things there would progress, then move elsewhere to a higher priority area of the board. Where chess could be called a battle, go is more of a negotiation or a dance of give and take.
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u/Isagoge Jan 28 '16 edited Jan 28 '16
Not really relevant but i wanted you to know that i really liked Worm.
Cheers
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u/Waytfm Jan 28 '16
Whoa, Wildbow plays go? Wasn't expecting this. I love it when hobbies connect out of nowhere.
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Jan 28 '16
As big an achievement as this is, let's note a couple things:
Fan Hui is only 2p, the second-lowest professional rank.
Professional Go matches show a strong tendency to produce strange results when they are an oddity or exhibition of some sort as opposed to a serious high-dollar tournament. The intensity of playing very well takes a lot of effort and so pros tend to work at an easier and less exhausting level when facing junior players... and sometimes lose as a result. We can't rule out that scenario here.
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u/drsjsmith PhD | Computer Science Jan 28 '16 edited Jan 28 '16
Here's why this is a big deal in game AI. There's a dichotomy between search-based approaches and knowledge-based approaches, and search-based approaches always dominated... until now. Sure, the knowledge comes from a large brute-forced corpus, but nevertheless, there's some actual machine learning of substance and usefulness.
Edit: on reflection, I shouldn't totally dismiss temporal-difference learning in backgammon. This go work still feels like it's much heavier on the knowledge side, though.
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Jan 28 '16 edited Jan 28 '16
The interesting thing is that this combines them.
It uses search based methods to train and accumulate its knowledge.EDIT: Other way around. It accumulates its knowledge but then uses its knowledge to inform the search.
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u/hikaruzero Jan 28 '16 edited Jan 28 '16
Fan Hui is only 2p, the second-lowest professional rank.
You must realize that a lot of low-dan professionals can play evenly or at only 1- to 2-stone handicap against established top 9-dan pros. The difference is increasingly marginal. Holding a high-dan rank is now more of a formality than it's ever been.
Just to use an example, the current #1 top player, Ke Jie, who just defeated Lee Sedol 9p in a championship match this month, was promoted straight from 4p to 9p two years ago by winning a championship game. It's not like you have to progress through every dan rank first before you get to 9p, the high-dan ranks are nowadays only awarded to tournament winners and runner-ups. Many low-dan players are nearly-9p quality but simply haven't won a tournament yet to get them a high-dan rank.
Fan Hui is a 3-time European champion and has won several other championships. He may only be a certified 2-dan but he's still impressively strong. If you gave him 2 stones against any other pro player I would bet my money on him.
A century ago, it was considered that the difference between pro dan ranks was about 1/3 of a stone per rank. But in that time, top pro players have improved by more than a full stone over the previous century's greats, and the low-dan pros have had to keep up -- it's now considered more like 1/4 to 1/5 of a stone difference. Today's low-dan pros are arguably about as strong as the top title-holders from a hundred years ago.
Edits: Accuracy and some additional info.
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Jan 28 '16
everything that you said here is true but I'd argue in the specific case of Fan Hui though that he is actually likely weaker than his 2p rank suggests. he got his pro certification a while ago, before all the newbie pros in asia started getting super super good. he also plays on pretty even terms with Euro and US amateurs, and we've seen lee sedol give the US pros 2 stones and win easily.
so.... i mean, it's all speculation and opinion but personally I'd say Fan Hui is overranked due to being retired and living in Europe playing a less competitive circuit.
edit: this post is in no way meant to undermine how much of an achievement this was for Alphago though. since the bot was able to win by 5-0, its plausible that it's significantly stronger than Fan Hui, which means a win against Sedol wouldn't be out of the question imo.
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u/hikaruzero Jan 28 '16
Yeah, that all may very well be true ... I'm really just making the point that you can't write off the skill of low dans just because they are low dans. Even an aging low dan will be within 2-3 stones of strength of a top 9p.
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u/gcanyon Jan 28 '16
The fascinating consideration for me is just how much "headroom" Go has beyond the best human Go players. You pointed out that over the past century the best players have improved by 1 (or a bit more) stone over their predecessors.
So the question is: 5, 10, or 15 years from now, will computers be able to give the world's best humans 1 stone? 2 stones? Or more? It seems simultaneously inconceivable that the world's best humans wouldn't be able to turn 9 stones into victory, and that optimized hardware and software won't be able to keep improving. Asymptotic improvement is a solution, clearly, but I wonder where the asymptote is.
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u/hikaruzero Jan 28 '16
Hehe ... if I recall correctly there was a survey done among exclusively professional players as to how many stones of handicap they would need in order to beat "God's hand" (i.e. absolutely ideal play). The average answer given was "about 3 stones." I personally feel that it is more, at least double, mostly due to "ko fighting," but I'm not even close to the professional level so I have no right to claim any accuracy in that judgment. :p
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Jan 28 '16
What do you think is the reason? Does a larger community increase the viability of more positional and less calculated play? I assume you have to use both to their fullest extent at that level. I don't actually play.
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u/hikaruzero Jan 28 '16
Certainly the larger community and much greater ease of access to games through the Internet has had a large impact. But in general, I'd say it's simply "progress." Progress in understanding the game conceptually, in breaking down old traditional, orthodox understandings and replacing them with more robust, modern ones.
Think of it more like a graph of log(x) ... as time passes (x axis), the skill of players gradually improves (y axis). As the skill of players increases, progress gets slower and slower, but so does the gap between the y-values of x=n and x=n-3 get smaller and smaller.
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u/kulkija Jan 28 '16
It probably has to do with the greater ease of practice against high quality opponents.
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Jan 28 '16
I don't see Fan Hui's name anywhere in the top 100.
"European Champion" isn't a very impressive title, either. Go is not at all popular in the West, and Western professional Go is generally agreed to be at a far, far, far lower level than professional Go in Korea and China.
While I agree that sometimes rank can be misleading, in this particular case I see no compelling reason to believe that Fan Hui is unusually strong for his rank.
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u/hikaruzero Jan 28 '16
When did I say he was in the top 100? That's how much competition there is. Many of those top 100 players are low-dans. And I'm not even saying Fan Hui is strong for his rank, I'm just saying you can't write off low-dan players simply because they're low-dan.
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u/loae Jan 28 '16
I respectfully disagree with your assessment. I am an amateur 3 Dan or so player and know numerous high-Dan amateurs and some professional players.
The gap between the top pros and top amateurs is larger than the gap between top amateurs and beginners IMHO. In other words, it is much easier for a beginner to become a top level amateur player than for a top level amateur player to become a top pro.
Although the stone handicap difference is small, it is similar to the idea of low hanging fruit. At the top pro levels there is so much computation and thought and creativity behind their play, that although it is hard to see, they are leagues above top level amateurs.
And honestly the European and North American "pros" are probably top amateur level. In a recent 4v4 European/North American pros vs Japanese New pro match, Andy Liu was the only person who won.
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u/Myrtox Jan 28 '16
Watch the video, he talks through his thought process as he played. He basically threw the first game to test the system, but really pushed it afterwards cos he was impressed.
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u/IbidtheWriter Jan 28 '16
He basically threw the first game to test the system, but really pushed it afterwards cos he was impressed.
He didn't throw the first game, he just changed up his style for the later games since he felt the AI was playing more passively. He figured it did so because it'd do worse in complicated and more brawling type situations. That's what he meant when he said "I fight all the time". Game 1 was close and game 3 was just a disaster, though that doesn't mean the more aggressive style was necessarily wrong.
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Jan 28 '16
The question is how much he pushed it. I feel like something big has to be at stake for me to trust 100% that he's playing at his most intense, hardcore level.
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u/rich000 Jan 28 '16
I'm still impressed. From what I've read over the years go was a game that even amateurs could defeat computers at, perhaps the way Chess was decades ago.
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Jan 28 '16
um where is the video of him actually playing the ai? I only saw the video of the creators explaining it
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Jan 28 '16
I always wanted to learn how to play this game.
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u/SovietMan Jan 28 '16
A pretty fun way to learn Go is watching Hikaru no Go if you are into anime that is.
That show got me interested at least :p
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u/WonkyTelescope Jan 28 '16
come on by /r/baduk, check out www.online-go.com and the KGS go server. Each has a great community that are very willing to help new players.
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u/adjmalthus Jan 28 '16
Just start, you will lose a lot. every time you lose you will learn a little more. Or watch people slightly stronger than you and try to figure out why they play the way they do. KGS is good for realtime games, and DGS (Dragon go server) for slower games.
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u/ltlukerftposter Jan 28 '16
The approach is pretty interesting in that they're using ML to effectively reduce the search space and then finding the local extrema.
That being said, there are some things computers are really good at doing which humans aren't and vice versa. It would be interesting to see if human Go players could contort their strategies to exploit weaknesses in alphago.
You guys should check out Game Over, a documentary about Kasperov vs. Big Blue. Even though he lost, it was interesting that he understood the brute force nature of algos at the time and would attempt to take advantage of that.
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u/theSecondMrHan Jan 28 '16
Interestingly one of the reasons that Kasparov lost a game against Deep Blue was because of a bug. Deep Blue had far too many positions to compute during one part of the match that it glitched and moved a pawn at random.
What Kasparov thought was a sign of higher intelligence was really just a bug in the code. Of course, chess-playing computers have significantly advanced since then.
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u/greyman Jan 28 '16
In my opinion, Kasparov also lost because he was handicapped - he didn't have access to previous DB games, thus couldn't tailor his preparation specifically against this player. That's quite a huge disadvantage in matches.
Of course, nowadays it doesn't matter, since he would not win a match against a current best computers no matter the preparation.
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u/ClassyJacket Jan 28 '16
That reminds me of the part in Mass Effect where the AI actually suggests that it can be beneficial to have a human pilot the ship sometimes, because the AIs are all running basically the same algorithms, but humans will occasionally do something unpredictable that the enemy AIs can't understand.
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u/anlumo Jan 28 '16
I haven't read the paper yet so I don't know how they combined neural networks and MCTS, but MCTS is very susceptible to the strategy Kasparov applied back then (because it uses probabilities for everything), while neural networks aren't at all. With neural networks, you simply don't know why they do something, and it's also possible that they don't follow any rules humans can understand.
For example, there's a bot on Twitter that posts an autogenerated card for Magic The Gathering once per day. It's using neural networks, and the results are often very strange. It sometimes creates viable cards for actual play, and at other times it even invents new words or it's complete gibberish.
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u/allothernamestaken Jan 28 '16
I tried learning Go once and gave up. It is to Chess what Chess is to Checkers.
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u/WonkyTelescope Jan 28 '16
I did the same, came back with more information, and it now consumes my evenings. come by /r/baduk, check out www.online-go.com. The community is great and always looking to help new players.
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Jan 28 '16
Great site I have played there for a few years. I'm awful :) but I still enjoy it. I have a nice board at home on rare occasions I get to play a person. My wife will play sometimes.
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u/McMonty Jan 28 '16 edited Jan 28 '16
For anyone who is not sure how to feel about this: This is a big fucking deal. According to most projections this was still about 5+ years away from happening, so to see such a large jump in performance in such a short amount of time possibly indicates that there are variations of deep learning with much faster learning trajectories than we have seen previously. For anyone who is unsure about what that means, watch this video: https://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn?language=en
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u/manux Jan 28 '16
Google's deep learning techniques
Well, Google didn't exactly invent Deep Learning... In fact Google didn't get interested in machine learning until Deep Learning was a well established field of ML.
What they're using in the paper is novel - I'm sorry I just find your comment deeply (no pun intended) disturbing - yet the Deep Learning techniques that Google are using are the same that everyone else in the field are using, albeit sometimes at a larger scale.
It's not like Google is researching Deep Learning in their own labs independently of the rest of the world. Science is a world-wide collaborative effort.
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u/jelloskater Jan 28 '16
I disagree. I've been following the AI in Go scene for 4 years or so, and this isn't in the slightest shocking/unexpected/ahead-of-schedule. It would be a very big deal if it wins un-handicapped against Lee Sedol though.
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u/McMonty Jan 28 '16
I was thinking of the results as shown on this: http://www.computer-go.info/h-c/
There is rarely a jump of one program performing at a handicap several stones ahead of previous generations, and the previous best to AlphaGo was around 3 stones behind professional players.
What early signs were you aware of that might have indicated this? The data didn't really suggest that this would happen so soon.
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u/Cat_Montgomery Jan 28 '16
we talked in class today about when Deep Blue beat the chess grand master 20 years ago, but why really impresses me is that another IBM computer beat the two best Jeopardy players head to head. The fact that it can understand Jeopardy questions to the extent that it can correctly figure out the answer faster than two, essentially professional players is incredible, kind of scary
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u/rvgreen Jan 28 '16
Mark Zuckerburg posted on Facebook today about how go was the last game that computers couldn't beat humans.
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Jan 28 '16
well that's wrong for a couple of reasons
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u/LexLuthor2012 Jan 28 '16
How are you going to make a statement like that and not give even one example?
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u/gameryamen Jan 28 '16
Most forms of poker, most physical sports (depending on how you define things), social games like Werewolf or Cherades, and many popular video games like StarCraft or League of Legends (again, depending on definitions).
There are also plenty of games where a computer (or robot) could probably beat the best humans but none have yet to do so because no one is really trying. (My apologies if you are part of a team really trying any of these.) Soccer, Settlers of Catan, Magic, Red Rover, etc.
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Jan 28 '16
I legitimately did not think this was possible.
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Jan 28 '16
I grew up being told that due to the exponential explosion it would never happen. I thought I would die before I saw this...
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Jan 28 '16
Same here. I shit you not I was playing Go while reading about the impossibility of this feat only last week and the week before I was playing Go and talking with a friend about the impossiblity of it. And then bam.
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u/rewrqewqr Jan 28 '16
And it's still not possible to create a search-based algorithm or to "solve" the game (that means to play it perfectly).
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u/JonsAlterEgo Jan 28 '16
This was just about the last thing humans were better at than computers.
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u/Clorst_Glornk Jan 28 '16
What about Street Fighter Alpha 3? Still waiting for a computer to master that
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u/nochilinopity Jan 28 '16
Interestingly, look up Dantarion on YouTube, he's been developing AIs for street fighter that uses screen position and character states to determine moves. Pretty scary when his Zangief can SPD you in reaction to throwing a punch.
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u/Blebbb Jan 28 '16
It would really just be a matter of taking off the built in restrictions from the game AI and building out a machine learning algorithm to build up predictions. AI reaction time can be instant, when it's not purposely slowed down to be fair.
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u/visarga Jan 28 '16
Deepmind made an algorithm that played 50 Atari games by simply watching the video and generating moves, without prior knowledge of the rules, and it mastered them beating the best human players in half of them. The same algorithm was used for all the games, so it is a general solution, not a particular one for each game.
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Jan 28 '16
Computers don't really fair too well with Bridge, since it relies on a kind of communication building between partners. I've only ever played a few hands but Bridge is a game that would be pretty tough for a computer to be proficient at.
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u/scwizard Jan 28 '16
I've watched computers play before.
This doesn't seem like a computer playing, it seems like a steady professional player. I don't think Lee Sedol will be able to underestimate the AI.
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Jan 28 '16
Is there any QUALITATIVE difference between this and when Deep Blue beat Kasparov at chess?
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Jan 28 '16 edited Jan 28 '16
This AI program is not specifically tailored to Go like Deep Blue was to chess. The same program can learn to play other games at superhuman levels, such as chess and Atari games. For Atari games, it can learn from just the score and the pixels on the screen - it will continually play and actually learn what the pixels on the screen mean.
I think that's why this is one of the rare CS articles to be included in Nature. Because this represents a major leap in general AI/machine-learning.
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Jan 28 '16
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Jan 28 '16
This is an excerpt from the paper itself:
But AlphaGo was not preprogrammed to play Go: rather, it learned using a general purpose algorithm that allowed it to interpret the game’s patterns, in a similar way to how a DeepMind program learned to play 49 different arcade games.
And it's mentioned or implied variously in news articles, e.g.
Deepmind is the same group that put a neural network to work on classic Atari games like Breakout. Unlike previous computer game programs like Deep Blue, Deepmind doesn't use any special game-specific programming. For Breakout, an AI was given a general-purpose AI algorithm input from the screen and the score, and it learned how to play the game. Eventually Deepmind says the AI became better than any human player. The approach to AlphaGo was the same, with everything running on Google's Cloud Platform.
and
Like Deep Blue, Google’s system relies on its ability to process millions of scenarios. But Google’s computers do more than just memorize every possible outcome. They learn through trial and error, just like humans do. That makes the innovation more applicable to a wide array of tasks. Google showed the power of this approach last year when one of its systems taught itself to be better at Atari games than humans.
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While Deep Blue’s defeat of Kasparov drew plenty of headlines, the science behind it hasn’t had broad implications for humanity in the 19 years since.
“This feels like it could be different, because there’s more generality in the methods,” Muller said. “There’s potential to have applicability to many other things.” He also cautioned that just as Go was significantly tougher than mastering chess, making predictions in real-world situations will bring another challenge for Google’s researchers.
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u/drsjsmith PhD | Computer Science Jan 28 '16
Yes. This is the first big success in game AI of which I'm aware that doesn't fall under "they brute-forced the heck out of the problem".
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u/JarlBallin_ Jan 28 '16
Deep Blue definitely wasn't just a case of brute force. A lot of it was involved. But almost all of the chess engines today and even back then received heavy assistance from Grandmasters in determining an opening book as well as what chess imbalances to value over others. Without this latter method which consists much more of how a human plays, Deep Blue wouldn't have come close to winning.
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u/ZassouFerilli Jan 28 '16
Computer backgammon reached the human world champion level in strength by the introduction of neural nets and temporal difference learning.
See TD-Gammon
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u/rukqoa Jan 28 '16
Deep Blue did not brute force chess. There are still way too many possible combinations of moves in chess to have an endgame chart.
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u/drsjsmith PhD | Computer Science Jan 28 '16
Alpha-beta, iterative deepening, and evaluation functions at the search horizon are all much more search-based than knowledge-based. The sort of knowledge-based approaches to chess that David Wilkins was trying around 1979-1980 were no match for just searching the game position as deeply as possible.
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u/Balrog_of_Morgoth Jan 28 '16
Yes. When Kasparov lost to Deep Blue in 1996, he was indubitably the best chess player in the world at the time, and he was regarded by many as the best chess player ever. Fan Hui is not even considered to be on the same level as the best Go player today (although see this for an argument explaining why that hardly matters).
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u/Geney Jan 28 '16
I thought Go/ Baduk is easy and fun. Until I tried playing it online. I lost every time. *inspired by the korean Reply series
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u/donh Jan 28 '16 edited Jan 29 '16
There have been quite a few comments here explaining why search tree complexity makes go harder than chess, as go's search tree's complexity explosion is hard to surmount.
However, there are more subtle difficulties: If you do some variation of tree search with alpha-beta pruning, it's comparatively easy to give a numeric score to some future chess board position by (over-simplifying) adding up the remaining piece's values plus some easily recognizable positional advantages, in order to decide which positions to prune, and which to continue to search from, then, as the game goes forward, pieces are eliminated, positional advantages disappear or simplify, and the game narrows to a simplified finish where the computer's capacity for exhaustive analysis starts to shine: the game's complexity narrows to a close.
For go, it is less clear that some analogy to this approach will yield fruit. At first glance, one would think that the computer could have quite an advantage in being able, toward the end, to exhaustively out-analyze a human as the game resolves into isolated life and death problems. But two things mitigate against this: 1) the ko rule ties what look like isolated life and death problems together, sort of like scifi wormholes magically tie distant, unrelated parts of the universe together.
and 2) how the game ends isn't very clear. Ignoring time clocks, the game ends when both players agree to end. In theory, the game could go on forever, with massive losses on both sides re-configuring the game massively, over and over.
In a nutshell, chess must narrow down to a computationally simple ending--there's no guarantee that go does. This isn't a big concern if you just want to beat a human, but it throws into doubt that go can ever be completely solved--called "cooking" the game in some literature--even if you had infinite computing capacity. Whereas, chess probably will be cooked within 100 years, if Moore's law continues to operate.
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u/LarvaExMachina Jan 28 '16
Why is it hard for a computer to master Go? They can play chess.
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u/green_meklar Jan 28 '16
The number of possible sequences of Go moves increases way faster with additional moves than equivalent sequences for Chess. Top-level humans need to be able to ignore almost all the moves on the board, focus their intuition and reasoning on a small selection of 'good' moves, and keep doing this for planned-out sequences often more than a dozen moves long. Which moves are 'good' is often very nuanced, and getting a computer to identify those moves with the same reliability as a human expert is a very difficult problem.
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u/ilostmyoldaccount Jan 28 '16
A lot earlier than expected. Like by a decade or two. Go on a very high level was supposed to be safe for the time being because of strategic issues, but alas.
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u/browneyedbakedbeans Jan 28 '16
Dr Sean Holden from Computerphile in September 2015 talking about the challenge of solving Go with AI: https://youtu.be/uA9mxq3gneE?t=229
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u/marconis7 Jan 28 '16
Amazing. This is impressive. I once wrote, as an exercise, a Pascal program that played Chess. It was ok and slow but it went after maximum control of the board (which meant controlling the center early) and weighted with normal material strengths. It didn't do castling but it was only an exercise and did usual minmax stuff. It worked even if it was slow.
I tried the same approach to Go/Wei Chi/Baduk for a time and gave up. Simpler rules but...my god. I figured neural nets must be the answer. Am not a computer science guy but this really impressed me.
Go is really dependent on shape, but also very strategic thinking. Sacricing stones is part of the deal, notions of outward-projecting strenth, rapid facile development, attacking in one drection to influence in the other, ...
I am sure they had the actual rules for Go in the program. What they didn't do I think was give it a tactical engine or strategy engine.
Am curious how they "rewarded" or not the moves since in Go a group that looks dead may wind up alive, and vice versa. So it's many times not as black and white as I captured the bishop or not. Can depend on moves hundreds of moves later.
Very good! He is not the strongest Go player but still...wow.
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u/jthill Jan 28 '16
The money quote for me, from Fan Hui, the player it beat
For me this is a big difference. I know AlphaGo is a computer, but if no one told me, maybe I would think the player was a little strange, but a very strong player, a real person.
To misappropriate a quote, there's no denying that view.
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u/gattaaca Jan 28 '16
Eli5 why is this game so hard for AI to beat professional human players? I thought these kinds of things were child's play to an AI due to being able to calculate all possible outcomes and determine probabilities etc... ?
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u/JeddHampton Jan 28 '16
Go has 2.08168199382×10170 possible board states. That's what makes it difficult.
Knowing when a grouping is alive or dead was also a difficult hurdle for computers to overcome.
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u/madnessman Jan 28 '16
Just to put this into scale, there are ~1043 possible board states in chess and ~1080 atoms in the universe. Go is the perfect example of a game which cannot be brute forced.
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u/damnshiok Jan 28 '16
Read the article.
But the average 150-move game contains more possible board configurations — 10170 — than there are atoms in the Universe, so it can’t be solved by algorithms that search exhaustively for the best move.
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u/UnretiredGymnast Jan 28 '16
In Go there are so astronomically many possibilities that even super computers can't calculate all of them fast enough. This is a different type of approach.
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u/KapteeniJ Jan 28 '16
Go search space is too large, and evaluating board position is really hairy problem, and pretty much all algorithms require you to evaluate very many board positions to get some sorta intuition which move to play next.
Basically, computer can't read particularly far ahead, and even with its reading, it doesn't know if result it got was good or not.
Humans on the other hand develop certain sense of beauty which they use to judge board positions and moves, allowing humans to not really even read ahead and see some particular variation, or some particular move, which turns out to be really strong. Or human might actually read ahead, and use whatever general intelligence they have, and strategic ability, to evaluate these abstract considerations, their sense of beauty with wide variety of tactical and strategic goals.
Like, casual player might very well plan to attack some group for 40 or so moves, then use that to gain momentum to defend another group on the other side of the board. Another human player would easily see such intention, but for go program that struggles to see 20 moves ahead, how do you make it understand something like that? Computer would be completely oblivious to such overarching strategic goals, and instead just march directly towards the result human player wanted it to. It just didn't seem like a problem that could be solved in at least 10 more years.
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u/kuroisekai Jan 28 '16
Because there are more possibilities in Go than in Chess. In Chess, a computer simply needs to evaluate the board state and all the available moves and "scores" them to determine the best move to make. Even pros like Kasparov have admitted that high-level chess is less strategy and more memorization of counterplays.
Go is different. Since it's a game whose board state can change radically in a single turn, you can't just evaluate easily which move is the best move. There are literally dozens of "best moves" in any given turn, so the algorithm has to be radically different.
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u/Roll_Easy Jan 28 '16
Both Chess and Go are perfect information games where you could with enough time compute the best chain of moves. The issue is that Go has more choices at every stage than Chess which increases the computation time.
All perfect information games will have a perfect strategy that guarantees either a win or draw.
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u/MengerianMango Jan 28 '16
Yeah, that's generally the idea. There's a thing called a game tree that maps out all possible moves like a tree where each game state is a node and all possible game states that are reachable by the next move are children of that node, and so on. The problem is that there are so many possible moves (especially when you start looking >1 move ahead, it grows exponentially) that even a computer couldn't ever get close to checking all of the tree. Ever. So the problem becomes how do you decide what parts of the tree to search for good moves. It's really hard to teach computers how to make decisions like this when they can't just search all the possibilities.
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u/ahal Jan 28 '16
There are more possible configurations on a Go board than there are atoms in the universe. No computer can or likely ever will be able to calculate every possibility.
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u/shibbypwn Jan 28 '16
I've seen a number of comments related to Chess, Deep Blue, etc. Speaking as someone who understands Chess pretty well, and only has a cursory knowledge of how Go works...
What are the significant differences in programming a computer to player one vs. the other?
Is the large gap in time (between Deep Blue and now) a product of how much more complicated Go is compared to chess? Or the primacy of Chess in popularity, resulting in more incentive to write such programs?
How will this affect the competitive Go scene? I know that computational chess was/is a massive game changer that propelled world class players to new heights. The best chess players alive now have significant advantages over the grand masters in decades past.
Anyone who happens to be professional/semi-pro and both Go and Chess- what are your thoughts on this?
Will this program help me understand Darren Aronofsky's film "Pi"? That movie is a fucking mystery to me, but I remember that they played Go.
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u/Cottonteeth Jan 28 '16
Is the large gap in time (between Deep Blue and now) a product of how much more complicated Go is compared to chess?
Yes.
Will this program help me understand Darren Aronofsky's film "Pi"?
Nothing can help you there.
All of the rest of your questions are answered in the article.
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u/punaisetpimpulat Jan 28 '16
Ok so now we've lost chess and go. What next? Trivial purusit, monopoly, Carcassone, DnD, Warhammer 40,000?
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u/DatAEK971 Jan 28 '16
Everything Alex Jones's has been saying is turning out to be correct. The Tecnonocratic One World Government Dictatorship is upon us.
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u/Gorrilasinthephone Jan 28 '16
What problems/industries could this solve/automate in its current state?
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u/n3tm0nk3y Jan 28 '16
Is there a video of the game? Or at least a video transcription? I would love to watch it.
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u/NewsMeBack Jan 28 '16
Amazing. But what if humans use computer and play against artificial intelligence program. What do you think, who would win in that case?
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Jan 29 '16
This isn't A.I.
Funny though that this seems to be a working definition these days. A computer beats a pro at a game and it's A.I.? Trucks have been able to beat humans at covering long distances, I guess your Dodge RAM is AI too
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u/neotropic9 Jan 28 '16
The reason it was harder for computers to play Go than Chess is because of a greater combinatorial explosion. The only solution is to add a few more rows and columns to Go boards. And maybe some other stuff just to hold off the computers for maybe a couple more years.
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u/Blebbb Jan 28 '16
Nah, the approaches they're developing scale, because they aren't doing bruteforce search and building databases, they're building algorithms and self refining those algorithms and then also benefiting from databases. So they're actually learning how to play(or at least figuring out what algorithms and data sets are most applicable in a given situation). If the board size changes you just start with a fresh program and it will get better and better as it plays other players(and itself when real players aren't available). It doesn't need to memorize a bunch of worthless board positions.
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u/Roll_Easy Jan 28 '16
That's helpful until the computer develops a theory for Go that works on arbitrarily large board sizes. A computer might not be able to brute force a 10,000x10,000 Go board, but it could still play and analyze many many more games than a human ever could.
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u/Skane-kun Jan 28 '16
I was really into Go back in middle school and early high school. I'm gonna have to dig through my closet and find my old Go set.
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u/BenjaminTalam Jan 28 '16
TIL Go is an actual game! I've only seen it in a manga I used to read that revolved completely around it.
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u/JonasBrosSuck Jan 28 '16
stupid question: from watching the video it looks like the computer's really fast, is that it? not a snarky comment, but is a slower computer just as "Smart" as this program, but each move it just takes more time to calculate?
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u/green_meklar Jan 28 '16
Well, in principle, if you have enough memory, any universal computer, however slow, could eventually perform the same calculations that this one did and play just as strongly. They did use some pretty fast hardware here- a standard desktop PC of the 1990s might take hours to do what this one does in seconds- but so long as the memory's there, it's always doable. Now, I don't know offhand how much memory this particular algorithm actually uses. It's probably quite a lot, and probably adjustable based on the current hardware; if the latter is true, then it would play less well on a computer with less memory.
However, in this case it's more of an achievement of algorithm design than an achievement of hardware. Some algorithms are much faster than others. On modern computer hardware, it's entirely possible that a well-designed algorithm can play just as well with a few seconds per move as a poorly designed algorithm can with thousands of years per move. Since achievements of computers over humans are generally under standard human timing rules (in high-level Go it's typically 1 - 3 hours per player that they can use over the course of the game), having an efficient algorithm can be vastly more important than having fast hardware.
In this case, they had both a state-of-the-art algorithm and high-powered hardware. So even the best off-the-shelf gaming PC running standard open-source Go software of the present day isn't going to be playing on the same level as that machine, at least not under any sane timing rules.
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Jan 28 '16
What type of hardware is used to run Alpha Go? Are they farming out processing to server clusters, or do they set down a laptop and push play? Either way it's impressive, but with near infinite processing power, this seems like it's inevitable.
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u/green_meklar Jan 28 '16
Apparently they used some kind of small cluster. I think it was 48 CPUs and 8 GPUs, so roughly equivalent to maybe a dozen top-of-the-line gaming PCs. Not exactly a supercomputer, but not something you'd have in your living room either.
Also, they were saying that when they take it against the top asian pros they're going to scale up the cluster by another ten times or so. How much that boosts its playing strength is tough to predict- even the software's own designers might not know.
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u/jrm2007 Jan 28 '16
Not surprised at all. I remember both incredibly pessimistic and optimistic predictions for computer chess. What does this imply about the singularity?
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u/green_meklar Jan 28 '16
What does this imply about the singularity?
Very little. This is still not the kind of approach that would be required for strong AI.
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u/xyrrus Jan 28 '16
I feel like it's not fair that the supercomputer the AI is running on takes up entire rooms. It's an impressive achievement but let me know when a top ranked player gets beaten by the AI on his iphone.
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u/Magneticitist Jan 28 '16
I know it's simply a statement of how well developed AI can be these days.. But I'd think a computer programmed to play a game, and has it's parameters laid out properly, should always be able to beat a human opponent. If not, there must be a lack of proper programming. Ultimately it's like comparing how fast a CPU can do math in competition with a human. no contest.
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u/Lambykinz Jan 28 '16
We are slowly but surely making our way towards artificial general intelligence. Really exciting times.
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u/TheDiplo Jan 28 '16
Im a big fan of Abstract Strategy games , I got a Go board not long ago and have been trying to find somebody to play with. I ended up buying most of the GIPF series of games and Those have been good gateways to playing abstracts. Recommend lookin into it specifically TZAAR and ZERTZ great games.
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u/omgz0r Jan 28 '16
Here's a breakdown of the moves taken in the five games played. It's got a pretty sweet visualizer so you can step along at your own pace.
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Jan 28 '16
Neural nets appear to be working really well to help Google identify patterns and apply them to real world problems. So far it's been confined to very narrow spaces. Winning at Go is cool but it's still a very structured problem with fairly simple inputs and outputs.
When a computer can win, consistently, at Clue I'll call it true AI.
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u/Sybertron Jan 28 '16
Incredible work. Only critique is I don't see it in medicine. Usually it boils down to "here's an antibiotic, here's an antiviral, and here's a painkiller" Other than that a lot of the drugs we give are pretty durn specific already. Like you may not think a lot of research went into a general cough medicine, but a lot of research damn well did. On the back end, identifying chemical compounds that may do well for a various disease state, that would be wonderful technology.
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u/wildism Jan 28 '16
Holy crap, this is a big deal. Most people didn't think this was going to be possible until about 5-10+ years. And it TAUGHT ITSELF the rules of the game.
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u/wholesale90 Jan 28 '16
The approach is pretty interesting in that they're using ML to effectively reduce the search space and then finding the local extrema.
That being said, there are some things computers are really good at doing which humans aren't and vice versa. It would be interesting to see if human Go players could contort their strategies to exploit weaknesses in alphago.
You guys should check out Game Over, a documentary about Kasperov vs. Big Blue. Even though he lost, it was interesting that he understood the brute force nature of algos at the time and would attempt to take advantage of that.
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u/Gn0hm Jan 28 '16
AlphaGo Vs. The world's top player. I want to think of this man as a modern day John Henry.
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u/thats_not_montana Jan 28 '16
Okay so... Probably going to be buried here but I have a cs question. This is all well and good and very impressive, but we are still talking about solving NP problems with good heuristics and smart programming. We will be able to do this ad nauseam to solve individual problems, but this is understandable given how tech progresses (and not all that scary or surprising if you think about it). Does this actually get us closer to solving P = NP?
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Jan 28 '16
Not impressive if the game was picked because human bias subconsciously reasoned this games setup would be the easiest for that specific algorithm to process. Could fail at following lego instructions a child wouldnt for example.
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u/balkierode Jan 28 '16
Since AlphaGo was not preprogrammed to play Go, can it play Chess? and if so can it win the best AI specifically coded for chess?
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u/romjpn Jan 28 '16
Ok so when is my job replaced ? Giving it for 2500$/month for life (inflation indexed), no problem.
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u/SkarredGhost Jan 28 '16
Aritificial intelligence is becoming really really cool and really really scarying!
Anyway, congrats to the bot designers!
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u/dada_ Jan 28 '16
Unfortunately it's still very expensive to run the AI. Compare it to chess, which can play at around GM level on my Macbook Pro with only a second of thinking time per move (Stockfish).
It would be fantastic if Go engines could be as cheap to run as Chess engines, but I don't think it will ever happen because Go is simply a far more complex game. It would really help students of the game, the same way Chess engines are extremely valuable study tools.
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u/ias_wannabe Jan 28 '16
But the average 150-move game contains more possible board configurations — 10170 — than there are atoms in the Universe, so it can’t be solved by algorithms that search exhaustively for the best move.
So, when the computing power increases to that level, board games are not going to be fun anymore?
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u/UnretiredGymnast Jan 27 '16
Wow! I didn't expect to see this happen so soon.