AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them.
I've been giving it some thought. When I was learning to play Go as a teenager in China, I followed a fairly standard, classical learning path. First I learned the rules, then progressively I learn the more abstract theories and tactics. Many of these theories, as I see them now, draw analogies from the physical world, and are used as tools to hide the underlying complexity (chunking), and enable the players to think at a higher level.
For example, we're taught of considering connected stones as one unit, and give this one unit attributes like dead, alive, strong, weak, projecting influence in the surrounding areas. In other words, much like a standalone army unit.
These abstractions all made a lot of sense, and feels natural, and certainly helps game play -- no player can consider the dozens (sometimes over 100) stones all as individuals and come up with a coherent game play. Chunking is such a natural and useful way of thinking.
But watching AlphaGo, I am not sure that's how it thinks of the game. Maybe it simply doesn't do chunking at all, or maybe it does chunking its own way, not influenced by the physical world as we humans invariably do. AlphaGo's moves are sometimes strange, and couldn't be explained by the way humans chunk the game.
It's both exciting and eerie. It's like another intelligent species opening up a new way of looking at the world (at least for this very specific domain). and much to our surprise, it's a new way that's more powerful than ours.
I have been watching Myungwan Kim's commentary for the games - and it seems notable that a few moves he finds very peculiar immediately when they are made, he will later point out to as achieving very good results some 20 moves later. So it also seems quite possible that AlphaGo is actually reading this far ahead, to find those peculiar moves achieve better results than from the more standard approaches.
Whether these constitute a 'new way' or not I think depends highly on whether these kind of moves can fit into some general heuristics useful for considering positions, or whether the ability to make them is limited to intelligence's with extremely high computational power for reading ahead.
This. It's a fairly common feature of any AI that uses some form of tree search/minimax, and the effect is very pronounced in chess. Even the best human players can only think 6-8 plies into the feature versus ~18 for a computer. What we can (could?) do is apply smarter evaluation functions to the board states resulting from candidate plays and stop considering moves that look problematic earlier in the search (game tree pruning). AI tends to use very simple evaluation functions that can be computed quickly. They do so given that 1) it allows for deeper search, and a weak heuristic evaluated far in the future often beats a strong one evaluated a few plies prior and 2) for some games (like Go) it's really hard to codify the "intuitions" that human players speak of.
Because search based AI considers board states __very__ far in the future, the results are often completely counterintuitive in a game with an established theory of play. Those theories are born of humans, for humans.
The introduction of MCTS some years back was the first leap towards a human level Go AI (incidentally, MCTS is more human-like than exhaustive tree search in that it prunes aggressively by making early judgement calls as to what merits further consideration). AlphaGo's use of deep policy and evaluation networks to score the board is very cool, and the next step in that journey. What's interesting to me is that, unlike chess AI, AlphaGo might actually advance the human theory of Go. It's possible that these "strange moves" will lead to some very interesting insights if DeepMind traces them through the eval and policy networks and manages to back out a more general theory of play.
I think that Chess machines play perfectly for the next 8 moves, but don't necessarily sense the importance of a Knight Outpost (which may have relevance 20 moves ahead. A proper Knight Outpost will remain a fork threat for the rest of the game).
It is far easier for a Human to beat a Chess Machine at positional play (ex: a backwards pawn shape will probably be a problem at endgame, 30+ moves from now) than to beat a Chess Machine at tactical play (3 moves from now, I can force a fork between two minor pieces)
Do some reading on Stockfish for example if you doubt the veracity of my statement.
But its just as you say: its weighting parameters and heuristics. When Stockfish recognizes a backwards pawn, it deducts a point value. When Stockfish recognizes "pawn on 6th row", it adds a point value to that pawn.
But that's a heuristic. A trained heuristic using games, but still comes down to what I understand to be a +/- point value (like... +35 centipawns).
In contrast, a chess engine truly knows that if you do X move, it will force a Rook / Minor piece exchange in 8 moves.
When you play positionally vs Stockfish, you're arguing with a heuristic (a heuristic which has been refined over many cycles of machine learning, but a heuristic nonetheless that comes down to "+/- centipawns") . When you play tactically vs Stockfish, it is evaluating positions more than a dozen moves ahead of what is humanly possible.
When you play against Stockfish in endgame tablebase mode, it plays utterly, and provably, perfectly.
Take a pick of what game you want to play against it. IMO, I'd bet on its positional "weakness" (yes, it is still very strong at positional play, but it is the most "heuristical" part of the engine)
My experience is with Chess and Chess AI, but in my experience, the more positional knowledge built into the evaluation function, the better the search performs, even if you have to sacrifice some speed for more thorough evaluation. A significant positional weakness may never be discovered within the search horizon of a chess engine because it may take 50 moves for the weakness to create a material loss, so while it's certainly possible that a deep, but carefully pruned search is being utilized, I suspect that some of the Value Network's evaluation is helping to create some of these seemingly odd moves.
For AlphaGo to recognize a position that doesn't achieve a good result for 20 moves, it would often have to search much deeper than those 20 moves (I'm not sure if you're using the term moves to mean ply or both players moving, but if it takes 20 AlphaGo moves for the advantage to materialize, that would be a minimum 40 ply search) to quiesce the search to the point that material exchanges have stopped (again, this is how chess typically does it, I don't know about Go), so the evaluation at the end of the 20 move sequence is arguably more important than a deep search. The sooner you can recognize that a position is good or bad for you, the more time you have to improve the position.
I fixed this behavior by scoring earlier wins higher than later wins. Now it will actually finish games (and win), but almost invariably its edge is very small, no matter how well or poorly I play. Because of the new win scoring, it willingly sacrifices its own advantage if it means securing a win even one turn earlier. (And since scoring is symmetrical, this has the added advantage of working to delay any win it sees for me, thus increasing the possibility of me making a mistake!)
I suppose I could try modifying the scoring rules again, to weight them by positional advantage. A "show off" mode if you like :) And again, with the flip side of working to create the least humiliating losses for itself.
Humans, I think, have the natural instinct to "hedge" themselves in games like go and chess, by creating positional/material advantages now to offset unknowns later. Of course, that advantage becomes useless in the end game, when all that matters is the binary win/lose.
An AI, which may have a deeper/broader view of the game tree than its human opponent (despite evaluating individual position strength in roughly the same manner), may see less of a need to "hedge" now, and instead spend moves creating more of a guaranteed advantage later (as you suggest). And indeed, my experience with my AI is that during the endgame (in which an AI generally knows with certainty the eventual outcome of each of its moves), it tends to retain the smallest advantage possible to win, preferring instead to spend moves to win sooner.
That's actually an excellent way to win chess games. Keep your eye on the mate while the other person is focusing on position and material.
Absolutely. Also worth noting that it may be simply unable to distinguish between good and bad moves if both outcomes lead to a win, since it has no conception of the margin of victory being important.
So it might not be that it increased win probability, but that both paths led to 100% win probability and it started playing "stupidly" due to lacking a score-maximizing bias.
I'm confused. Why would 'make the winning move' not be the way to maximise probability of winning?
I suppose that, in Hive, it is more likely that a path to a win is longer rather than shorter. Hence, when my AI was arbitrarily choosing "winning" moves, it statistically chose those that drew the game out.
Your post should be required reading in this discussion.
People forget how literal computers are.
Humans play that way too. Everyone wants to maximize the chance of leading by >=1 stone.
The difference is that AlphaGo is better at calculating a precise value of a position, so that when uncertainly plays in, AlphaGo can play for, say, "1-3 stone lead", while a human can only get confidence in "1-7 stone lead", and thus needs to play excessively aggressively to overcome the uncertainty.
That's called programming
if you have fully autonomous robots which can fight your war, you'd be able to launch a massive offensive within hours. properly mobilizing defenses and responding to that invasion would take too long, as any command centers would've already been wiped out by the first attack.
Some examples of 5th line early shoulder hits in recent professional play - these situations are not the same as the one seen in today's game, but something like a 5th line shoulder hit is always going to be highly contextual and creative.
http://ps.waltheri.net/database/game/26929/ (move 23)
http://ps.waltheri.net/database/game/69545/ (move 22)
http://ps.waltheri.net/database/game/71408/ (move 22)
http://ps.waltheri.net/database/game/4663/ (move 9)
The only one I can't parse is the last one. There are a lot of variations where I want to know what black's plan is.
For instance, I developed a system that used machine learning and linear solver models to spit out a series of actions to take in response to some events. The actions were to be acted on by humans who were experts in the field. In fact, they were the ones from whom we inferred the relevant initial heuristics.
Everyday, I would get a support call from one of the users. They'd be like, 'this output is completely wrong. You have a bug in your code.'
I'd then have to spend several hours walking through each of the actions with them and recording the results. In every case, the machine would produce recommended actions that were optimal. However, they were rarely intuitive.
In the end, it took months of this back and forth until the experts began to trust the machine outputs.
This is the frightening thing about AI - not only can an AI outperform experts, but it often makes decisions that are incomprehensible.
Later, he did admit that the "overextension" on the north side of the board was more solid than he originally thought, and called it a good move.
He never explicitly said that a move was "good" or "bad", and always emphasized that as he was talking, his analysis of the game was relatively shallow compared to the players. But in hindsight, whenever he point out an "bad-juju feel" on the part of Lee's move, AlphaGo managed to find a way to attack the position.
Overall, you knew when either player made a good move, because the commentator would stop talking and just stare at the board for minutes, at least until the other commentator (an amateur player) would force a conversation, so that the feed wouldn't be quiet.
The vast, vast majority of the time, the English-speaking 9-dan was predicting the moves of both players, in positions more complicated than I could read. (Oh, but it was obvious both players would move there. There were clearly times when the commentator would veer off into a deep distant conversation with the predicted moves still on the demonstration board, because he KNEW both players were going to play out a sequence of maybe 6 or 7 moves forward).
They really got a world-class commentator on the English live feed. If you got 4 hours to spare, I suggest watching the game live.
> I sense a change in the announcer's attitude towards AlphaGo. Yesterday there were a few strange moves from AlphaGo that were called mistakes; today, similar moves were called "interesting".
If I'm an expert in some domain and a computer is telling me to do something completely different ("Trust me--just drive over the river!") I'm certainly going to question the result.
Could AlphaGO be winning in a way similar to left handed fencers having an advantage over right handers by wrong footing them rather than simply being better? Would giving Lee more chance to see this style give him a chance to catch up?
Think Bruce Lee and the creation of Jeet Kune Do. Before him everyone concentrated on improving one style by following it classically, rather than just thinking of 'how do I defeat someone'.
IMHO Lee is the best at the current style of Go. AlphaGO is the best at playing Go. Maybe humans can devise a better style and defeat AlphaGo, but I'm sure AlphaGo can adapt easily if another style exists.
Ke Jie is an arrogant 18 year old and he's been saying on social network in the past couple days how he will defeat AlphaGo.
Swimming. It used to be that swimmers were supposed to be streamlined and avoid bulky muscles. Then a weightlifter decided he wanted to swim. Swimmers today all lift weights.
Programming. It used to be that people built programs in a very top down, heavily planned way. Think waterfall. We now understand that a highly iterative process is more appropriate in most areas of programming.
Expert systems. It used to be that we would develop expert systems (machine translation, competitive games, etc) through building large sets of explicit rules based on what human experts thought would work. Today we start with simple systems, large data sets, and use a variety of machine learning algorithms to let the program figure out its own rules. (One of the giant turning points there was when Google Translate completely demolished all existing translation software.)
Nowadays, top players slug it out baseline-to-baseline.
In terms of stance, we were taught to hit from a rotated position where your shoulder faces the net, and a normal vector from your chest points to either the left or right side of the court.
Nowadays, it's much more common to hit from an "open" position, where your body is facing the net, not turned. This would have been considered "unprepared" or poor footwork in my day, but it actually allows for greater reach. It does make it more difficult to hit a hard shot, but that's made up for by racquet technology and generally stronger players.
Although it takes a few paragraphs until it gets into the details of "today's power-baseline game."
Which is a curious point. The gripes about early brute force search algorithms (e.g. Deep Blue?) were that they felt unnature.
However, as the searches get more nuanced and finely grained, is there a point at which a fast machine begins doing fast stupid machine things quickly enough to feel smart?
Are there any chess / Go analogs of the Turing test? Or is a computer players always still recognizable at a high level?
A Turing test for game players is an interesting idea, it would be useful for designing game players that are good sparring partners rather than brutes that can whipe the floor with you.
As for JKD, people are drawn in by its oriental esotericism, but there's no evidence it is an especially effective fighting style, or that it has something that (kick)boxing does not.
Remember that AlphaGo has spent months developing its own style and theory of the game in a way that no human has ever seen. Its style is sure to have weaknesses, but humans will have a hard time figuring them out on first sight.
Similarly chess computers do better in some positions than others (they love open tactics!) and one of the games that Kasparov won against Deep Blue he won by playing an extreme anti-silicon style that took advantage of computer weaknesses. However Kasparov didn't have to figure out what that style was because there was a lot of knowledge floating around about how to do that.
Therefore I'd expect that Lee Sedol from a year from now could beat AlphaGo from today. And human Go will improve in general from trying to figure out what AlphaGo has discovered.
However that won't help humans going forward. AlphaGo is not done figuring out the game. At its current rate of improvement, AlphaGo a year from now, running on a single PC, should be able to beat the full distributed version of AlphaGo that is playing today. Now the march of progress is not whether computers can beat professionals. It is going to be how small a computing device can be and still beat the best player in the world.
Weaknesses are only relative to capabilities of the opponent to exploit them. If a tank has a weak spot that rockets can hit, but it's being opposed by humans on horseback, is it really a weakness in that context?
Additionally AlphaGo has the advantage that it started with a database of human play, so it has some ideas what kinds of positions humans miscalculate.
As for your tank vs horseback analogy, that's flawed at the moment. AlphaGo is probably reasonably close in strength to the human facing him. Improved human knowledge could tip the balance.
However in the future it will become an apt analogy. Computers are going to become so good that knowing the relative weaknesses in their style of play may reduce the handicap you need against them, but won't give you a chance of becoming even with them. That happened close to 20 years ago in chess, and is now only a question of time in Go.
Yes. A representation of ladders is among the input features of its neural networks.
Stone colour 3 Player stone / opponent stone / empty
Ones 1 A constant plane filled with 1
Turns since 8 How many turns since a move was played
Liberties 8 Number of liberties (empty adjacent points)
Capture size 8 How many opponent stones would be captured
Self-atari size 8 How many of own stones would be captured
Liberties after move 8 Number of liberties after this move is played
Ladder capture 1 Whether a move at this point is a successful ladder capture
Ladder escape 1 Whether a move at this point is a successful ladder escape
Sensibleness 1 Whether a move is legal and does not fill its own eyes
Zeros 1 A constant plane filled with 0
Player color 1 Whether current player is black
(The number is how many 19x19 planes the feature consists of.)
I could easily see the difference in tournaments with other clubs that were not used to left handed players.
(This also applies without a 100% chance of winning, as long as its chances of winning hover near the highest percent it's able to distinguish.)
Even if it did output an actual 100% chance, AlphaGo would still end up picking moves favored by the policy network, so it would probably just revert to playing like it predicts a human pro would.
It's similar to how ray tracing renderers start to return weird speckle patterns when the room is dark enough.
And the policy network chooses branches to investigate, not which one to choose. It adds sample resolution to places pros might play, but doesn't add to the estimated probability of winning.
Edit: Actually, since places pros might play have higher sample resolution, they're less random. So worse moves get worse evaluation, and a higher chance of leading the pack. This might actually bias AlphaGo to play some pretty bad moves - but, again, this is all assuming it's going to win anyway.
The excellent point you're making applies in general to nearly every type of human thinking.
The way we think about other people, our intuitions about probabilities, our predictions about politics, and so on -- all are based on our peculiarly effective, yet woefully approximate, analogy based reasoning.
It shouldn't be surprising in the least when commonly accepted "expert" heuristics are proved wrong by AIs that actually search the space of possibilities with orders of magnitude more depth than we can. What's surprising -- and I think still a mystery -- is how human heuristics are able to perform so well to begin with.
I'm not a Go player, but I saw this same phenomenon as poker bots have surpassed humans in ability. As with AlphaGo, they make plays that fly in the face of years of "expert" wisdom. Of course, as with any revolutionary thinking, some of the new strategies are "obvious" in hindsight, and experts now use them. Others seem to require the computational precision of a computer to be effective in practice, and so can't be co-opted. That is, we can't extract a new human-compatible "heuristic" from them -- the complexity is just irreducible.
They are peculiarly effective only because of lack of comparison. Humans have been the most intelligent species on this planet for millennia, where no other species come even close. We don't know how ineffective those strategies are seen by a more advanced species. Well, until now.
Of course, the counterpoint could be that it's only the case because humans, with their laughable reasoning abilities, are the ones programming those computers.
AlphaGo can’t decide that it’s bored and go skydiving. Humans aren’t merely capable of playing Go. And when they do it, they can also pace around the table, and drink something, all at the same time, on a ridiculously low energy budget. Or they can decide never to learn Go in the first place but to master an equally difficult other discipline. They continuously decide what out of all of this to do at any given moment.
AlphaGo was built by humans, for a purpose selected by humans, out of algorithms designed by humans. It is not a more advanced species. It’s not even a general intelligence.
Your own original point was much better than the one made in response.
"oh what if the machine suddenly came alive!?" has been done 1000 times. But such concepts like: a computer can detect and act patterns which we cannot, in ways that are almost, if not possibly intelligence, are magnitudes more believable, and therefore, compelling.
Of course, those fools underestimated it. They should have known better...
Pretty much the same thing happened with TD-Gammon with it playing unconventional moves, in the longer term humans ended up adopting some of TD-Gammon's tactics once they understood how they played out, it wouldn't be surprising to see the same happen with Go.
“Top competitors who once relied on particular styles of play are now forced to mix up their strategies, for fear that powerful analysis engines will be used to reveal fatal weaknesses in favoured openings....Anything unusual that you can produce has quadruple, quintuple the value, precisely because your opponent is likely to do the predictable stuff, which is on a computer” 
Anand isn't really talking about strategy here, he's just talking about choice of opening. Players with narrow opening repertoires, like Fischer, have always been easier to prepare for than players who play a wide variety of openings.
As far as actual changes to strategy, the most obvious one is that computers tend to value material more highly than humans. So a computer will take a risky pawn if it looks sound, while a human will see that taking the pawn is very complicated and prefer a simpler move.
(1) Online game databases have made it easier for players to track developments in opening theory and prepare to play specific opponents
(2) Chess engines add to this be used to search for antidotes to complicated opening systems
(3) Young players have greater access to high-quality sparring partners - either engines or fellow humans on online servers.
This has lead to the best players becoming younger, and players playing more varied and less 'sharp' openings.
It uses MCTS, which is unlike minimax. It doesn't use temporal difference learning, although they say that the policy somewhat resembles TD.
That doesn't sound like 'essentially built on', its sounds maybe like 'slightly influenced by'
Tesauro's work on TD-Gammon was pioneering at the high level, i.e. combining reinforcement learning + self-play + neural networks.
Looks like citation 46 is the relevant one here.
Until someone got better weapons and suddenly the "rules" of the battlefield that dictated standing in lines across each other made no sense to follow anymore because the original principles that dictated those rules to be good were not valid anymore.
I think this will the theme of our future interactions with AIs. We simply can't imagine in advance how they will see and interact with the world. There will be many surprises.
It's not like this at all; let's not do this sort of thing. Humans are inveterate myth makers (viz. your description of how people conceive the Go board as army units), and our impositions on the world are easily confused for reality.
In this case, there's no "intelligent species" at work other than humans. We made this, and it is not an intelligence, it is a series of mathematical optimization functions. We have been doing this for decades, and these systems, while sophisticated, are mathematical toys that we have applied. We built and trained this thing to do exactly this.
As a student of AI you know that convolutional neural networks are black boxes and are hard to interpret. A different choice of machine would have yielded more insight about how it is operating (for example, decision trees are easier to interpret). The inscrutability of the system is not a product of its complexity; even a simple neural network is hard to understand.
This, actually, is my primary objection to using CNNs as the basic unit of machine learning - they don't help US learn, they require us to put our faith in machines that are trained to operate in ways that are resistant to inspection. In the future I hope that this research will move more towards models that provide interpretable results, so they ARE actually a tool for improved understanding.
You can say the same about your mind too which is a bunch of optimization nodes. If something is intelligent, does it matter if it's evolved in nature or created by a species who is evolved in nature?
> In the future I hope that this research will move more towards models that provide interpretable results
I think it's not really possible to understand in detail how these networks operate on the level of nodes, because emergent behavior is necessarily more complex than the sum of its parts.
A CNN is a pure mathematical function - if you want, you could write it down that way. Given a set of inputs, it will always produce the same output. We don't call a linear regression model an "intelligence", a CNN is no different.
Of course I agree that humans are built up of billions of tiny machines like this, but let's appreciate the vast difference in scale.
> A CNN is a pure mathematical function
That's their basic property, but who are we to say that our cell based neural network is superior? Cells are just compositions of atoms and they are defined by quantum mechanics, which is... "just" math and information.
I also think that Go might be a great communication tool between AI and humans. If you look at the commentary from this angle if's fun to think about like this.
I think the answer must be in figuring out how to decompose the black box of a CNN - it is, after all, just a set of simple algebraic operations at work, and we should be able to get something out of inspection.
I have to imagine Hinton et al. have done work in this regard, but this is far afield for me, so if it exists I don't know it.
Human intuition and to certain extent, creativity are like this as well.
And this is just the beginning with AlphaGo. As we keep on training Deep Learning systems for other domains, we'll realise how differently they approach problems and solve them. It'll, in turn, help us in adapting these different perspectives and applying them to solve other problems as well.
.. that we'll be probably unable to comprehend ourselves.
Which attempts to visualize machine areas of attention that look like:
Go, unlike Chess, has deep mytho attached to it. Throughout the history of many Asian countries it's seen as the ultimate abstract strategy game that deeply relies on players' intuition, personality, worldview. The best players are not described as "smart", they are described as "wise". I think there is even an ancient story about an entire diplomatic exchange being brokered over a single Go game.
Throughout history, Go has become more than just a board game, it has become a medium where the sagacious ones use to reflect their world views, discuss their philosophy, and communicate their beliefs.
So instead of a logic game, it's almost seen and treated as an art form. And now an AI without emotion, philosophy or personality just comes in and brushes all of that aside and turns Go into a simple game of mathematics. It's a little hard to accept for some people.
Now imagine the winning author of the next Hugo Award turns out to be an AI, how unsettling would that be.
The way it picks moves is very similar to how top professionals do.
Intuition is reduced to memories stored vaguely as neural connections.
So the most likely explanation is that policy/value nets in AlphaGo have learned to extract - with cold logic - the key factors that make up what humans believe to be "good" board positions.
It has little to do with voodoo about neural connections and magic emerging from the weights. AlphaGo has most likely managed to identify the important factors of good board positions (by seeing tons of examples of good and bad moves/positions). It only appears to be magical because these factors are most likely very complex and inter-dependent.
This is supported by the AlphaGo paper - they report that AlphaGo without tree search is about as good as the best tree search programs (amateur pro level). So AlphaGo has taken amateur-pro-level board analysis ability, and combined it with tree search, to achieve top-player performance..
I don't think that's an entirely accurate way of putting it, because a player who reaches that level is also doing a little tree searching. Maybe if you found some human who managed to reach "amateur pro" level by playing purely on snap, instinctual decisions without any logic or exploration of variants at all, yes, then you could say their ability to evaluate positions is as good as AlphaGo's.
But I would guess that you are right anyway that we can deduce that its ability to evaluate a board position really is below that of better professionals, and its huge strength is due to the tree search (which of course involves a second "policy" net to pick moves to explore).
Isn't that pretty much the definition of intuition? Combining a bunch of things in some unknown and nonlinear way to result in a 'feeling' about the situation?
Isn't most human experience an overly mystical interpretation of physics/chemistry?
The point is, the programmers don't quite understand what exactly are those features that the neural net is seeing.
They can see certain stats and high level overview.
But they have no idea what it's thinking.
They know the general pattern of the algorithm. They even explain it.
But the algorithm involves two deep neural networks, and they don't really know what's going on inside them.
One of the developers showed up during the commentary on the second game and talk about this stuff:
Well apparently my question leads to some deep mathematic theories about languages encoded by data. http://cstheory.stackexchange.com/questions/15039/why-can-ma...
We still have chess tournaments, super-star grandmasters and circus freaks (people who can play blindfolded against multiple opponents). And, yes, computers can easily smoke all but elite players.
Why should Go be different?
In chess the top engines are rated hundreds of ELO points above Magnus Carlsen (top human). No top ranked human vs computer match has been publicised in over 5 years because humans are thoroughly trounced. There are cyborg matches which are interesting. Human + Computer vs Human + Computer because gameplay techniques are considered different. Humans still depend more on higher level goal strategy and less on ruthless positional efficiency (which is probably why they get beat midgame).
What is mind boggling is that 6 months ago no go engine was scratching the surface of professional level go. It took the engines getting a 4-5 stone handicap to be competitive at the lowest level of professional levels.
It looks like this one algorithm has blown through the professional ranks in about 3 months. And a 5-0 victory here would be like 2006 vs 1996 (or even 1993) chess in 3 months.
As for Go, I guess I would never had made a long bet against computers. But as recently as just over a decade ago, computers lost to merely competent players and people working on Go programs were pretty much saying that they didn't even know what the path forward looked like. Things improved a lot with Monte Carlo but even that stalled out. Admittedly, I don't follow this area closely, but these wins pretty much came out of nowhere.
(Not saying this is a bad thing. Evolution in games is natural, and I think it's amazing how much innovation is going on right now (particularly enabled by Kickstarter) - you'd think that board game design would have been worked out decades or centuries ago, but in the same way that incandescent bulb development accelerated massively when competition arrived, it feels like game design has got so much better when forced to compete with computer games. If there are other activities that people find more fun than Chess, that's all to the good)
I also played chess as a kid and the allure of both local and national tournaments was that you could play with a multitude of different players, as opposed to the same 4 or 5 habitual chess players in your family/school/circle of friends.
But now, with the internet, at any second you can play with different people from all over the world, different strengths, styles and whatnot.
Hence now, instead of looking for the local chess club in the weekends we can play, any time of the day, any day of the week, anywhere.
Sites like the excellent lichess  are even free (in this case, free both as in beer but also as in speech) and, at any moment there there are 9 thousand, 10 thousand players enjoying this magnificent game.
And yes, chess has lost some reputation. And I guess it will be similar with go. I mean there is a University just for go.
But learning something where you know you can become the best, is something different, than learning something knowing computers will be allways better than you ... so I guess they are having a hard time right now ..
And the fact, that, prior to the deep learning revolution, Go is the only board game that human cannot be beaten, add even more myth and charm to the game and players alike.
Now, it comes to the time, that Go can be modeled by computers, and hundred years of human study is topped by computer in less than a year's time. All those myths around it will be gone. That is the biggest bummer I guess.
And who can make the most interesting new go-like game?
Perhaps this could be tested with chess or checkers, even.
It's an upright denial to the way of life they so chose and devoted.
IMHO Google should donate the prize towards Go education and Go organizations instead of some random charities.
I don't think people will pay to watch Go Bots square off, but I think this example of "obsolete education" is a great reminder that it's not just the assembly line jobs on the chopping block.
So they're doing what you want them to (I can't find a summary of how they're allocating the money across each category). Personally I think the work UNICEF is doing to help women in developing countries is more important than Go charities, but I guess their choices should satisfy everyone.
Because strategies like "ripping out someone's intestines" are illegal, boxing and wrestling have an unfair advantage because they don't have to worry about that stuff to begin with. For more realistic fighting situations, see: https://en.wikipedia.org/wiki/Lei_tai
i believe all these ancient arts are making a big comeback. the roots are still there, they just got concealed over the centures.
If an AI won the next Hugo award, I would be rejoiced. It wouldn't mean the end of literature at all; it would mean that humans are ready to produce an even higher form of literature.
We have all kinds of visual art made by computers and AI - prom painting from photos to abstract art to 3D renders.
We have computers writing poems and haiku.
The only thing that's missing is the conceptual creation, which, let's be honest, most human artists struggle at as well. So writing and interesting story is not yet in the AI's domain.
I'm sure soon (if not now) AI can easily create art that regurgitates popular trends in the past, and perhaps some artists may find a way to use AI / other algorithmic techniques in a way that complements their personal vision. But AI is a long way off from replicating the quirks of human nature, the unique personalities and personal visions of humans. Until that happens, I can't see terribly interesting art emerging from AI alone.
And they were well accepted by the music community.
Literature is not a lower form of art that we must strive to automate so that we can dedicate ourselves to more "complex" forms.
You are confusing the unknown with art.
> If an AI won the next Hugo award, I would be rejoiced. It wouldn't mean the end of literature at all; it would mean that humans are ready to produce an even higher form of literature.
To me this seems to be claiming that what we have now is a form of "lower" literature, to be tackled by AI so that humans can produce "an even higher form of literature". But, of course, literature isn't graded in a scale of "low" to "high". (Well, there is lowbrow and highbrow, but that's something else).
The mention of medicine as "holy art turned into boring science" (already somewhat dubious) also seems to point to the idea that it is art that's being "solved". But I admit I might have misread it.
By the way, I don't rule out that art can be produced by an AI (whatever that means). I subscribe to the notion that art is in the eye of the beholder, so if humans can find meaning in something produced by a non-human, that's probably valid art!
Being "low" or "high" is all dynamic. We already have a good example: the advertisement industry. When a way of advertising your product first came out, it is fresh and captures people eyes. As more and more advertisers follow suit, it became bad ad, and advertisers are forced to find new ways to attract people. Basically the criteria for good ads changes all the time, but that doesn't kill the ads industry.
Now imagine if AIs can write sci-fis that are "good" according to today's criteria. That would mean there will be loads of "good" sci-fis in the market, and people soon get tired of it. Now sci-fi authors have to come up with more creative ways of writing good sci-fis.
So AIs being able to produce literature means more variations and faster iteration in literature style, much like the ads industry today. I don't know whether this is a good or bad thing, but it is certainly far away from the death of literature.
I'm specifically objecting to your notion of art.
The advertisement industry is not a good analogy. It can indeed be improved, possibly by automated means. In contrast, the progression from "good" to "better" art doesn't work like that -- if it even exists at all! What is your measure of quality, anyway? Complexity? But sometimes minimalism is preferred in art. Maybe how many people like it? It doesn't work either; a lot of people like stuff that is not enjoyed by the majority.
When is art "better"? How can it be "improved"?
PS: the Sci-Fi market is already flooded by below-average human writers, so we don't need an AI to picture this nightmare scenario of good SF writers struggling to sell their books :P
Just wait until machines start producing top notch research in experimental fields such as chemistry and Physics...
I've been thinking precisely about that. I think a book written by a machine will make the NYT bestseller list within our lifetimes (I would give it a 75% chance within 10 years, but that's just a gut feeling).
I also think my kids will live long enough to see an animated movie that is conceived of, written, scored, and animated by an AI.
But generating a coherent narrative, and good writing to "implement" that narrative. These are huge problems which - as far as I'm aware - would require major breakthroughs to achieve. Machine translation is still utter garbage, and that's fairly straightforward work. We're nowhere near an AI which actually understands language.
Sure. I think they will be solved in the next 100 years though.
I think that would be pretty awesome and amazing, to be honest.
Imagine the best book you've ever read. Entrancing, enlightening, cathartic. You reach the end, and it's ... perfect. Oh hey, a sequel. Wow, the sequel is just as good as the first book. It expands upon it without diminishing the original -- you feel better, more complete for having read it. Wait, is that a third book in the series? Wow, it's even better than the first two! A fourth -- well, maybe you should go to work now, it's Monday, but the book is so good. Calling in sick once won't hurt anything.
Imagine a perfect series of books, published without end, each better than the last, a new one coming out weekly ... daily ... hourly ...
In the end all life is is one choice after another, and making good ones over bad mostly leads to a happier life.
The practical problem with this is that, as I understand it, the deep learning system needs a pretty large data set to work with to infer rules from. You can do this with go because there is a constraint on legal moves and a deterministic win condition, but given how vast the number of potential novels is (If we count the space of all ten thousand word collections of grammatically acceptable sentences) the existing number of novels may no be enough to infer a pattern. (Though possibly you could split the problem up by separately doing the natural language processing and abstractin out the plot)
Well, I am of the opinion that mathematics is the language that subsumes all other kinds of languages and line of thoughts. In the end we shall be able to describe every idea or thought in purely mathematical form.
That is a very ironically imprecise sentiment.
This can be claimed to be true when we understand how deep neural networks mathematically.
An outsider, new to the game, had managed to pick up and challenge top players successfully in a venerated game.
The reactions of community to this is uncannily similar.
"As a casual player of Go myself, some of the moves that AlphaGo made were crazy. Even one of the 9th Dan analysts said something along the lines of 'That move has never been made in the history of Go, and its brillant.' and 'Professional Go players will be learning and copying that move as a part of the Go canon now'."
One interesting thing that happened during the time for Sedol's next move was that the 9th dan commentator started referring to AlphaGo as "he".
Other than the kake(shoulder hit on the right) the game might have been a regular top-prop game.
While I'm not able to comment on the length/depth of history of chess vs. go, the above statement seems foolish. Chess also has a lot of mythology and mystique attached to it. Champion chess players (perhaps more so a decade or two or three ago) are also treated with a respect that is not casual.
Why do you think AI has no emotion, philosophy, or personality? We too are mere machines. Magnificent machines, no doubt, but machines nonetheless.
Considering some of the recent Hugo winners, it turns out the story doesn't actually have to be good, so yeah, a computer-written story winning is probably closer than we think.
There's 10^50 atoms in the planet Earth. That's a lot.
Let's put a chess board in each of them. We'll count each possible permutation of each of the chess boards as a separate position. That's a lot, right? There's 10^50 atoms, and 10^40 positions in each chess board so that gives us 10^90 total positions.
That's a lot of positions, but we're not quite there yet.
What we do now is we shrink this planet Earth full of chess board atoms down to the size of an atom itself, and make a whole universe out of these atoms.
So each atom in the universe is a planet Earth, and each atom in this planet Earth is a separate chess board. There's 10^80 atoms in the universe, and 10^90 positions in each of these atoms.
That makes 10^170 positions in total, which is the same as a single Go board.
Chess positions: 10^40 (https://en.wikipedia.org/wiki/Shannon_number)
Go positions: 10^170 (https://en.wikipedia.org/wiki/Go_and_mathematics)
Atoms in the universe: 10^80 (https://en.wikipedia.org/wiki/Observable_universe#Matter_con...)
Atoms in the world: 10^50 (http://education.jlab.org/qa/mathatom_05.html)
I wouldn't go with most (because I don't know about that), but many of these boards would also be either impossible to achieve (in a normal game) or illegal.
Are they not? MoGo beat pros of 9 Dan on 9x9 in 2011: https://www.lri.fr/~teytaud/mogo.html
Fuego beat a pro in 2008 using MCTS actually.
Not sure what you meant regarding MCTS, I never said anything about MCTS not being able to beat pros.
See, a chess program needs to find a lot of valid moves (see Deep Blue which won because it had stupid but extremely fast HW move generators), evaluate the moves and do a very deep search, up to 14, out of the very few alternatives. Russian chess programmers were better those times. They came up with AVL trees e.g. But hardware won.
In Go it's completely different. A move generator makes no sense at all, and a depth search of 14 neither. There are not a few alternatives, there are too many.
What you need is a good overall pattern matching of areas of interest and an evaluation of those areas. And we saw that this feature outplayed Lee Sedol. Sedol couldn't quite follow in the recalculation of the areas.
Same as in chess AlphaGo learned the easy thing, that the center is more important than the corners, something Lee forgot during the game.
But it's not a deep search, it's a very broad search, and very complicated evaluation function. A neural net is perfect for this function.
> whereas in Go no such function seems to exist.
It does exist. It's the neural net. It's a simple pattern recognizer, which learns over time more and more.
Evaluation function exists but it is not as simple as it can be for chess.