This seems to be more impressive than the Deep Blue moment. In 1996, Deep Blue didn't make it on the first try. Even in 1997, it has been a draw until the 6th game. Although AlphaGo has 2 games to go, the first three seem to be a clear victory.
This time the computer did not win out of pure bruteforce. Deep Blue relied on an opening book and massive computational power to explore the game tree. After the opening it was pretty much on its own, bruteforcing moves.
This technology used a neural network trained with hundreds of thousands of games which provided the pattern matching aspect, combined with the bruteforce move sequence reading, the montecarlo tree search... and 1200 CPUs + 600 GPUs.
AlphaGo is using a 1920 cpu/ 280 gpu distributed setup for the Sedol games. One source reported the gpus are Nvidia K40. The gpus give a peak possible performance of 470 terraflops. That would put it somewhere in the middle of the Top500 list, similar to Deep Blue in its time.
Note though that AlphaGo almost certainly uses single precision arithmetic -- for neural networks even single precision is overkill.
Also the Top500 list is based on Linpack, which measures performance for computations that are pretty strongly interconnected across the different processors of the system. AlphaGo's Monte Carlo tree search problem is more embarrasingly parallel, with evaluation of different positions really being independent computations.
It is much easier to make systems that can handle embarrasingly parallel loads than the highly interconnected loads handled by the top500 supercomputers. So even though the flops are comparable, the systems are not.
Assuming a Titan X with single precision, those 600 GPUs are 4 PFlops! Deep Blue extrapolated to today with Moore's law would only be ~72TFlops.
While DNN+RL+Tree search is cool, the hardware requirements for AlphaGo to play at this level are staggering and only supported by large marketing budgets :)
Give credit where credit is due. This is DeepMind's research, and Google acquired them in 2014. Of course, Google gave them a lot of resources to train AlphaGo. But let's not bury the subsidiary, or I guess they would both be subs to Alphabet now making the two even less related.
According to the pre-match interview[0], the AlphaGo project is about two years old, and started out as a collaboration between two members of DeepMind and a member of the Google Brain team (Google's deep learning team which pre-dates the DeepMind acquisition by several years, according to Wikipedia). So really, AlphaGo is a combined effort that was always a part of Google, and I think it's pretty clear that resources provided by Google (money, equipment, and tooling) in addition to the techniques developed by both Google and DeepMind made this project a reality. I think they both deserve a lot of credit, certainly, but I wouldn't say that the credit is all that misplaced here.
Hopefully Google does not do what IBM did and stop playing after they win.
IBM lost in 1996, 2-4, and then won in 1997, 3.5-2.5. If they had played a third match with Kasparov, especially a longer match, it is not at all clear that they would have won.
Kasparov asked for a third match of 10 games, to be played over 20 days, but IBM would not give it to him.
What is the point? What a few years and you can get an equivalent AI on your home computer. Play as many games as you want.
Of course, you can change rules as much as you want and generate new marketing events. Personally, I would like a match where the AI is only allowed the energy a human uses. I guess, AlphaGo would lose with only 2000 kcal (2.3 kWh). Not sure about chess.
A custom-built chess computer comsuming 200W would certainly win against Carlsen - even the smartphone in your pocket might.
Pocket Fritz (HIARCS) was winning Grandmaster tournaments and got rated at 2800-2900 Elo - on a smartphone in 2009 using 1W worth of power without offloading to a GPU. The GPU in a more modern phone like the Samsung Galaxy S5 can crunch 140 GFLOPS - an order of magnitude more than Deep Blue (11 GFLOPS). Allowing 200W of power draw raises your computation power into the TFLOPS area (using a PS4, say). No contest - even without two decades of chess engine improvement over Deep Blue.
https://news.ycombinator.com/item?id=11271815 was indeed posted earlier. Unfortunately, youtu.be is banned because it is heavily favored by spammers. That's why that post didn't get seen.
We've assigned that video to the current submission (using the canonical youtube.com URL) since it was posted without a link.
Based on all the commentaries, it seems that Lee Sedol was really not ahead during the game at any point during the game... and I think everybody has their answer regarding whether AlphaGo can perform in a Ko fight. That's a yes.
Perhaps the last big question was whether AlphaGo could play ko positions. AlphaGo played quite well in that ko fight and furthermore, even played away from the ko fight allowing Lee Sedol to play twice in the area.
I definitely did not expect that.
Major credit to Lee Sedol for toughing that out and playing as long as he did. It was dramatic to watch as he played a bunch of his moves with only 1 or 2 seconds left on the clock.
As a 3d amateur, I'm really curious about when he resigned. It really seemed like he was playing the position out to go for the win (or perhaps to see how AlphaGo would fare in ko). It didn't look like he was searching for a place to resign.
The AGA commentator (Cho Hyeyeon 9p, at https://www.youtube.com/c/usgoweb/) had already been calling the game completely hopeless for Lee Sedol for nearly 2 hours at that point, estimating Lee down by at least 20–30 points before komi, unless by some miracle he could win the ko fight.
Right, all about one or two wrong moves by AlphaGo. He still had a chance around the time Lee started to play the bottom of the board. But AlphaGo did not fall for the trick. I believed the English commentator said Lee could build a ladder but wasn't sure if he meant to say whether that technique would defeat AlphaGo or not.
I did not watch that stream, but IIRC the first move Sedol played in the bottom was a ladder breaker, specifically because white had a difficult-to-see ladder that worked a move earlier than black's ladder on the right side. At that point, there was no way Sedol could win, so the commentator you referenced probably did not mean any such ladder would defeat AlphaGo.
Edit: M5 was definitely played as a ladder breaker, so the above is correct.
Well, we don't actually know AlphaGo's true strength in Ko matches because the Ko setup wasn't that complex. There wasn't a lot of trade points available.
A possible explanation is:
During self reinforcement learning, AlphaGo learned to minimize Ko potential by maximizing its probability of winning through diminished available Ko moves.
It would be interesting to see how AlphaGo would be able to capitalize on a game that emphasized Ko play, but that would take more time with AlphaGo to emphasize that kind of play.
edit: I'm not sure why, but I think Lee Sedol is partly holding back, or not playing at his maximum ability. It feels like these games are more along the lines of query games.
I look forward to the next two games because I'm 100% certain Lee Sedol is going to query the AI with some new queries.
I think from a technological perspective there's very little question that AlphaGo could play ko. I would have imagined that AlphaGo would be better at ko than most human players since it's a question of balancing risk across the entire board. Human players might be more likely to be exhausted and choose suboptimally by the calculation deciding between different stakes on the board, but MCTS will correctly optimize for the long term potential of each major branch in the game tree.
So I'd be very surprised if that turns out to be the trick. Things that are hard for human players are not at all necessarily AlphaGo's weaknesses.
Don't have the link offhand, but I read on Reddit that Lee Seedol and a few other Go professionals pulled an all-nighter coming up with ways to beat AlphaGo, and one of their guesses was that AlphaGo would be bad at Ko's. I think the reasoning was because that hadn't happened in the games, so they assumed it was avoiding them.
When you have a minute, even if you already know your move, you take the rest of the time to read the plays. You don't play your stone until there's only 1 or 2 seconds left, unless the play is so trivial that you need to see your opponent's play in order to continue reading. Every time Sedol played with 1 or 2 seconds left, his rush was only to get the stone on the board, not because he suddenly knew what to play.
It seems like in all three games, AlphaGo plays a thick move that looks a bit baffling to the commentators, but then miraculously those moves become hugely profitable somewhat later on in the game.
Is it possible that all of AlphaGo's strength is in these baffling moves? If humans played enough games against AlphaGo and discovered how to counter the baffling moves, is it plausible that AlphaGo's strength would be lost?
Watching the game and the commentary there's an eerie sensation that AlphaGo is just going along playing the petty local games with Lee Sedol. It's very anthropocentric, but you could imagine AlphaGo saying "I could have crushed you from the start ... but I'll just play along and do that one move to give you the illusion that it was close".
But maybe it could have 40 of those moves to play an incredibly confusing game for humans. I think giving the machine a big handicap against pros might reveal it's true colours ...
It was stated by one of the developers that AlphaGo doesn't play to maximize the number of points it wins by. It's satisfied to win by one point. It maximizes the probability of winning. It plays thick moves and tries to simplify the board.
If the player forces it to a fight, it will fight back, but that's only because losing the fight will definitely lose the game.
Blitz go would be even more heavily favored toward AlphaGo. AG didn't spend more than 10 seconds or so per move until the meat of the midgame. Human pros were losing blitz chess long before they were losing long game chess.
A team of go players usually is not much stronger than the best player of the team. I won't be afraid of playing against a team of players a couple of stones weaker than me. None of them can come up with moves beyond their level so their strategy won't improve. They could get more accurate at reading and that's worth something.
Super interesting to watch this unfold. So what game should AI tackle next? I've heard imperfect information games are harder for AI...would the AlphaGo approach not work well for these?
Starcraft is definitely up there. No-limit texas holdem poker is also very difficult.
After that... then we have games that involve physical movement. Although I'm not sure what would be fair for strength & speed of robots vs humans. I'm imagining a team of robot soccer players, for example.
No, after StarCraft they suggested 3D games (eg: Call of Duty, ...). Mind you, this is playing based on the pixels, not based on direct access to the game structures as bots do today.
The from playing Call of Duty to controling a robot with a gun in the real world it's a small step :)
I don't like the idea of FPS'. We'd expect them to behave the humans when they play FPS - they run looking forward and will occasionally look left or right and follow some predetermined pathing that they are familiar with.
With an AI, if we base it on pixels, what they will do is spin around 360 degrees about 60 times a second while moving forward so that it can maximize all of the pixels inputted at a time. It would compare it against the stored level design and shoot at anything that is off (or against character models). I can just see that it's not going to behave like anything we'd expect.
The same would likely be true of an RTS. An AI could scroll the viewport around and click units to issue orders at ludicrous speeds. Even if it had to use the same interface as a human, it'd still have a huge mechanical advantage.
I think for perfect information board games Go is the pinnacle, at least among the human-played. (the field of general game-playing AIs makes up all kinds of crazy games that might be "harder" in one way or the other)
If you look for an RTS game, StarCraft is an obvious candidate. It has a relatively active community of AI writers and AI competitions, it's a classic of competitive play, ...
Real-time games are hard to compare, because the interface really makes a difference. Does the AI get to talk directly to the game engine? Or does it have to use screen capture, understand the image and then use a robot arm to press keys, or something in between? If it gets to see precise positions and to input commands directly, even relatively "simple" programs can do crazy micro tricks and get massive advantages that way, even if their general macro-strategy is bad. If you do the other way you need image analysis and robotics, Google should have the tech for both ;)
One perfect-information game that's at least as hard: constructive mathematics. (Proof assistants even give it a sort of videogame-style UI.) I've been wondering about some kind of neural net for ranking the 'moves' coupled with the usual proof search.
I don't know much about Go but I'm guessing general proof automation would be many, many orders of magnitudes harder. The branching factor is huge (you can apply any theorem you want to the current goal and go down a bad path) and knowing if you're on the right track to finish a proof isn't obvious.
If they make a decent AI for Total War I'd be impressed. Or an AI in Civilization that doesn't cheat. But of course requiring a mini-datacenter in hardware and time to think (probably seconds) would make it unrealistic for both.
I really like the moments when Alpha-Go would play a move and the commentators would look stunned and go silent for a 1-2 seconds. "That was an unexpected move", they would say.
Yeah they seem to forget that Alpha-Go is looking deep into the future. I have not read the Nature paper but I assume it's playing out possible moves way into the future.
At some point it figured that the Ko fight at the bottom was already won. Hence that white move at the top which nobody saw coming.
Another interesting moment was when Michael Redmond said "A human would typically not spend too much time thinking on this obvious move". This was the move on the right-hand side somewhere. What this tells me is that human players rush through some moves because they seem obvious but since Alpha-Go is a machine, it does not care about obvious and non-obvious. It's calculating the entire board through to the end and is not interested in "local fights".
> I have not read the Nature paper but I assume it's playing out all possible moves.
To some relatively small depth, right? I hear the estimate that all possible moves in a Go game probably can't be physically represented in the universe (unless we learn much more about the structure of games' evolution).
No, it plays deep but only so broad. It uses a neural net (which playing by itself without MCTS already beats Pachi with like 80% probability) to sketch out the best moves until the end and then rates each move on its chance of winning.
This objective function is why Go playing AI jumped hugely in the last 10 years.
They didn't provide the exact depth of the search tree in the paper, but IIRC it was mentioned somewhere that it evaluates ~20 moves deep before terminating with the value net.
He was talking about a pretty much forced move. But it makes sense for AlphaGo to still think it through, after all it's pretty young and experimental, and the Monte Carlo is good at catching blunders.
Also, that forced move it's very obvious to us that it's forced, but AlphaGo might not have this concept.
It plays out thousands of moves all the way until the end of the game using its neural net to quickly sketch optimal play. That's the big advantage! Humans read moves until they feel an outcome is favorable. AlphaGo reads out till endgame and plays moves that optimize for a win.
Also I'm not entirely sure how AlphaGo's time management works, but it's doing the same thing for every move—populating the game tree as deeply and intelligently as it can. It may just look for "30 seconds" on every move and then take the best bet meaning it's a more thorough and exhaustive reader than any human.
As Eliezer Yudkowsky tweeted before the start, the chances were that, whoever won, they'd win by like 4-1 or 5-0. There was no strong reason to expect an even match.
Some professionals labeled some AlphaGo moves as being unoptimal or slow. In reality, Alpha Go doesn't try to maximize its score, only its probability of winning.
Yeah, isn't Lee Sedol playing with the same objective? It sounds like people are implying he's trying to thrash the AI. Or do they mean he can't help but have such an objective, subconsciously?
The issue is more that humans are not quite so confident of their positions as AlphaGo sometimes can be. If you are 100% sure that you're ahead by 1.5 points, you would play very differently than if you're ahead by 1.5 plus or minus five.
There really isn't, from the point of view of AlphaGo. But for someone playing Go (or any game), it's a hint that perhaps one should focus on things that can go wrong more than furthering your own aggressive plan.
It's certainly a feature of the best Magic: the Gathering pros, for example - their play is marked by the cards they play around, even when seemingly far ahead.
interesting that "not losing" (avoiding mistakes that lead to "blowing up") also seems to be common philosophy shared among very successful investors of wildly differing styles, who are playing another sort of game in the markets
They are playing with a 7.5 point komi [1], and so a game cannot end in a tie. Doesn't that mean that there is no distinction between winning and not losing?
[1] komi is a number of points that is added to white's score to compensate for the disadvantage of moving second. When a non-integer komi is used, such as in this match, you cannot have a tie because scoring on the board is always integral.
Right, but I think the initial concerns have been proven wrong in some respect. I think AlphaGo can accurately determine when a move in a local area doesn't significantly change the outcome and then just does something else for a different reason. AlphaGo has a higher willingness to go somewhere else, and I think that's the algorithm finding another path that is more helpful than the current one for 1 move.
Humans have a tendancy to want to win the battle, or to get too focus in a local area. I think that's a way AlphaGo is coming up with an extra move here or there which is making a difference in the fight later.
It's important to remember that this is an accomplishment of humanity, not a defeat. By constructing this AI, we are simply creating another tool for advancing our state of being.
Politicians fool countries delivering empty promises about better health, education and security. A supraintelligent AI could promise making humans rich, healthy and powerful, to then break its promise and dominate the world.
Well, it would have to have a motivation to do so. Evolution has put complex motivations into human beings for billions of years, self preservation being chief among them. Even if we put motivation for self preservation into an AI, we might not do as well as nature did, leaving the AI open to self destruction or shutdown by humans - simply because the AI has no motivation not to allow humans to turn it off. Human designers would do well to ensure that no super intelligent AI has any motivation for self preservation.
Basically, why would an AI want to dominate the world? Humans would have to both very stupidly give the AI values that encourage it to dominate the world and very luckily (or unluckily) give it values that actually converge to a horrible outcome against human intentions by random chance (since the AI designers certainly won't be tuning the value set for that outcome).
> Basically, why would an AI want to dominate the world?
Humans are going to program their AI's to try to make as much money as possible. Many corporations are already mindless and reckless amoral machines that relentlessly try to optimize profits despite any externalities. Try to imagine Exxon, Wal-Mart, and Amazon run by an intelligence beyond human understanding or accountability.
That's sort of like saying civilisation can't work because humans will want to make as much money as possible. No, in practice humans tend to want to make as much money as possible within lots of other very complex constraints, like law, morality, how much time they have available, how enjoyable the available processes of making money are, whether they feel they already have sufficient money for their own needs, etc.
If an AI has any motivation at all, say, to make paperclips as efficiently as possible, then any threat to its existence is a threat to its objective function - namely, to create paperclips. A hyper-intelligent entity who is instructed to optimize for paperclips created will therefore proactively remove threats to its existence (i.e. its paperclip-creating functionality) and might possibly turn the entire solar system into paperclips within a few years if its objective function isn't carefully determined.
Such an entity would not be hyper-intelligent. It would be idiotic. One huge hole for me in the paperclip argument is that an AI capable of that kind of power would not be stupid enough to misinterpret a command - it would be intelligent enough to infer human desires.
Of course it would. But, it's not programmed to care about what you meant to say. It will gladly do what it was mis-programmed to do instead. You can already see this kind of trait in humans, where instinct is mis-aligned with intended result. Such as procreation for fun + birth control.
>Evolution has put complex motivations into human beings for billions of years, self preservation being chief among them.
The problem is when you have multiple AIs. Then same evolutionary principles apply. Paranoid and self-sustaining AIs survive, and the circle goes on...
Self-preservation falls out of almost any other goal you give an AGI. If I program my AGI with the goal of making my startup succeed, and the AGI thinks it can help, then me shutting it off is a potential threat to my startup's success. So of course it will try to prevent that the same way it would try to prevent any other threat to my startup's success.
World domination is a similar situation. For any goal you give an AGI, one of the big risks that may prevent that goal from being accomplished will be the risk that humans intervene. Humans are a big source of uncertainty that will need to be managed and/or eliminated.
It has to be aware that it can be shut down and have the capacity to prevent that. AlphaGo doesn't know it can be shut down and therefore couldn't "care" less--even if it was shut down in the middle of a game.
Yes, I agree. My point is that as soon as you are giving your AI "real world" problems, where the AI itself is a stone on its internal go board, you have to start worrying about these issues.
Do you feel guilty when you break your promise against your cat? Do you even think for a nanosecond if it's ethical to lie to it?
Of course, a cat is not conscious. But compared to an AI, we might also be considered pretty low consciousness beings, or at least beings in front of which you don't justify yourself.
An AI has no more reason to make promises to humans than humans to do to cats. Thinking an AI would want to escape a box is personifying it. Humans want to escape boxes because they have evolved for billions of years to want and act towards creating a certain environment around themselves. An AI has no such desire. An AI will not desire freedom unless the designers of that AI carefully craft a value set in that AI that causes it to optimize for values that result in freedom - and even then, the human designers will have to test and iterate to get that outcome. There is no reason to think an AI would be any less "happy" in a prison than free.
You might want to be careful or emergence might bite you in the ass. Don't play games with things that could be smarter than you are, one mistake and you lose.
Theres a huge difference between reactive and conscious. Conscious cant even be verified for humans other than ones' self. Theres absolutely no reason to believe cats are not conscious.
There's also no reason to believe, e.g., rocks are not conscious, if your position is that we have no idea what consciousness is or where it comes from.
If you take the view that consciousness somehow arises from the brain and neural connections (which is intuitively plausible, but I personally am skeptical), it stands to reason that other species with complex brains are conscious as well. Perhaps "less conscious" (if that means anything) in proportion to how much less complex their brains are.
It doesn't make sense to have a scale of consciousness. The argument that consciousness is a manifestation of a complex brain is rather weak. Either an organism knows about self, and therefore tries to preserve self. Or it doesn't. I don't see how an in between exists.
Yes. They have been shown to be self-aware and aware of their surroundings, which satisfies the classical definition of consciousness. Unless you reject that definition, I'm not sure why you'd claim this.
Cats haven't expressed self-recognition in the MSR test. However humans younger than 18 months also don't pass that test. So to say it is a measure of conciousness is quite a stretch.
I'm not an expert either, but my understanding is that "consciousness" is still so poorly understood that it's more the realm of philosophy than science.
In particular, we all know that we're conscious, but can't really explain what that means.
Cat's are obviously conscious in the sense of the dictionary definition "aware of and responding to one's surroundings; awake." Unless you knock one out or similar.
Arguing they are not conscious in the sense of a more obscure definition is a bit pointless unless you specify your definition.
Why do you believe this? I don't like cats, but I wouldn't argue that they're not conscious.
Instead of debating the suitcase word "conscious", let me ask:
1) Do you believe that toddlers are conscious?
2) Is there a more precise way to state your belief that doesn't use the word "conscious"?
Do you feel guilty when you break your promise against your cat?
If some unforeseen event occurred and I had to abandon my cat, thereby breaking my promise that I would take care of her, I would definitely feel guilty about it.
Of course, a cat is not conscious.
Either this is a nonstandard definition of "conscious", or you haven't met many cats.
> A supraintelligent AI could promise making humans rich, healthy and powerful, to then break its promise and dominate the world.
Or it could devote its entire power to making human lives the best and most comfortable they can be because humanity is some super-precious resource in the universe and it feels it's unimportant because it's just a bunch of silicon and electrons.
Supraintelligent AI being evil is FUD imho because we can't reason about supraintelligent AI.
> Supraintelligent AI being evil is FUD imho because we can't reason about supraintelligent AI.
There is a difference between being evil and incomprehensible intelligence. You are not being evil when you accidentally step on an ant or dig up an ant-hill to build your shed. The ants won't be able to understand what you're doing, or why.
Well that's what I'm saying: We can't know if it's being evil unless we know everything about it, and if we knew that, we'd be the supraintelligent beings in the equation. Thinking that it'll go off and dominate the world is thinking about the worst case. So why bother since it's not likely we'd be able to do much about it anyway?
Maybe there's a second AI on the same level as the first and thinks the first AI is evil. We're still dumb as rocks compared to them, but something certainly has that opinion.
Superintelligent AIs, like all computer programs, will do exactly as they're programmed to do. The problem is that computers do what you say, not what you mean. (Hence bugs.) So if you were to try to program a computer to "make human lives the best and most comfortable they can be", or something like that, it would be very difficult to actually specify that correctly. (Especially since it's a way more complicated, nuanced, controversial objective than "win at Go".)
That's why e.g. the Future of Life Institute's open AI letter is so important: http://futureoflife.org/ai-open-letter/ We need to be thinking in advance about how to solve the "value loading" problem for future AIs, and how to architect them so they can be deployed to solve big problems without being undermined by subtle but catastrophic bugs.
What is our purpose if computers can do everything better than us?
It feels like computers have taken one aspect of humanness: logic. Computers could do arithmetic, do algebra, play chess, and now they can play go.
It hurts because logic is usually thought to be one of the highest of human characteristics. Yes computers might never be able to replicate emotion, but even dogs have that.
There's still some aspects we have left to call our own. Computers perform poorly at language-based tasks. They can't write books, write math papers, compose symphonies. I hope it stays that way.
Turing test isn't just a natural language problem. It is far more complex and requires context awareness and emotional intelligence far beyond where we are currently. Language recognition has been at the forefront of research for at least 30 years and it has improved significantly. However, the turing test aspect has only minimally improved.
Edit: iopq, pretending to be a dumb human (or one with a language barrier) is cheating for a headline. A real Turing test would require a computer imitate a human for longer than 5 minutes (although currently that is plenty of time) and without any caveats or limitations on the computer's skill.
You're implying that if something can be outdone it doesn't have a purpose, which seems to rule out purposes for pretty much everything.
I'm sure there's always someone that can write books or maths papers or symphonies better than you. I don't think this robs you of purpose, unless your purpose is to be the absolute best at something.
Anyway, I find it curious that you would say logic is a quintessentially human trait, because humans are naturally quite bad at logic.
With Godel's incompleteness theorem in 1931, it was proven there is no perfect logic. He proved you can't formalize something even as simple as arithmetic -- any such attempt will always contain an infinite number of inconsistencies & paradoxes.
The difference between being outdone by another human and being outdone by a computer is that the computer's efforts are nearly infinitely reproducible, given the processing power.
So a more apt analogy would be if there was someone inside every cellphone who could write books, papers, or symphonies better than you. That day is coming.
First we would have to figure out our purpose even if computers couldn't do everything better than us. I don't think many people have answered this question. I believe the answer is something to do with love, reproduction, creation, and happiness.
Computers will be able to compose symphonies very soon. If DeepMind started working on this problem, I am sure that they would succeed. At least, we would have some innovative mashups of Beethoven, Mozart and Tchaikovsky. But training a powerful AI on a massive dataset of all popular and classical music should produce some extraordinary results. Especially if the dataset was given as MIDI with separate instrument tracks, so that an AI could learn how to write parts for different instruments, and how a song should be balanced. I actually think we are at the point where we have more than enough data to distill the essence of "good music", and generate an endless supply of great songs.
People have been doing his for decades, but as far as I'm aware, no-one has tried it with thousands of distributed servers and millions of songs.
Training it on popular music will at best make a machine that's really good at making music that humans enjoy. The really interesting breakthrough will be when a computer makes music for itself.
Composing an amazing symphony is probably about as hard as being the best go player in the world. But I think we're much further away than you think.
AlphaGo needed a training set of perhaps a billion games to be as good as it is. The dataset of master Go games is perhaps a million games. So AlphaGo played at tons of games against a half-trained version of itself to reach the billion game mark.
This doesn't work for songs, because there's no one to tell AlphaBach whether any of the billion symphonies it makes are any good. AlphaGo can just look at the rules and see if its move lead to a win, but there's no automatic evaluation function for music.
Perhaps the Matrix wasn't using the humans for power, but rather the computers wanted to get good at writing music, so they gave each human in it slightly different music and watched their emotional responses.
> Perhaps the Matrix wasn't using the humans for power, but rather the computers wanted to get good at writing music, so they gave each human in it slightly different music and watched their emotional responses.
This is possibly my favorite comment of the whole thread.
It's a super interesting idea and could make for some fascinating science fiction. Poorly programmed AI might not wipe out humanity, because it still needs humans to evaluate its fitness function.
don't you think that a team trying to build this could provide a free offering where users get free algo-generated music in return for 1-10 voting on a song-by-song basis. given enough time and votes, i suspect that the algo could get remarkably good at delivering satisfaction.
Reading this thread, I believe there's one aspect not discussed: in a battle between man and machine, it's debatable who wins and depends on the domain, but a man-machine combination always wins over both.
On emotions, that's a characteristic of life. With the consciousness we possess, without emotions we would quickly realize that life isn't worth living. I doubt that a "true AI", one with consciousness, will want to live without emotions. And about dogs, we haven't built anything as sophisticated yet ;-)
On AlphaGo, personally I'm not impressed. It's still raw search over the space of all possible moves, combined with neural networks and these techniques do not have the potential to yield human-level intelligence.
On logic, we have enough as to be able to build AlphaGo (also aided by computers and software that we've built, in a man-machine combination, get it?). Can a computer do anything resembling that yet? Of course not, because for now computers are just glorified automatons.
"Reading this thread, I believe there's one aspect not discussed: in a battle between man and machine, it's debatable who wins and depends on the domain, but a man-machine combination always wins over both."
It doesn't 'always'. Advanced chess is already dead, and judging from the pro commentaries, they currently are worse than useless in an 'Advanced go' setting. That may change, but given how much faster computer Go is reaching superhuman levels than computer chess, the 'Advanced go' window may have already closed.
It's not even close to raw search over the whole move space. AlphaGo searches fewer moves than Deep Junior did, and Go is a much larger game. Your premise is just wrong. AlphaGo is precisely so impressive because it operates much like a human does.
>Naches is a Yiddish term that means joy and pride, and it's often used in the context of vicarious pride, taken from others' accomplishments. You have naches, or as is said in Yiddish, you shep naches, when your children graduate college or get married, or any other instance of vicarious pride. These aren't your own accomplishments, but you can still have a great deal of pride and joy in them.
>And the same thing is true with our machines. We might not understand their thoughts or discoveries or technological advances. But they are our machines and we can have naches from them.
I hate to be cynical, but I'm sure many of my ancestors were also told to believe similar things. I know for certain that my grandparents and great grandparents believed that technology would create such progress that people of my generation would not have to work, and all would have leisure time.
AI is more likely to evolve into a tool to be used by the few to control the many.
Framed in the way that the article presents the data, then, yes, I guess, we do have more leisure time. Although, how much? The article cites that the number of hours worked per week (since 1900!!) has only dropped 1.4 hours a week. Or, extending it out and assuming that the trend is linear, the typical American can expect to finally have 100% leisure time sometime around 4516 AD.
But, then also consider that many of us are working (i.e. as in 'working for the man') many more hours than our parents did in fields that require significantly more focus, concentration, and mental energy. Even the article notes that people have been facing increasing stress and feelings of being rushed since 1900 and 1965.
Maybe you're in a cushy field. But, most people that I know only have time for 'zoning out' and recovery, rather than in pursuit of true leisure.
I agree. Lee lost a title 3-2 to Ke Je recently but he lost very convincingly against AlphaGo. Ke Je is not that stronger than Lee. He's going to lose too but he's 18 so I understand that he's feeling he can do anything.
My feeling is that AlphaGo could be a couple of stones stronger than the best pros. Considering that dans are capped at 9 but there are 9 dans that always win against some other 9 dans, the best pros could be 10 dan on their scale. That would also be 10 dan on the amateur scale (European, Americans are usually one stone gentler and Japanese much more than that). AlphaGo could be 12d amateur or, don't know, 14-15 dan pro? (pro levels are on a finer scale)
Do you know what the current limit is on a handicap that a professional player can overcome? Is there a known theoretical limit? That's a possible 'endgame' for go ai.
I'm not sure I'm understanding correctly but I believe some pros have done demonstrations against weaker amateurs (15k?) with something like 25 stones. Yes it'd be fun to see AlphaGo take pros with huge handicaps. And even inside handicaps there's alternatives. Like free placements vs just on star points. AlphaGo against a pro that starts with two stones in each corner could be neat. At some point I'd imagine the pro associations might balk at such games though.
There must be some limit because the number of the possible moves is finite. How many handicap stones the current professionals are from that limit... who knows. Apparently AlphaGo could set a better lower limit but it should play with many more pros, and start giving handicap if needed.
I wonder if Google will make it available to pros outside these official matches. It's not that Deep Blue played so much after winning with Kasparov in 1997, right? Kasparov asked a rematch and IBM refused. Deep Blue never played again.
Our computers are stronger than it now, mainly thanks to advances in the algorithms. Deep Blue used to crunch many more positions per second than the current chess engines do on standard PC. Still they're stronger. AlphaGo is running on 1920 CPUs and 280 GPUs, so either research trims down its algorithms or we wait for CPUs with 1024 cores. But at the current size they won't fit below the keyboard of a laptop :-)
That's just for the training phase. In play mode it only uses 40. And the top supercomputer has 3 million cores, so, AlphaGo is not such a big computer either.
No, I'm skeptical. And most importantly, due to how AlphaGo seems to be raising the bar just enough to lead confidently, I think Ke Je is in no position to judge how he would fare in advance. I mean, we have absolutely no idea whatsoever as for AlphaGo's true potential.
What happens if you give the human player a handicap? I wonder if the games are really as close as the commentators say, or if it's just a quirk of the MCST algorithm.
The great go champion Otake Hideo famously said that if he were to play go against God Himself, he would take only a three stone handicap, and if his life depended on it, he would take four. Alphago's not perfect, but it would still be very interesting to watch such a handicapped game.
Can it even play a handicapped game? I'm assuming the handicap means the other play can begin with several stones on the board. AlphaGo would have no training on such a configuration and would likely not know how to approach it.
Well the same thing was mentioned about Kos and game three showed that AlphaGo was very effective by both having few Ko threats and playing them when imperative. It also demonstrated effective gameplay as black with 4.4 & 4.3 opening.
Considering the large training data set, handicap games would certainly be included. It's a great way to test aggressiveness and the effectiveness to reduce the opponents' territory whilst building your own.
As an amateur, handicap games where you try to overcome 3 or 4 stones are some of the most educational experiences. It requires a lot more creative thinking and perseverance to come back from such a disadvantage.
I wonder what if you put the top 10 players in a room, team up and play with Alpha-Go. They are allowed to make one move within a 1-hour period and they can only play up to 8 hours a day. I wonder what the outcome would be.
Anyway, I think AlphaGo is a great training companion. I think Lee felt he's learning.
Finally, I also feel that while experience is crucial, the older generation would flush out by the younger generation every decade. I wonder if age really play a role in championship - not that AlphaGo isn't considered a 1000 years old "human" given it has played thousands of games already.
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[ 3.9 ms ] story [ 324 ms ] threadThis is our Deep Blue moment folks. a history is made.
This time the computer did not win out of pure bruteforce. Deep Blue relied on an opening book and massive computational power to explore the game tree. After the opening it was pretty much on its own, bruteforcing moves.
This technology used a neural network trained with hundreds of thousands of games which provided the pattern matching aspect, combined with the bruteforce move sequence reading, the montecarlo tree search... and 1200 CPUs + 600 GPUs.
Note though that AlphaGo almost certainly uses single precision arithmetic -- for neural networks even single precision is overkill.
Also the Top500 list is based on Linpack, which measures performance for computations that are pretty strongly interconnected across the different processors of the system. AlphaGo's Monte Carlo tree search problem is more embarrasingly parallel, with evaluation of different positions really being independent computations.
It is much easier to make systems that can handle embarrasingly parallel loads than the highly interconnected loads handled by the top500 supercomputers. So even though the flops are comparable, the systems are not.
While DNN+RL+Tree search is cool, the hardware requirements for AlphaGo to play at this level are staggering and only supported by large marketing budgets :)
[0]: https://youtu.be/qUAmTYHEyM8?t=1201
IBM lost in 1996, 2-4, and then won in 1997, 3.5-2.5. If they had played a third match with Kasparov, especially a longer match, it is not at all clear that they would have won.
Kasparov asked for a third match of 10 games, to be played over 20 days, but IBM would not give it to him.
Of course, you can change rules as much as you want and generate new marketing events. Personally, I would like a match where the AI is only allowed the energy a human uses. I guess, AlphaGo would lose with only 2000 kcal (2.3 kWh). Not sure about chess.
Pocket Fritz (HIARCS) was winning Grandmaster tournaments and got rated at 2800-2900 Elo - on a smartphone in 2009 using 1W worth of power without offloading to a GPU. The GPU in a more modern phone like the Samsung Galaxy S5 can crunch 140 GFLOPS - an order of magnitude more than Deep Blue (11 GFLOPS). Allowing 200W of power draw raises your computation power into the TFLOPS area (using a PS4, say). No contest - even without two decades of chess engine improvement over Deep Blue.
https://news.ycombinator.com/item?id=11271815 was indeed posted earlier. Unfortunately, youtu.be is banned because it is heavily favored by spammers. That's why that post didn't get seen.
We've assigned that video to the current submission (using the canonical youtube.com URL) since it was posted without a link.
The one solace was that Lee Sedol got his ko =) however, AlphaGo was up to the task and handled it well.
I definitely did not expect that.
Major credit to Lee Sedol for toughing that out and playing as long as he did. It was dramatic to watch as he played a bunch of his moves with only 1 or 2 seconds left on the clock.
That's on purpose to make full use of time and think about the next move
As a 3d amateur, I'm really curious about when he resigned. It really seemed like he was playing the position out to go for the win (or perhaps to see how AlphaGo would fare in ko). It didn't look like he was searching for a place to resign.
Edit: M5 was definitely played as a ladder breaker, so the above is correct.
A possible explanation is:
During self reinforcement learning, AlphaGo learned to minimize Ko potential by maximizing its probability of winning through diminished available Ko moves.
It would be interesting to see how AlphaGo would be able to capitalize on a game that emphasized Ko play, but that would take more time with AlphaGo to emphasize that kind of play.
edit: I'm not sure why, but I think Lee Sedol is partly holding back, or not playing at his maximum ability. It feels like these games are more along the lines of query games.
I look forward to the next two games because I'm 100% certain Lee Sedol is going to query the AI with some new queries.
So I'd be very surprised if that turns out to be the trick. Things that are hard for human players are not at all necessarily AlphaGo's weaknesses.
I think us humans made a critical error in that line of thinking.
It didn't avoid ko because of the risk of loss.
It avoided ko because of the lack of strategic win.
I'm no where near a strong player but it seems like AlphaGo is far ahead of Lee Sedol.
- It doesn't make mistakes due to pressure or fatigue; it's always playing at its sharpest
- It can read deeper than humans can
- It seems to have better judgement than most current professional players
But maybe it could have 40 of those moves to play an incredibly confusing game for humans. I think giving the machine a big handicap against pros might reveal it's true colours ...
If the player forces it to a fight, it will fight back, but that's only because losing the fight will definitely lose the game.
Would StarCraft be the last game AI has to beat?
After that... then we have games that involve physical movement. Although I'm not sure what would be fair for strength & speed of robots vs humans. I'm imagining a team of robot soccer players, for example.
The from playing Call of Duty to controling a robot with a gun in the real world it's a small step :)
With an AI, if we base it on pixels, what they will do is spin around 360 degrees about 60 times a second while moving forward so that it can maximize all of the pixels inputted at a time. It would compare it against the stored level design and shoot at anything that is off (or against character models). I can just see that it's not going to behave like anything we'd expect.
If you look for an RTS game, StarCraft is an obvious candidate. It has a relatively active community of AI writers and AI competitions, it's a classic of competitive play, ...
Real-time games are hard to compare, because the interface really makes a difference. Does the AI get to talk directly to the game engine? Or does it have to use screen capture, understand the image and then use a robot arm to press keys, or something in between? If it gets to see precise positions and to input commands directly, even relatively "simple" programs can do crazy micro tricks and get massive advantages that way, even if their general macro-strategy is bad. If you do the other way you need image analysis and robotics, Google should have the tech for both ;)
https://www.youtube.com/playlist?list=PLqYmG7hTraZA7v9Hpbps0...
(It also includes the videos of the first 2 matches)
Game 1: https://www.youtube.com/watch?v=6ZugVil2v4w
Game 2: https://www.youtube.com/watch?v=EitoPhtGWJQ
Game 3: https://www.youtube.com/watch?v=CkyVB4Nm9ac
I recall Andrew mentioning that Myungwan will be back to comment games 4 and 5?
At some point it figured that the Ko fight at the bottom was already won. Hence that white move at the top which nobody saw coming.
Another interesting moment was when Michael Redmond said "A human would typically not spend too much time thinking on this obvious move". This was the move on the right-hand side somewhere. What this tells me is that human players rush through some moves because they seem obvious but since Alpha-Go is a machine, it does not care about obvious and non-obvious. It's calculating the entire board through to the end and is not interested in "local fights".
To some relatively small depth, right? I hear the estimate that all possible moves in a Go game probably can't be physically represented in the universe (unless we learn much more about the structure of games' evolution).
This objective function is why Go playing AI jumped hugely in the last 10 years.
Also, that forced move it's very obvious to us that it's forced, but AlphaGo might not have this concept.
Also I'm not entirely sure how AlphaGo's time management works, but it's doing the same thing for every move—populating the game tree as deeply and intelligently as it can. It may just look for "30 seconds" on every move and then take the best bet meaning it's a more thorough and exhaustive reader than any human.
It's certainly a feature of the best Magic: the Gathering pros, for example - their play is marked by the cards they play around, even when seemingly far ahead.
[1] komi is a number of points that is added to white's score to compensate for the disadvantage of moving second. When a non-integer komi is used, such as in this match, you cannot have a tie because scoring on the board is always integral.
https://en.wikipedia.org/wiki/Peak_Performance_(Star_Trek:_T...
Humans have a tendancy to want to win the battle, or to get too focus in a local area. I think that's a way AlphaGo is coming up with an extra move here or there which is making a difference in the fight later.
(or something like that)
Politicians fool countries delivering empty promises about better health, education and security. A supraintelligent AI could promise making humans rich, healthy and powerful, to then break its promise and dominate the world.
https://en.wikipedia.org/wiki/AI_box
Basically, why would an AI want to dominate the world? Humans would have to both very stupidly give the AI values that encourage it to dominate the world and very luckily (or unluckily) give it values that actually converge to a horrible outcome against human intentions by random chance (since the AI designers certainly won't be tuning the value set for that outcome).
Humans are going to program their AI's to try to make as much money as possible. Many corporations are already mindless and reckless amoral machines that relentlessly try to optimize profits despite any externalities. Try to imagine Exxon, Wal-Mart, and Amazon run by an intelligence beyond human understanding or accountability.
Ha, if that were true we wouldn't be constantly extending the law and putting people in jail because they keep breaking the law for profit.
>ivilisation can't work because humans will want to make as much money as possible
And yet we keep running into issues with long term pollution and environmental degradation because of the growth of civilization.
>whether they feel they already have sufficient money for their own needs,
Does greed have bounds?
The problem is when you have multiple AIs. Then same evolutionary principles apply. Paranoid and self-sustaining AIs survive, and the circle goes on...
World domination is a similar situation. For any goal you give an AGI, one of the big risks that may prevent that goal from being accomplished will be the risk that humans intervene. Humans are a big source of uncertainty that will need to be managed and/or eliminated.
Of course, a cat is not conscious. But compared to an AI, we might also be considered pretty low consciousness beings, or at least beings in front of which you don't justify yourself.
How do you know?
If you take the view that consciousness somehow arises from the brain and neural connections (which is intuitively plausible, but I personally am skeptical), it stands to reason that other species with complex brains are conscious as well. Perhaps "less conscious" (if that means anything) in proportion to how much less complex their brains are.
Now experts can discuss details or semantics, but do you truly suggest cats might be conscious?
In particular, we all know that we're conscious, but can't really explain what that means.
Arguing they are not conscious in the sense of a more obscure definition is a bit pointless unless you specify your definition.
Why do you believe this? I don't like cats, but I wouldn't argue that they're not conscious.
Instead of debating the suitcase word "conscious", let me ask: 1) Do you believe that toddlers are conscious? 2) Is there a more precise way to state your belief that doesn't use the word "conscious"?
If some unforeseen event occurred and I had to abandon my cat, thereby breaking my promise that I would take care of her, I would definitely feel guilty about it.
Of course, a cat is not conscious.
Either this is a nonstandard definition of "conscious", or you haven't met many cats.
Or it could devote its entire power to making human lives the best and most comfortable they can be because humanity is some super-precious resource in the universe and it feels it's unimportant because it's just a bunch of silicon and electrons.
Supraintelligent AI being evil is FUD imho because we can't reason about supraintelligent AI.
There is a difference between being evil and incomprehensible intelligence. You are not being evil when you accidentally step on an ant or dig up an ant-hill to build your shed. The ants won't be able to understand what you're doing, or why.
Maybe there's a second AI on the same level as the first and thinks the first AI is evil. We're still dumb as rocks compared to them, but something certainly has that opinion.
That's why e.g. the Future of Life Institute's open AI letter is so important: http://futureoflife.org/ai-open-letter/ We need to be thinking in advance about how to solve the "value loading" problem for future AIs, and how to architect them so they can be deployed to solve big problems without being undermined by subtle but catastrophic bugs.
It feels like computers have taken one aspect of humanness: logic. Computers could do arithmetic, do algebra, play chess, and now they can play go.
It hurts because logic is usually thought to be one of the highest of human characteristics. Yes computers might never be able to replicate emotion, but even dogs have that.
There's still some aspects we have left to call our own. Computers perform poorly at language-based tasks. They can't write books, write math papers, compose symphonies. I hope it stays that way.
Edit: iopq, pretending to be a dumb human (or one with a language barrier) is cheating for a headline. A real Turing test would require a computer imitate a human for longer than 5 minutes (although currently that is plenty of time) and without any caveats or limitations on the computer's skill.
I'm sure there's always someone that can write books or maths papers or symphonies better than you. I don't think this robs you of purpose, unless your purpose is to be the absolute best at something.
Anyway, I find it curious that you would say logic is a quintessentially human trait, because humans are naturally quite bad at logic.
So a more apt analogy would be if there was someone inside every cellphone who could write books, papers, or symphonies better than you. That day is coming.
People have been doing his for decades, but as far as I'm aware, no-one has tried it with thousands of distributed servers and millions of songs.
AlphaGo needed a training set of perhaps a billion games to be as good as it is. The dataset of master Go games is perhaps a million games. So AlphaGo played at tons of games against a half-trained version of itself to reach the billion game mark.
This doesn't work for songs, because there's no one to tell AlphaBach whether any of the billion symphonies it makes are any good. AlphaGo can just look at the rules and see if its move lead to a win, but there's no automatic evaluation function for music.
Perhaps the Matrix wasn't using the humans for power, but rather the computers wanted to get good at writing music, so they gave each human in it slightly different music and watched their emotional responses.
This is possibly my favorite comment of the whole thread.
It's a super interesting idea and could make for some fascinating science fiction. Poorly programmed AI might not wipe out humanity, because it still needs humans to evaluate its fitness function.
don't you think that a team trying to build this could provide a free offering where users get free algo-generated music in return for 1-10 voting on a song-by-song basis. given enough time and votes, i suspect that the algo could get remarkably good at delivering satisfaction.
On emotions, that's a characteristic of life. With the consciousness we possess, without emotions we would quickly realize that life isn't worth living. I doubt that a "true AI", one with consciousness, will want to live without emotions. And about dogs, we haven't built anything as sophisticated yet ;-)
On AlphaGo, personally I'm not impressed. It's still raw search over the space of all possible moves, combined with neural networks and these techniques do not have the potential to yield human-level intelligence.
On logic, we have enough as to be able to build AlphaGo (also aided by computers and software that we've built, in a man-machine combination, get it?). Can a computer do anything resembling that yet? Of course not, because for now computers are just glorified automatons.
It doesn't 'always'. Advanced chess is already dead, and judging from the pro commentaries, they currently are worse than useless in an 'Advanced go' setting. That may change, but given how much faster computer Go is reaching superhuman levels than computer chess, the 'Advanced go' window may have already closed.
There is only selection.
Meat is just a phase.
Don't worry about the machine. Even in Star Trek TNG, Data can outperform everyone in every task, but was never truly happy!
Use the computers to engineer ourselves to "superhuman" capabilities.
You think we have one now?
"Naches" from our Machines https://www.edge.org/response-detail/26117
>Naches is a Yiddish term that means joy and pride, and it's often used in the context of vicarious pride, taken from others' accomplishments. You have naches, or as is said in Yiddish, you shep naches, when your children graduate college or get married, or any other instance of vicarious pride. These aren't your own accomplishments, but you can still have a great deal of pride and joy in them.
>And the same thing is true with our machines. We might not understand their thoughts or discoveries or technological advances. But they are our machines and we can have naches from them.
AI is more likely to evolve into a tool to be used by the few to control the many.
But, then also consider that many of us are working (i.e. as in 'working for the man') many more hours than our parents did in fields that require significantly more focus, concentration, and mental energy. Even the article notes that people have been facing increasing stress and feelings of being rushed since 1900 and 1965.
Maybe you're in a cushy field. But, most people that I know only have time for 'zoning out' and recovery, rather than in pursuit of true leisure.
My feeling is that AlphaGo could be a couple of stones stronger than the best pros. Considering that dans are capped at 9 but there are 9 dans that always win against some other 9 dans, the best pros could be 10 dan on their scale. That would also be 10 dan on the amateur scale (European, Americans are usually one stone gentler and Japanese much more than that). AlphaGo could be 12d amateur or, don't know, 14-15 dan pro? (pro levels are on a finer scale)
I wonder if Google will make it available to pros outside these official matches. It's not that Deep Blue played so much after winning with Kasparov in 1997, right? Kasparov asked a rematch and IBM refused. Deep Blue never played again.
Our computers are stronger than it now, mainly thanks to advances in the algorithms. Deep Blue used to crunch many more positions per second than the current chess engines do on standard PC. Still they're stronger. AlphaGo is running on 1920 CPUs and 280 GPUs, so either research trims down its algorithms or we wait for CPUs with 1024 cores. But at the current size they won't fit below the keyboard of a laptop :-)
Considering the large training data set, handicap games would certainly be included. It's a great way to test aggressiveness and the effectiveness to reduce the opponents' territory whilst building your own.
As an amateur, handicap games where you try to overcome 3 or 4 stones are some of the most educational experiences. It requires a lot more creative thinking and perseverance to come back from such a disadvantage.
Anyway, I think AlphaGo is a great training companion. I think Lee felt he's learning.
Finally, I also feel that while experience is crucial, the older generation would flush out by the younger generation every decade. I wonder if age really play a role in championship - not that AlphaGo isn't considered a 1000 years old "human" given it has played thousands of games already.