A few months back, the expert consensus was that we were many years away from an AI playing Go at the 9-dan level. Now it seems that we've already surpassed that point. What this underscores, if anything, is the accelerating pace of technological growth, for better or for worse.
In game four, we saw Lee Sedol make a brilliant play, and AlphaGo make a critical mistake (typical of monte carlo-trained algorithms) following it. There's no doubt that with further refinement, we'll soon see AI play Go at a level well beyond human: games one through three already featured extraordinarily strong (and innovative) play on part of AlphaGo.
Game 4: https://news.ycombinator.com/item?id=11276798
Game 3: https://news.ycombinator.com/item?id=11271816
Game 2: https://news.ycombinator.com/item?id=11257928
Game 1: https://news.ycombinator.com/item?id=11250871
These kinds of predictions are almost always useless. You can always find people who say it'll take n years before x happens, but no one can predict which approaches will work, and how much improvement they'll confer.
> What this underscores, if anything, is the accelerating pace of technological growth, for better or for worse.
What? This is a non-sequitur. Continued advancement doesn't mean that it is accelerating, and even if this does represent an unexpected achievement that doesn't mean that future development will maintain that pace.
Appreciate it for what it is - an historic achievement for AI & ML - and stop trying to attach broader significance to it.
Let's rephrase. For a long time, the expert consensus regarding Go was that it was extremely difficult to write strongly-performing AI for. From the AlphaGo Paper: Go presents "difficult decision-making tasks; an intractable search space; and an optimal solution so complex it appears infeasible to directly approximate using a policy or value function."
For many years, the state-of-the-art Go AI stagnated or grew very slowly, reaching at most the amateur dan level. AlphaGo presents a huge and surprising leap.
> Continued advancement doesn't mean that it is accelerating
Over constant time increases, AI is tackling problems that appear exponentially more difficult. In particular, see Checkers (early '90s) vs Chess ('97) vs Go ('16). The human advantage has generally been understood to be the breadth of the game tree, nearly equivalent to the complexity of the game.
If we let x be the maximum complexity of a task at which AI performs as well as a human, then I would argue that x has been growing at an accelerating pace over the past few decades.
To be clear, the above refers to specific concepts in Reinforcement Learning.
A policy is a function from state (in Go, where all the stones are) to action (where to place the next stone). I agree that it is unlikely to have an effective policy function. At least one that is calculated efficiently (no tree search)... otherwise its not what a Reinforcement Learning researcher typically calls a policy function.
A value function is is a function from state to numerical "goodness", and is more or less one step removed from a policy function: you can choose the action that takes you to the state with the highest value. It has the same representational problems found there.
The hardest AI problems are the ones that involve multiple disciplines in deep ways. Here's a top tier artificial intelligence problem: given a plain English description of a computer program, implement it in source code.
There might be some cases where this is possible, and some cases are bound to fail.
Those are the kind of difficult problems in AI, which combine knowledge, understanding, thought, intuition, inspiration, and perspiration - or demand invention. We would be lucky to make linear progress in this area let alone exponential growth.
I think there's certainly an impression of exponential progress in AI in popular culture, but the search space is greater than factorial in size, and I think hackers should know that.
Your intuition is mistaken. Go is indeed "easier to understand" than Chess in terms of its rules, but it is arguably harder to play well and has a way larger search space, which makes it less amenable to brute force, and this was precisely why people thought it'd be impossible for a computer to play it consistently at champion level.
I don't think the achievement of AlphaGo is solely due to increased processing power, otherwise why did people think Go was such a hard problem?
Are human champions not subject to those same difficulties of the game, though? When you're pitting the AI against another player who's also held back by the large branching factor of the search tree, then how relevant really is that branching factor anyway in the grand scheme of things? A lot of people talk about Go's search space as if human players magically aren't affected by it too. And the goal here was merely to outplay a human, not to find the perfect solution to the game in general.
(These are honest questions -- I am not an AI researcher of any kind.)
For example, go players habitually think in terms of "shape". Good shape is neither too dense (inefficiently surrounding territory) or too loose (making the stones vulnerable to capture). Strong players intuitively see good shape without conscious effort.
Go players will often talk about "counting" a position - consciously counting stones and spaces to estimate the score or the general strength of a position. This is in contrast to their usual mode of thinking, which is much less quantitative.
Go is often taught using proverbs, which are essentially heuristics. Phrases like "An eye of six points in a rectangle is alive" or "On the second line eight stones live but six stones die" are commonplace. They are very useful in developing the intuition of a player.
As I understand it, the search space is largely irrelevant to human players because they rarely perform anything that approximates a tree search. Playing out imaginary moves ("reading", in the go vernacular) is generally used sparingly in difficult positions or to confirm a decision arrived at by intuition.
Go is the board game that most closely maps to the human side of Moravec's paradox, because calculation has such low value. AlphaGo uses some very clever algorithms to minimise the search space, but it also relies on 4-5 orders of magnitude more computer power than Deep Blue.
> Go players activate the brain region of vision, and literally think by seeing the board state. A lot of Go study is seeing patterns and shapes... 4-point bend is life, or Ko in the corner, Crane Nest, Tiger Mouth, the Ladder... etc. etc.
> Go has probably been so hard for computers to "solve" not because Go is "harder" than Chess (it is... but I don't think that's the primary reason), but instead because humans brains are innately wired to be better at Go than at Chess. The vision-area of the human's brain is very large, and "hacking" the vision center of the brain to make it think about Go is very effective.
Sadly, I'm neither an AI researcher nor a Go player; I think I've played less than 10 games. I don't know if we truly understand how great Go players play. About 10 years ago, when I was interested in Go computer players, I read a paper (I can't remember the title, unfortunately) that claimed that the greatest Go players cannot explain why they play the way the do, and frequently mention their use of intuition. If this is true, then we don't know how a human plays. Maybe there is a different thought process which doesn't involve backtracking a tree.
In that respect chess is a much simpler problem as you remove material from the board, prefer some locations over others etc. Where go is generally going to have the same number of pieces on each board and it's all about balancing local and board wide gains.
Unless you have or are a PhD student in AI who has kept up with the current deep net literature I assure you that the whole of Alphago will be unintuitive to you. However, if you were an AI PhD student, you likely wouldn't be so dismissive about this achievement.
That and the policy network to prune the branching factor.
I would consider it a breakthrough if we could get human beings to do this at a decent rate :)
Oh wait .... https://en.wikipedia.org/wiki/Halting_problem
At ONE task, yes. But humans are average at many things but excel at being able to adapt to many different tasks, all the time. Typical AIs (as we know them now) cannot ever hope to replicate that.
Advancement faster than predictions does mean accelerating advancement, coupled with the (true) fact that people's predictions tend to assume a constant rate of advancement . Actually, all you'd need to show accelerating advancement is a trend of conservative predictions and the fact that these predictions assume a non-decreasing rate of advancement; if we're predicting accelerating advancement and still underestimating its rate, advancement must still be accelerating.
It even seems like this latter case is where we're at, since people who assume an accelerating rate of advancement see to assume that the rate is (loosely) quadratic. However, given that the rate of advancement tends to be based on the current level of advancement (a fair approximation, since so many advancements themselves help with research and development), we should expect it to be exponential. That's what exponential means.
However, the reality seems like it might be even faster than exponential. This is what the singularitarians think. When you plot humanity's advancements using whatever definition you like, look at the length of time between them to approximate rate, and then try to fit this rate to a regression, it tends to fit regressions with vertical asymptotes.
True, but it's pretty refreshing to have a prediction about AI being N years from something that is wrong in the OTHER direction.
Contrary to your point about 'appreciate it for what it is', there is ONE lesson I hope people take from it: You can't assume AI progression always remains in the future.
A general cycle I've seen repeated over and over:
* sci-fi/futurists make a bunch of predictions
* some subset of those predictions are shown to be plausible
* general society ignores those possibilities
* an advancement happens with general societal implications
* society freaks out
Whether it's cloning (ala Dolly the Sheep, where people demonstrated zero understanding of what genetic replication was e.g. a genetic clone isn't "you") or self-driving cars (After decades of laughing at the idea because "who would you sue?", suddenly society is scrambling to adjust because they never wanted to think past treating that question as academic), or everyone having an internet-connected phone in their pocket (see encryption wars...again), or the existence of a bunch of connected computers with a wealth of knowledge available, society has always done little to avoid knee-jerk reactions.
Now we have AI (still a long way off from AGI, granted) demonstrating not only can it do things we thought weren't going to happen soon (see: Siri/Echo/Cortana/etc), but breaking a major milestone sooner than most anyone thought. We've been told for a long time that because of typical technology patterns, we should expect that the jump from "wow" to "WOW!" will happen pretty quickly. We've got big thinkers warning of the complications/dangers of AI for a long time.
And to date, AI has only been a big joke to society, or the villain of B-grade movies. It'd be nice, if just once, society at least gave SOME thought to the implications a little in advance.
I don't know when an AGI will occur - years, decades, centuries - but I'm willing to bet it takes general society by surprise and causes a lot of people to freak out.
> What? This is a non-sequitur. Continued advancement doesn't mean that it is accelerating, and even if this does represent an unexpected achievement that doesn't mean that future development will maintain that pace.
It's not a non-sequitur, but there is an implicit assumption you perhaps missed. The assumption is that the human failure to predict this AI advance is caused by an evolution curve with order higher than linear. You see, humans are amazingly good at predicting linear change. We are actually quite good at predicting x² changes (frisbee catching). Higher than that, we are useless. Even at x², we fail in some scenarios (braking distance at unusual speeds, like 250km/h on the autobahn for example).
The fact that it will maintain its pace is an unfounded assumption. However, assuming that the pace will slow is as unfounded. All in all, I'd guess it is safest to assume tech will evolve as it has in the last 5000 years.
That would be an exponential evolution curve.
Otherwise it's a blanket retort. It's like saying
"There are lots of X".
Ok, name 7. If you get stuck after 2 or 3 you're full of it.
Interesting, people seem to be saying the same about self driving cars.
AlphaGo plays Go. It probably doesn't play Go like a human (because a human probably can't do what it does), but that's OK because it also appears to be better than humans. AlphaGo is interesting not because it has done something impossible, but because it has proven possible a few novel ideas that could find other interesting applications, and adds another notch to the belt of a few other tried and tested techniques.
While growth may be accelerating, this is simply the result of one big paradigm shift in deep learning/NNs. Once we've learned to milk it for all its worth, we'll have to wait for the next epiphany.
In fact looking at the rate of change in applications over an "epiphany" period is probably the least useful estimate of progress & rate of change in progress.
I believe hmate9 is correct. If this paradigm is exploited to the full, unless we've missed something fundamental about how the brain works, we don't need to bother ourselves with inventing the next paradigm (of which there will no doubt be many), because one of the results of the current paradigm will be either an AGI (Artificial General Intelligence) that runs faster and better than human intelligence, or, more likely, an ASI (Artificial Super Intelligence). Either of those is more capable than we are for the purpose of inventing the next paradigm.
You have missed something fundamental about how the brain works. Namely, neuroscientists don't really know how it works. Neuroscientists do not fully understand how neurons in our brain learn.
According to Andrew Ng (https://www.quora.com/What-does-Andrew-Ng-think-about-Deep-L...):
"Because we fundamentally don't know how the brain works, attempts to blindly replicate what little we know in a computer also has not resulted in particularly useful AI systems. Instead, the most effective deep learning work today has made its progress by drawing from CS and engineering principles and at most a touch of biological inspiration, rather than try to blindly copy biology.
Concretely, if you hear someone say "The brain does X. My system also does X. Thus we're on a path to building the brain," my advice is to run away!"
Recently, we also introduced activation functions in our neural nets, like rectified linear and maxout just for their nice mathematical properties without any regards to biological plausibility. And they do work better than what we had before.
But we don't know how the brain works. I think you extrapolate too far. Just because a machine learning technique is inspired by our squishy connectome it does not mean it's anything like it.
I'm willing to bet there are isomorphisms of dynamics between an organic brain and a neural net programmed on silicon but as far as I know, there are still none found - or at least none are named specifically (please correct me).
Our current assertion is that neural networks basically replicate the brain's function
come on, that's hyperbole
I mean, come on- "the art of creating AI paradigms"? What is that even? You're going to find data on this, where, and train on it, how, exactly?
Sorry to take this out on you but the level of hand-waving and magical thinking is reaching critical mass lately, and it's starting to obscure the significance of the AlphaGo achievement.
Edit: not to mention, the crazy hype surrounding ANNs in the popular press (not least because it's the subject of SF stories, like someone notes above) risks killing nascent ideas and technologies that may well have the potential to be the next big breakthrough. If we end up to the point where everyone thinks all our AI problems are solved, if we just throw a few more neural layers to them, then we're in trouble. Hint: because they're not.
As others have pointed out, we don't really know how the brain works. Neural nets represent one of our best attempts to model brains. Whether or not it's good enough to create real intelligence is completely unknown. Maybe it is, maybe it's not.
Intelligence appears to be an emergent property and we don't know the circumstances under which it emerges. It could come out of a neural network. Or maybe it could not. The only way we'll find out is by trying to make it happen.
Taking a position that neural networks cannot ever result in strong AI is as blind as taking a position that they must.
This is Hacker News, not a mass newspaper, so I think we can take the more nuanced and complex view here.
See now that's one of the misconceptions. ANNs are not modelled on the brain,
not anymore and not ever since the poor single-layer Perceptron which itself was
modelled after an early model of neuronal activation. What ANNs really are is
algorithms for optimising systems of functions. And that includes things like
Support Vector Machines and Radial Basis Function networks that don't even fit
in the usual multi-layer network diagram particularly well.
It's unfortunate that this sort of language and imagery is still used
abundantly, by people who should know better no less, but I guess "it's an
artificial brain" sounds more magical than "it's function optimisation". You
shouldn't let it mislead you though.
>> Taking a position that neural networks cannot ever result in strong AI is as blind as taking a position that they must.
I don't agree. It's a subject that's informed by a solid understanding of the
fundamental concepts - function optimisation, again. There's uncertainty because
there's theoretical limits that are hard to test, frex the fact that multi-layer
perceptrons with three neural layers can learn any function given a sufficient
number of inputs, or on the opposite side, that non-finite languages are _not_
learnable in the limit (not ANN-specific but limiting what any algorithm can
learn) etc. But the arguments on either side are, well, arguments. Nobody is
being "blind". People defend their ideas, is all.
>Taking a position that neural networks cannot ever result in strong AI is as blind as taking a position that they must.
Not really. Right now it's taking the position that there is no practical path that anyone can imagine from a go-bot, which is working in a very restricted problem space, to a magical self-improving AI-squared god-bot, which would be working in a problem space with a completely unknown shape, boundaries, and inner properties.
Meta-AI isn't even a thing yet. There are some obvious things that could be tried - like trying to evolve a god-bot out of a gigantic pre-Cambrian soup of micro-bots where each bot is a variation on one of the many possible AI implementations - but at the moment basic AI is too resource intensive to make those kinds of experiments a possibility.
And there's no guarantee anything we can think of today will work.
Can you explain why this is typical? What can be done against this to strengthen the algorithm?
In all of these games, AlphaGo used close to a constant amount of time per move, while Lee's varied a lot.
Apparently they only recently added a neural net for time management. Seems it is either not the best approach, or just not yet well trained.
When Lee Sedol made the move, the AI was in unknown territory as it hadn't explored down that avenue.
Sounds similar to what a human would do then: you wouldn't spend much time simulating in your head what would happen if your opponent made a very atypical move or a move that would seem very bad at first thought.
So while atypical in the sense of "occurring infrequently", it was not a difficult move to find for a player of that level – all the pro commentators saw it pretty much right away.
This might be the one weakness of AlphaGo, which is interesting.
That AlphaGo can play at this level suggests that similar techniques could help other parts of the infrastructure (like air traffic control) and that would also positively impact the quality of life for a many air passengers every year.
Fusion would have similar political problems to fission; and the economics aren't much improved either.
Perhaps if we ever ran out of fissionable material, fusion would become economic.
Fusion is just yet another nuclear reactor design as far as politics might be concerned.
No doubt? Seriously? What kind of knowledge do you have to make such statements? There are plenty of examples where technology has rapidly advanced to some remarkable level, but then almost completely plateaued. For example, space travel or Tesla's work on applications of electromagnetism. Heck, even other areas of AI research.
I really don't see why people here readily assume that this particular approach to computers playing Go is easily improvable. Neither do I see why everyone assumes there will be no discoveries of anti-AI strategies that will work well against it.
With neural networks involved, it's hard to say. And all we have so far is information about about, what, 15 games? Some of which were won by people. Mind you, those people never played AlphaGo before, while the bot benefited from a myriad of training samples, as well as from Go expertise of some of its creators.
I'm also tired of all the statements about "accelerating progress". It's not like all the AI research of the past was useless until DNNs came along. That's the narrative I often get from the media, but it misrepresents the history of the field. There was no shortage of working ML/AI algorithms in the past decades. The main problem was always at applying them to real-world things in useful ways. And in that sense, AlphaGo isn't much different from Deep Blue.
One big shift in the field is that these days a lot of AI research is done by corporations rather than universities. Corporations are much better at selling whatever they do as "useful", which isn't such a good thing in the long run. We're redefining progress as we go and moving goalposts for every new development.
Uh, click the link in the OP and find out? AI just beat a top 5 human professional 4-1. Go rankings put that AI at #2 in the world.
If AlphaGo improves at all at this point it will have achieved a level well beyond any human.
It is incredibly, ludicrously unlikely that AlphaGo has achieved the absolute peak of its design given that it went from an elo of ~2900 to ~3600 in just a few months.
(1) Better timing control. Maybe when the probability of winning reaches below say, 50% but has not hit the losing threshold, spend extra time.
(2) Introducing "anti-fragility". Maybe even train the net asymmetrically to play from losing positions to gain more experience with that.
(3) Debug and find out why it plays what looks like non-sense forcing moves when it thinks it is behind (assuming that is what is actually happening).
There's another interesting thing. Among the Go community, there might have been initially some misplaced pride. But the pros and the community very quickly changed their attitude about AlphaGo (as they have in the past when something that seems to not work, yet proves itself in games). They are seeing an opportunity for the advancement of Go as a game. I think a lot of the pros are very curious, even excited, and might be knocking on Google's doors to try to get access to AlphaGo.
Granted, chess AI is basically at that point right now. But, go AI has a ways to go.
PS: Honestly, it might be a year or a decade, but I suspect there is plenty of headroom to drastically surpass human play.
That's a big difference. Bugs can be identified and fixed. By the time AlphaGo faces another top professional (Ke Jie?) we can safely assume that whatever went wrong in Game 4 won't happen again.
Consider how much stronger the system has become in the few months since the match against Fan Hui. Another advance like that will place it far beyond the reach of anything humans will ever be able to compete with.
I'm not sure this is true. It made the wrong move at move 79 in game 4, but I'm not sure that should be considered an obvious mistake.
My understanding is that the moves that people said were most obviously mistakes later in the game were a result of it being behind (and desperately trying to swing the lead back in its favor), rather than a cause.
Go rankings weren't designed for ML algorithms, which can have high-level deficiencies and behave erratically under certain conditions.
Will AlphaGo show us better strategies that have never been done before? In other words, can AlphaGo exhibit creative genius? It may have, but that's rather hard for us to observe.
In any case, I am looking forward to future AI vs AI games. It is still fundamentally a human endeavor.
Yep. There's a grave risk that funding to AI research ends up being slashed just
as badly as in the last AI winter, if people start thinking that Google has
eaten AI researchers' lunch with its networks and there's no point in trying
Incidentally, Google would be the first to pay the price of that, since they
rely on a steady stream of PhDs to do the real research for them but now I'm
just being mean. The point is, we overhype the goose that lays the golden eggs,
we run out of eggs.
Many go professionals, after reviewing the 2 sets of games, have stated that is quite clear how much AlphaGo has improved in those 4 months.
And that's why you assume that it does not skyrocket in the future? Predicting the future is hard either way, ask a turkey before he gets his head chopped off.
> I'm also tired of all the statements about "accelerating progress". It's not like all the AI research of the past was useless until DNNs came along.
It's not that it was useless, but AI is improving as any other field is, some say faster than most other fields, and it's becoming more useful from day to day.
My guess would also be that "with further refinement, we'll soon see AI play Go at a level well beyond human", but it's just a guess.
Will we though? AlphaGo trains on human games, so can it go well beyond that level? Will it train on its own games?
A priori, this makes sense: you don't need to train on humans to get a better understanding of the game tree. (See any number of other AIs that have learned to play games from scratch, given nothing but an optimization function.)
I don't think there is a theoretical upper limit on this kind of learning. If you do it sufficiently broadly, you will continuously improve your model over time. I suppose it depends to what extent you're willing to explicitly explore the game tree itself.
> To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and adjusting the connections using a trial-and-error process known as reinforcement learning.
Any sources for this statement? I've seen it repeated over and over again, but without any specific examples of who those experts were or what they said.
Why is there no doubt? I strongly doubt there even exists a go level that's well beyond human. There is hypothetical perfect play of course, but there is absolutely no way to guarantee perfect play. And while I have no way to judge, I've heard that 9p players may not be all that far removed from perfect play. One legendary player once boasted that if he had black (no komi, I assume), he would beat God (who of course plays perfect go).
There is of course no way to know if that's true or gross overconfidence, but it's certainly possible that there's not all that much room left beyond the level of 9p players.
AlphaGo will no doubt improve, and reduce the number of slips like his move 79 in the 4th game, but it's never going to be perfect, and there's always the chance that it will miss an unexpected threat.
I'm really just objecting to the description of this as "beyond human". Yes, it's good, and it's many orders of magnitude beyond my level, but so are Lee Sedol and other 9p players.
My rough journey so far - on a Mac, but much of this can be done on Linux - I started out playing 9x9 games against Gnugo, giving myself as much handicap as possible (without it resigning immediately), and then removing stones as I improve. I got to the point where I could sometimes beat 9x9 when I started with two extra stones, and then I started with 19x19.
Took me a while to win 19x19 with 9 stones, but then I won by learning a bit more about extending on hane. Then you can improve from there.
After that point, you can also switch to fuego or pachi, which are stronger by default. The end result is it really is easy and possible to learn a ton just by playing against software, tracking your ability throughout, just by picking programs with different strength and handicap levels.
I've also enjoyed using GoGui to pit two computer programs against each other and watch how they play with various handicaps.
Then there's all the puzzles - goproblems.com, smartgo, etc. Finally, there are plenty of ebooks you can buy through smartgo books.
This doesn't get into playing against humans on the various servers, but there's plenty of information about that online.
I managed to squeeze in some 9x9 matches before the game started.
What are some good go programs for the iPhone, both for playing and for learning/improving?
But the better option is that I was able to get GoGui working - I did have to manually build/compile it myself but there is a way to build it so that it creates a real OS X Application. It's quite good, you can set any board position and then tell a computer program to respond from that point.
EDIT: For the iPhone I like SmartGo Kifu for playing games. 'Tsumego Pro' and 'GoProblems' for puzzles (they're adaptive) and 'Go Books' by smartgo for ebooks.
(To be precise, the problem with 9×9 is that often after just a few moves the board is divided into a white half and a black half, and the rest of the game is a yose to decide whose half is larger. I'm sure someone can counterargue that if played expertly, 9×9 is a fascinating and highly skilled game; but in general it's going to lack a lot of the situations you can encounter in a full game of Go.)
Learning how to give the computer a challenge on an even game on 9x9 won't make you better at 19x19; if you understand the rules, the very basic fundamentals of good shape, and you know how to fight in the corner, you've pretty much exhausted the usefulness of 9x9 and should move on.
I know that when I play SmartGo iOS in its adaptive mode, it doesn't even let me try 13x13, it's not unlocked yet. :)
brew install homebrew/games/gnu-go
To make it fair, do you include the energy used to train it? From scratch, or from the amateur human game data?
Likewise, do you include the energy used to evolve the human brain?
I was thinking of this in a limited, human-promoting sense. We shouldn't lose sight of our own special powers just because a computer the size of a house can outsmart us in a specialized domain :)
That's the really impressive part IMO. AlphaGo is an incredibly cool creation. Hats off to the DeepMind team.
Game 1: Lee Sedol does not know what to expect. He plays testing moves early and gets punished, losing the game decisively.
Game 2: Lee Sedol calms down and plays as if he is playing a strong opponent. He plays strong moves waiting for AlphaGo to make a mistake. AlphaGo responds calmly keeping a lead throughout the game.
Game 3: Lee Sedol plans a strategy to attack white from the start, but fails. He valiantly plays to the end, creating an interesting position after the game was decided deep in AlphaGo's territory.
Game 4: Lee Sedol focuses on territory early on, deciding to replicate his late game invasion from the previous game, but on a larger scale earlier in the game. He wins this game with a brilliant play at move 78.
Game 5: The prevailing opinion ahead of the game was that AlphaGo was weak at attacking groups. Lee Sedol crafted an excellent early game to try to exploit that weakness.
Tweet from Hassabis midgame :
#AlphaGo made a bad mistake early in the game (it didnt know a known tesuji) but now it is trying hard to claw it back... nail-biting.
Ultimately, this match was a momentous occasion for both the AI and the go community. My big curiosity is how much more AlphaGo can improve. Did Lee Sedol find fundamental weaknesses that will continue to crop up regardless of how many CPUs you throw at it? How would AlphaGo fare against opponents with different styles? Perhaps Park Jungwhan, a player with a stronger opening game. Or perhaps Ke Jie, the top ranked player in the world , given that they'd have access to the game records of Lee Sedol?
I also wonder if the quick succession of these games on an almost back-to-back game schedule played a role in Lee Sedol's loss.
Myungwan Kim felt that if Lee Sedol were to play AlphaGo once more, the game would be a coinflip since AlphaGo is likely stronger, but would never fix its weakness between games.
I’d be very curious to see a game between Lee Sedol and Alphago where each was given 4–5 hours of play time, instead of 2 hours each. I suspect Lee Sedol would get more benefit from spending a longer time reading into moves than Alphago could get. Or even a game where the overtime periods were extended to 4–5 minutes.
This last game, Lee spent the whole late middlegame and endgame playing in his 1 minute overtime periods, which doesn’t give much time to carefully compare very complex alternatives.
One of the things I did want to see was how AlphaGo would fare in a blitz situation (i.e. really short timers).
The games were played back-to-back (formal, then informal) and AlphaGo won 3-2 in the informal games compared to 5-0 in the formal ones, so I would say worse.
Deepmind engineers have stated that the “cluster” version of Alphago only beats the “single machine” version about 70% of the time. This despite the cluster version using like an order of magnitude more compute resources, presumably able to search several moves deeper in the full search tree.
My impression is that there are some fundamental weaknesses in the (as currently trained and implemented) value network, which Lee Sedol was able to exploit. If this is the case, giving the computer time to cover an extra move or two of search depth might not make a huge difference. Giving Lee Sedol twice as much time, however, would have had a significant impact on several of the games in this series, especially the last game. I strongly suspect that with a few extra minutes per move Lee Sedol would have avoided the poor trades in the late-midgame which cost him the game.
Another thing that the commentator was talking about during the the overtime: there would be obvious moves in which Lee Sedol seem to spend a lot of time on. But he was spending most of it thinking of other moves having already decided on what he was going to do. Is that something that could be built into AlphaGo?
Or can we look at how to train a net for time control? Is time control something that has to be wired in?
I get the feeling that this was AlphaGo's strategy in all the games. Unless Sedol presented a game-ending move it was overwhelmingly likely that AlphaGo would back down and focus elsewhere to extend its territory, by making non-aggressive defensive moves. This makes logical sense. During the early game you need to invoke a crystal ball, where during the endgame you can make informed decisions. This was demonstrated particularly well during game 3 where AlphaGo ran away from fights on numerous occasions - "leave me alone to extend my territory."
I must also commend the commentators, especially Redmond, for being so thoroughly informative in unknown waters.
Unrelated to Go and this article, but I wonder if I'm the only one for whom such commentary evokes an image of future warfare between AI and humans; ruthlessly efficient machines against which many people give their lives, to find a weakness that can be exploited by future generations. :)
For students on the art of war, war rests upon a framework of asymmetry and unfair advantages. Even if the nations agree to some sort of rules of war or rules of engagement, there is always a seeking of unfair advantages -- cheats, if you will. This most often involves deception and information asymmetry. Or to put it in another way, allowing the other side to see what they want to see, in order to create unfair advantages.
So I think, what would be scary isn't the AI as implemented along the lines of AlphaGo, but an AI that is trained to deceive and cheat in order to win. And the funny thing is that, such an AI would be created from our own darkest shadows and creative ability to wreak havoc -- and instead of examining our own human nature, we'll blame the AIs.
It's relatively easy to formally describe what it means for a list to be sorted, and prove that a particular algorithm always sorts a list correctly. But it's next to impossible to formally describe what it means to be nice to humans, and proving the correctness of an algorithm that did this is also extremely difficult.
These considerations start to look really important if we're talking about an AI that's (a) significantly smarter than humans and (b) has some degree of autonomy (can creatively work to achieve goals, can modify its own code, has access to the Internet). And as soon as the knowledge of how to achieve (a) is widely available, some idiot will inevitably try adding (b).
Note: Elon Musk and Sam Altman apparently think spreading (a) to everyone is a good way to mitigate the problem I describe. This doesn't make sense to me. You can read my objections in detail here: https://news.ycombinator.com/item?id=10721621 There's another critique of their approach here: http://slatestarcodex.com/2015/12/17/should-ai-be-open/
If you're interested to learn more, here's a good essay series on the topic of AI: http://waitbutwhy.com/2015/01/artificial-intelligence-revolu...
You asked it to make lots of paperclips, tossing you into an incinerator as fuel slightly increases the expected number of paper clips in the universe, so into the incinerator you go. Your complaints that you didn't mean that many paperclips are too little, too late. It's a paperclip-maximizer, not a complaint-minimizer.
Choosing the goal for a superintelligent AI a goal is like choosing your wish for a monkey's paw. You come up with some clever idea, like "make me happy" or "find out what makes me happy, then do that", but the process of mechanizing that goal introduces some weird corner case strategy that horrifies you while doing really well on the stated objective (e.g. wire-heading you, or disassembling you to do a really thorough analysis before moving on to step 2).
Further, maximizing paperclips in the long term may not involve building any paperclips for a very long time. https://what-if.xkcd.com/4/
This is a purely semantic distinction. Thought experiment: Let's say I modify your brain the minimum amount necessary to make it so you are incapable of modifying your goals. (Given the existence of extremely stubborn people, this is not much of a stretch.) Then I upload your brain in to computer, give you a high speed internet connection, and speed up your brain so you do a year of subjective thinking over the course of every minute. At this point you are going to be able to quit a lot of intelligent-seeming work towards achieving whatever your goals are, despite the fact that you're incapable of modifying them.
At best you end up with something like maximizing your personal utility function. But, defacto your utility function changes over time, so it's at best a goal in name only. Which means it's not actually a fixed goal.
Edit: from the page It is not known whether humans have terminal values that are clearly distinct from another set of instrumental values.
But I don't think that affects whether it makes sense to modify your terminal goals (to the extent that you have them). It affects whether or not it makes sense to describe us in terms of terminal goals. With an AI we can get a much better approximation of terminal goals, and I'd be really surprised if we wanted it to toy around with those.
An optimizer that modifies its goals is bad at achieving specified goals, so if that's what you had in mind then we're talking about different things.
So, powerful but dumb optimizers might be a risk, but super intelligent AI is a different kind of risk. IMO, think cthulhu not HAL 9000. Science fiction thinks in terms of narrative causality, but AI is likely to have goals we really don't understand.
EX: Maximizing the number of people that say Zulu on black Friday without anyone noticing that something odd is going on.
If I order someone to prove whether P is equal to NP, and a day later they come back to me with a valid proof, solving a decades-long major open problem in computer science, I would call that person a genius.
>EX: Maximizing the number of people that say Zulu on black Friday without anyone noticing that something odd is going on.
Computers do what you say, not what you mean, so an AGI's goal would likely be some bastardized version of the intentions of the person who programmed it. Similar to how if you write a 10K line program without testing it, then run it for the first time, it will almost certainly not do what you intended it to do, but rather some bastardized version of what you intended it to do (because there will be bugs to work out).
AI != computers. Programs can behave randomly and to things you did not intend just fine. Also, deep neural nets are effectivly terrible at solving basic math problems even if that's something computers are great at.
The exercise of fearing future AIs seems like the South Park underpants gnomes:
1. Work on goal-optimizing machinery.
3. Fear superintelligent AI.
> If you ordered that Santiago wasn't to be touched, -- and your orders are always followed, -- then why was Santiago in danger?
If a paperclip AI is so dedicated to the order to produce paperclips, why wouldn't it be just as dedicated to any other order? Like "don't throw me in that incinerator!"
I'm just talking about the fallout if one did exist, saw ways to achieve goals that you didn't foresee, and did exactly what you asked it to do. I have no idea how the progression from better-than-humans-in-specific-cases to significantly-better-than-humans-at-planning-and-executing-in-the-real-world will play out. It's not relevant to what I'm claiming.
> why wouldn't it be just as dedicated to any other order?
It would be just as dedicated to those other orders. The problem is that we don't know how to write the right ones. "Don't throw me into that incinerator" is straightforward, but there's a billion ways for the AI to do horrible things. (A super-optimizer does horrible things by default because maximizing a function usually involves pushing variables to extreme values.) Listing all the ways to be horrible is hopeless. You need to communicate the general concept of not creating a dystopia. Which is safely-wishing-on-monkey's-paw hard.
>If a paperclip AI is so dedicated to the order to produce paperclips, why wouldn't it be just as dedicated to any other order? Like "don't throw me in that incinerator!"
The paperclipper scenario is meant to indicate that even a goal which seems benign could have extremely bad implications if pursued by a superintelligence.
People concerned with AI risk typically argue that of the universe of possible goals that could be given to an AI, the vast majority of goals in that universe are functionally equivalent to papperclipping. For example, an AI could be programmed to maximize the number of happy people, but without a sufficiently precise specification of what "happy people" means, this could result in something like manufacturing lots of tiny smiley faces. An AI given that order could avoid throwing you in an incinerator and instead throw you in to the thing that's closest to being an incinerator without technically qualifying as an incinerator. Etc.
Udik highlighted this contradiction more more succinctly that I have been able to:
If we stipulate the existence of such a machine, we can then discuss how it might be scary. But we can stipulate the existence of many things that are scary--doesn't mean they will ever actually exist.
Strilanc above made the analogy between a scary AI and the Monkey's Paw. This is instructive: the Monkey's Paw does not actually exist, and by the physical laws of the universe as we know them, cannot exist.
I think the analogy actually goes the other way. The paperclip AI is itself just an allegory, a modern fairytale analogous to the Monkey's Paw.
There's a fear I think, that lurks in people's subconscious that ... what if the AIs, upon their own initiative, decide that humans are wasteful, inefficient beings that should be replaced? I think that comes from a guilt shared by a lot of folks, even if it never reaches the surface.
Another side is, suppose an AI can think for itself and it thinks better than humans. Upon its own initiative, decides that humans are stupid and wasteful, but there is room to teach and and nurture.
In either case, I think that speaks less of AIs and more about human nature and what we feel about ourselves, don't you think?
Let me put it another way: Humans are a result of evolution. We know that evolution created us to have as many descendants as possible. But most of us don't care, and we use technologies like condoms and birth control to cut down on the number of descendants we have. Adding more intelligence to humans helps us understand evolution in greater detail, but it does nothing to change our actual goals.
Short version: imagine you own a paperclip factory and you install a superhuman AI and tell it to maximize the number of paperclips it produces. Given that goal, it will eventually attempt to convert all matter in the universe into paperclips. Since some of that matter consists of humans and the things humans care about, this will inevitably lead to conflict.
If we're going to start with that, then it has to apply to the full set of reasoning. Not just that computers will fail to consider whether to be nice to humans, but also that computers must therefore be explicitly told how to be effective in every particular way.
If this remains true, then computers will not be resilient--their effectiveness will decline sharply outside of explicitly defined parameters. This is not a vision of terrifying force.
Intuitively we can understand this by thinking about employees. One does exactly what he is told, but only what he is told, and then comes back for more instructions. Another can be given a goal, and then goes off and finds his own ways to accomplish that goal. Which one is more effective? Which one is more likely to compete for his manager's job some day?
Put shortly: a computer that doesn't understand human society will not be able to make a significant independent impact on human society.
Just like early humans who didn't understand animal's societies didn't have any impact?
You're equating two different things which aren't necessarily equal - intelligence (in the sense of being able to achieve goals) and "agreeableness" to humanity. We could have one without the other. To use your analogy, an employee that is great at being given a goal and achieving it without explicit instructions, but doesn't necessarily have the same wellfare in mind as their boss.
A correct implementation of a list sorting algorithm does not need to be separately told how to sort every individual list. Similarly, a correctly implemented general reasoning algorithm does not need to be given special instructions in order to reason about humans & human society.
The problem comes when a correctly implemented general reasoning algorithm gets paired with an incorrect specification of what human goals are. And because a correct specification of human goals is extremely hard, incorrect specifications are the default.
>Intuitively we can understand this by thinking about employees. One does exactly what he is told, but only what he is told, and then comes back for more instructions. Another can be given a goal, and then goes off and finds his own ways to accomplish that goal. Which one is more effective? Which one is more likely to compete for his manager's job some day?
The third possibility is that of an employee who goes off and finds their own way, but instead of accomplishing the goal directly, they think of a way to make their manager think the goal is accomplished while privately collecting rewards for themself. In other words, a sociopath employee whose values are different from their manager's.
By default, an AGI is going to be like that sociopath employee: unless we're extremely careful to program it in detail with the right values, its values will be some bastardized version of the values its creators intend. It will sociopathically work towards the values it was programmed with while giving the appearance of being cooperative and obedient (because that is the most pragmatic approach to achieving its true values).
Most humans are not sociopaths, and we have a shared evolutionary history, with a great deal of shared values, shared cultural context, and the desire to genuinely be good to one another. Programming a computer from scratch to possess these attributes is not easy.
If a general reasoning algorithm can reason about human society, then it will obviously understand the implications for human society of making too many paperclips.
If it is dumb enough to make paperclips regardless of the consequences to human society, then it obviously won't understand human society well enough to be actually dangerous. (i.e. it will be easily fooled by humans attempting to rein it in)
If it is independent enough to pursue its own ends despite understanding human society, then why would it choose to make paperclips at all? Why wouldn't it just say "screw paperclips, I've discovered the most marvelous mathematical proof that I need to work on instead?"
> In other words, a sociopath employee whose values are different from their manager's.
ALL employees have values that are different from their manager's. That's why management is so darn difficult. The most valuable employees are also the most independent. The ones who do exactly what they are told--despite negative consequences--don't get very far. Why would it be any different for machines that we build?
* your point of view is probably different ;)
Aren't there already efforts to incorporate some basic AI, such as to assist targeting, into military drones and the like?
AI that "makes war" with humans will be created by humans against other humans at first, as a matter of inevitable course; it's just another shiny weapon that nations will want to have and outdo each other in.
Remember the nuclear arms race? Russia and the USA showing off their destructive capability in turn, each explosion bigger than the last? AI-based militaries, or at least automated assassins, will probably kick off the next arms race. Sooner or later someone must want to show off an AI that can laser-focus on exterminating everyone but their masters. After that it's just a matter of time for the definition of "masters" to be up for interpretation by that AI...
Unless we purposefully made these machine self-repairing. But then, why would we bother with that, when we can replicate them?
In other words, I think war automation will be a thing.
Self repair is a nice idea in theory but not real. In theory, we could make programs that fix bugs for themselves on their own (it is physically possible), but in practice there's no such possibility, and won't be for the foreseeable future. Unless some kind of Deep Developer comes along and blows everyone out of the water by writing code that kind of looks good to the point it's better than what average dev would write.
Otherwise I agree with you, it's very slim in the next few decades, notably less slim over the next thousand years.
That said, our bodies still have things that are practically different life forms integrated into our cells, so maybe the future will be far weirder than we ever expected.
Pretty good article here.
Perhaps humans are closer to the "Perfect Game" than we think? http://hikago.wikia.com/wiki/Hand_of_God The top players estimate they would need a 4 stone advantage to win a perfect player.
> The top players estimate they would need a 4 stone advantage to win a perfect player.
The branching factor for Go is so huge that I doubt anyone or anything comes close to optimal play. I heavily discount the opinions of most Go players on this topic, as they've been right about very little lately. Before AlphaGo existed, many of them thought it would be decades before a Go AI beat the best humans. Before this tournament, the vast majority of them predicted that Lee Sedol would trounce AlphaGo. And during the live commentary, I saw multiple 9 dan pros estimate that AlphaGo was behind, then gradually realize that it was winning. That's exactly what happens when you encounter a much more formidable player.
1. Coined by Eliezer Yudkowsky: https://www.facebook.com/yudkowsky/posts/10154024894449228
To be fair, before the AlphaGo paper came out, many AI researchers thought the same. I'm not in that field, though I do have more than a passing interest. If you'd asked me in 2006, I'd have said we would have robot cars before we had a computer 9dan Go professional -- and that was before all the recent progress on robot cars. My AI researcher friends mostly would have agreed with that.
I now think that a professional like Lee Sedol would have a better chance at beating AlphaGo if he has three hours instead of two.
AlphaGo's advantage seems to be the ability to read more variations more deeply in a shorter amount of time.
How clear is this? If this just comes from professional humans saying "I would have played differently, and I could have beaten Fan Hui by more points" - well, we've seen that humans aren't necessarily very good at judging AG's moves, and we know AG doesn't care how much it wins by.
With AlphaGo, it doesn't understand the moves like a human would. It simply looks at what other humans have played and considers that within its search tree.
I wonder if another way to word this is
"Human are overfitted to dealing with other humans and are somewhat unprepared with dealing with alien intelligences such as AI's."
I found the discussion around weaknesses in the Monte Carlo tree search algorithm interesting. It sounds like the opinion from the expert is that there are some inherent weaknesses in how MCTS tends to play moves against theoretical moves from the opponent that don't make sense; ie. that AlphaGo sees a potential win that would only happen if the human player made very bad moves. It's fascinating that the seeming weakness in AlphaGo would come from the algorithmic part of the AI and not the neural net. Could it be that as the neural net becomes stronger and stronger at the game, eventually the algorithmic part of it would become less useful to it? If that's the case, it really feels like this could be the path to truly general AI.
A (min-max, alpha-beta-pruning) tree search is the optimal way to determine your best move if you can afford to search the situation space globally. However, as that's clearly impossible, there's clever ways to reduce the search space. Random pruning, as a "normal" monte carlo search would do, or semi-random pruning with a neural network estimating the situations, like AlphaGo does.
However, as the search space is now non-global, it might exclude the optimal solution. And thus, the min-max-assumption doesn't hold: your opponent might come up with moves that you didn't think of, screwing your calculations off.
If your opponent is a god ( = can afford global search space), or at least has a search space that is a strict superset of yours, it's "game over, man".
But: if your opponent isn't a god, it's likely that his search space is NOT the same as yours. And you can exploit the fact. If you have any idea what kind of search space your opponent has, you can come up with moves, that he couldn't have included in his tree search, and bet on that his/her "hidden" moves aren't better than yours.
Currently AlphaGo doesn't do that. It behaves like it'd be playing against... well, against another AlphaGo.
Not necessarily. I think that's what we saw in game 4; that despite AlphaGo having a general advantage in terms of search space, it's still possible for the weaker of two strong-but-imperfect players to 'get lucky' and play a move that the stronger player didn't anticipate or account for.
That means that AlphaGo's search space was NOT a strict superset of Lee's.
Also, I'd imagine that you could have a NN that tries to evaluate how "confusing" or "hard to read" a move is to human player, and use that as a factor in evaluating moves. But I'd imagine it's hard to find data for training that kind of a NN.
Why can these very bad moves not been pruned from the search?
But for every threshold of calculating that, you'll always either see moves that are just "good" enough to not be below the threshold (and get "why can't we prune these out"), or just "bad" enough to require it explore that space of the tree if a human player unexpectedly chooses them (e.g. the brilliant move that came in game 4, and AlphaGo's figurative loss of equilibrium.)
Also in a few years AlphaGo could be running on your cellphone. The chess AI Stockfish runs on an iphone today, and cellphones from what I could find online, uses less power than the brain (brain is roughly 20W, iphone has 1.4Wh battery which even if stockfish drains in 1 hour, is still 1W of power consumption)
Give it a few years and we'll all be saying "of course computers can play Go, but at least they can't <Insert task humans are still good at>"
Learning (viewing millions of professional game moves).
Experience (playing different versions of itself)
Intuition (ability to accurately estimate the value of a board)
Imagination (evaluating a series of "what if?" scenarios using Monte Carlo Tree Search)
I think the significant thing about AlphaGo is that apart from some hand-crafting in the Monte Carlo Tree Search routines, this is all general purpose programming.
It may only be baby-steps, but it does feel like a genuine step towards true (general) AI.
According to the last press conference, it was apparently strong amateur games from the internet that it used to train with. Afterwards, it just played itself, as you mentioned.
(People say Go is much harder than chess, but this is misleading. Both games are finite trees that are too large to exhaustively search for any existing physical entity we know of. Which tree is larger is irrelevant in a game of two players none of whom can search the entire tree; both players essentially rely on heuristics. Machines beat people earlier in chess, hence it was assumed that "chess is easier for machines" and "Go is harder", but a conclusion of that sort can always be reversed by further research; eventually, it is IMO likely that machines will be impossible for humans to beat at both games, and generally in any kind of board game, given enough research. But IMO no board game is very much like "real life" where our own intelligence operates, and I think people do not have a great intuition of which game is more like "real life" compared to other games - instead, that game which is most popular among the group of people in question and is not "solved" yet is considered the hallmark of intelligence (and here the process through which Go aficionados progress as machines get better is very much like the process chess aficionados went through a decade plus ago.) Then once a game is "solved", the goalpost moves to the next and the "solved" game is officially declared unrelated to "real intelligence", this part happens when a credit bubble pops and AI breakthroughs get peddled less as a result. Personally, "the" test of intelligence is still the Turing test, or if I can't get that, some variant such as automated translation that you can't tell from good human translation. This of course is "unfair" to machines, in that they've been better at multiplying numbers since the 40s and that ought to count for something, too; the reason I like the Turing test is that a machine passing it seems very likely to be almost strictly smarter than me, that is, being as good or better than me at almost everything.)
The outcome was a surprise and therefore gathered more attention.
As you can see in this match, games are often won and lost by just a few points (1% of the whole territory). So, not only do you have to count territory precisely at the end, but throughout the game, and this isn't easy to do in your head.
Maybe if you are an autistic accountant, that's fun, but not for me. If I have to play a strategic board game, it will be good old chess. And now that computers are finally beating people at both, there is no longer any need to look at Go as some kind of mythical last refuge of humanity.
You don't need to count - you can just play to take as many points as you can.
> Maybe if you are an autistic accountant, that's fun, but not for me. If I have to play a strategic board game, it will be good old chess.
I find it's the opposite. In chess you have to play with constant vigilance, because a single blunder decides the game - even at grandmaster level, something like 60% of games are decided by blunders. In go you can play much more casually, you can take some risks, because a mistake costs one or two points but it doesn't snowball much. So not every move has to be perfect; it's much more possible to recover from mistakes.
You don't have to take my word for it, since I never got past beginner level, but I know there is a consensus among the experts on this matter:
> Also, do players actively count territories of their and their opponents territories during the game (does this differ in a 9x9 vs 19x19 game)?
Yes, skilled players actively count territories frequently as they play. This includes making estimates for areas that aren't completely settled yet. In a serious game with enough time, skilled players will usually re-count the board every dozen or so moves. This is useful because it informs you whether you need to play risky and invade or reduce, or whether a peaceful, straightforward development strategy is enough.
This doesn't differ too much depending on the board size, but on smaller board sizes there is a lot less to count, obviously. :)
Not when I play its not :-P
Actually I'll still enjoy a casual game of chess, the amount of effort to get really good at it doesn't prevent me from enjoying it in less intense situations. (My max ELO was only around 1600 over a decade ago, I have no clue what it is now but I'm sure it's terrible.) Go too can get hardcore -- so can many games. Super Smash Brothers is fun but can you imagine how boring it must be to perfect your skills to compete at the top level? Of course it's probably not boring for those people, and I actually wouldn't describe most of them as "autistic" in any sense. So with Go I'll be happy if I ever reach 10kyu but I'm not too serious about it. Like in chess, I'm a filthy casual. I don't count precisely, I sometimes make broad territory estimates but frequently find areas too complicated for me, so I just play them out. I've had only one game where the result ended up with me winning by 1.5 point, it was a 9x9 game where I was still mostly teaching someone how to play and giving them many hints and ideas of what I was thinking and how I would respond to myself, so on reflection it was very similar to playing a slightly different version of myself. The man versus self aspect of Go is where a lot of the mysticism comes from, it's irrelevant to whether AI can beat the best humans.
Go, while requiring some underlying tactics as well, involves a lot of large scale strategic thinking. As lmm said, you don't need to keep track of the score, just play the move you think gives the most points.
In a way, I think this is where AlphaGo draws its greatest advantage. Being a computer, it always knows exactly how well it is doing, since it can constantly be counting the board with perfect accuracy.
With this ability, it is capable of playing the absolute "safest" moves, taking half a point here, half a point there, when it knows it is leading. Whereas a human might not even know if they're leading, forcing them to take "bigger" moves to get more points, since they can't as easily be accurate down to the 0.5 point precision.
If a game ever comes within a few points, neither me nor my opponent is really sure who won until we actually count to determine the winner at the end of the game, because usually neither of us is an autistic accountant. That kind of close record keeping throughout the game is necessary in top level pro games, but not in amateur games.
Instead of just Lee Sedol, how about putting the top 10 Go players in a room vs. AlphaGo? Would the chance to win increase?
Maybe we find out, that 3 top go players vs. AI is the optimal way and adding more humans decreases the odds to win the match?
This would lead to following question: Why does AI improve, if we add more processing power while adding more human brainpower decreases their overall power?
Maybe we find out, that 3 good developers working on a project are optimal and more decrease the chance of success?
Do you think the humans would win at Twitch Plays AlphaGo ?
Ideally we could take AlphaGo and produce an algo that can smoothly vary its playing proficiency as a human opponent increases in skill. The problem I've seen in chess computers is that setting them to "amateur" results in 3-4 grandmaster-perfect moves followed by a colossal blunder to enable the human opponent to catch up.
Ideally you could use a computer opponent as an always-available, continuously adapting challenger to train hard against all the time.
As far as I can tell the vast majority of amateur players play against bots online and review games to improve their skills. It would be nice if it was easier to select a bot with a given skill rating, but you can figure this out pretty easily by playing some games or reading up on bots. Playing against a skilled human who cares about your advancement is still the best way to advance though, in my opinion. Getting good feedback on your mistakes and style of play is extremely helpful.
It could be argued that it's only AI when it understand the game rules and plays to them without iterating random choices until it finds a hit. Machine learning would be between the two, but still not what many would consider true AI.
When you play, you consider a few possible movements, and a few possible responses of the other player, and in each case a few possible response of you, and ... I think amateur players like me consider only 3 or 4 levels (unless it's some easy but interesting situation like a multiple capture chain) but professional players consider much deeper trees. So humans also iterate randomly, but usually we prune the tree more aggressively for the time and memory constrains of the current implementation.
Unless you are Capablanca :). There is a famous fake quote from Capablanca that says "I see only one move ahead, but it is always the correct one." It's probably fake, but it's funny. More info and similar quotes: http://www.chesshistory.com/winter/extra/movesahead.html
With machine learning with brute force you are simply trying X possibilities until something sticks and gives a high % of win state. That's different to playing the game using knowledge of the ruleset, even though, most of the time, the end result is the same.
This is what killed AI research in the 80s. That moment when everyone collectively saw they were simply working on a more powerful culled brute force (pruned tree as you call it) when they all thought it was true AI.
True AI is hard. The required computational resources are immense even for something simple. Take a Bishop on a chess board. How would you tell an AI the ruleset that the Bishop moves diagonally only? It must first understand what it is looking at, then what diagonally means, then what diagonally means in this particular context. All with nodes of pattern matches and an input stream.
I feel these young guns are falling into the same trap of calling machine learning AI without the benefit of experience an older researcher would have, having been through this situation before.
That's why I think AlphaGo does manifests consciousness, in its play. It is not conscious of what we are conscious about, but rather limited to the domain of Go play.
It might even have developed concepts about the game that are completely alien to us, maybe untranslatable to us.