> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board.
This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos.
Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.
Give the same challenge to DeepMind’s “superhuman” game-playing machine and it will be an absolute patzer.
This is an obvious indicator that the state of the art in so-called “machine learning” doesn’t involve any actual learning in the way it is normally applied to intelligent systems like humans or animals.
I am continually amazed by the failure of otherwise exceedingly intelligent tech people to grasp this problem.
Try learning to ride bike with inverted steering, try to navigate world with your vision flipped over or use your non-dominant hand to do things that you normally do. Well, try to write on Azerty keyboard if you are Qwerty native (really, fk Azerty :P).
Humans are also not a general intelligence.
In certain sense Deep Reinforcement Learning is actually more general than human intelligence. For example, when playing games you can remove certain visual clues. It makes it almost impossible to play for humans, while Deep RL scores will not even budge. It means that Deep RL is more general, because it does not relay on certain priors, but it also makes it more stupid in narrow domain of human expertise. Try this game to see yourself: https://high-level-4.herokuapp.com/experiment
Human brains are amazing, but they also require certain amount of time to retrain when inputs/outputs are fundamentally changed.
PS. I didn't hear about anyone testing different board sizes with AlphaZero-esque computer players. But I saw Leela Zero beating very strong humans, when rules of the game were modified so that that the human player could play 2 additional moves: https://www.youtube.com/watch?v=UFOyzU506pY
This is true too, we are adapted to our environment, in particular the things that we do automatically (System I).
Playing chess well is a combination of both conscious and unconscious skills. However when deep learning systems play, it is all the unconscious, automatic application of statistical rules. They are playing a very different game from the human chess game.
Because there is no abstract reasoning involved here, these systems cannot apply the lessons learned from chess to another board game, or to something completely different in life, which humans can and do. So even though they are much stronger than human players, they aren't strong in the same way.
>Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.
Interesting. Has this actually been shown? I would assume a lot of the strategies a human is familiar with would fall apart as well. I'm no chess or go player but I would have to learn new strategies in a tic-tac-toe game scaled to 10x10. I would certainly not be as proficient although I would still consider myself to have intelligence.
Almost all of the human strategies and concepts would still apply: center control, square control, development, initiative, king safety, the opposition, etc. The only exceptions would be fringe concepts like opening theory (already moot in Chess960) and endgame edge cases.
If you’re still not convinced,
I’ll prove that skills transfer by playing bullet against anyone who can make a 10x10 variant playable online.
I'm pretty sure this isn't how chess masters actually play chess. That'd be too slow and error prone. They pattern match very heavily in the beginning and towards the end. And all those patterns would be wrong on a larger board with more chess pieces. At least for chess.
I'm not a chess master myself, my knowledge of this topic is from what I've read of Kasparov. The guy is all about patterns. He even advises to play using a physical board as much as possible, to improve _visual_ recognition of patterns. Thing is, you might not even recognize this as "pattern recognition" per se. Call it "intuition", call it "experience", or whatever you like, but combinatorially I'm pretty sure you're not searching the entire tree of possible positions several moves ahead - that's literally impossible to do for a human to do in the finite time allotted to a game. You're relying on patterns to constrain the search, much like a modern neural algorithm would constrain its search using a cost function. That's what's meant by "pattern recognition" here, not rigid recognition of fixed positions. That is also combinatorially impossible for a human to precisely memorize.
"Center control", "square control", etc. all sound to me like things discovered by pattern matching. Yes, the patterns are large and somewhat abstract, but they're still patterns.
How? Remember, there'd have to be more pieces, and the new pieces could have different moves. The game would also be dramatically more difficult, combinatorially.
Chess is already very difficult, combinatorially. Chess players learn patterns with the current pieces. Those patterns include patterns that work both locally and globally. When learning a particular pattern, chess players typically can generalize that pattern to other cases. (This is what tactics training is all about. You're almost never going to find the exact same pattern that you learn in tactics training in the real world but tactics training will help you recognize similar patterns.) New pieces are going to give you new patterns you have to learn. But ranks, files, and diagonals are not going away. New pieces will likely be a bishop-knight combo piece and a rook-knight combo piece as in Seirawan chess. Therefore, they will still have common patterns you can recognize. The larger board is not a non-issue but it's not anywhere near as drastic a change as you're making it out to be. Most tactics in chess don't make use of the fact that the board is 8x8 rather than 10x10. They'll work in both boards.
I'm not saying I'm definitely going to do this, but is there a rulebook somewhere for 10x10 chess? (What are the initial piece positions, and how would castling work?)
I’m not aware of 10x10 chess ever being attempted, let alone codified. Here’s my suggestion: add another set of bishops (or knights, or one of each) in between rooks and king/queen. Castling works the same, king goes around rook.
Former world chess champion Capablanca suggested a 10x8 board with two additional pieces in the 1920s, but there have been many variants proposed earlier and later [0]. Grand Chess [1] is the most known 10x10 variant, also with 2 additional pieces and a different start position, castling not allowed. See wikipedia for links to programs implementing these rules.
Interesting. Note that both of those types involve introducing new pieces. I would argue that this changes the fundamental nature of the game in a way that increasing board size alone doesn’t. The reason is that I (as a human chess master) would need to retrain myself to learn the new piece movements.
What I really want is a 10x10 or even 8x10 board using the original set of pieces. This would be sufficient to prove that human chess masters can adapt in a way that machine-learning based algorithms cannot.
Wouldn't adding more of the existing pieces in the back row change the game a bit too? Which piece(s) would you suggest having more of to accomodate the extra fields?
It would change the game but not enough to reduce the performance gap between an expert and a novice. See my grandparent comment for a specific suggestion on which pieces to use.
Generalized NxN chess is a thing I've seen talked about in complexity theory. I'm not sure if anyone has actually made a proper set of rules for it though. It looks like they often just don't care about such trivialities as starting positions (and probably castling). E.g. http://www.ms.mff.cuni.cz/~truno7am/slozitostHer/chessExptim...
Why stick to chess? Magic: the Gathering is a game that is played with cards with printed rules text that describes how a card should be played. The set of cards is constantly updated with a few hundred new cards introduced at least twice a year (although games are often played with only a subset of all cards).
Despite the constant change of the card pool, and also the wording of the rules text on the cards, and the rules themselves, human players are perfectly capable of "picking up a card they've never seen before and playing it" correctly.
Perhaps a better example than 10x10 chess would be bughouse chess [1]. That's a chess variant played between two teams of two players using two sets and two clocks. It's a common break activity between rounds at amateur chess tournaments. Human chess players of all levels pick it up pretty fast after they play a handful of games.
I think a lot of AI research now is very narrow, but this ignores that there's also a lot of research in RL/etc that's working to solve the problem of generalization.
Meta learning is for solving similar problems from a distribution (like different sized boards in your chess example) and has taken off recently (only baby steps so far though). Modular learning is also becoming big, where concepts that are repeatedly used are stored/generalized.
Of course if you mess with a function's inputs in ways it's never seen it's going to "not understand" what's going on. This is an agent which only knows 8x8 space.
Train it on variable spaces, and you'll get an agent that can play on variable spaces. In fact, you can probably speed things up drastically by using transfer learning from a model which already learned 8x8 space and modifying the inputs and outputs to match the new state and action space.
What part of this do you think "exceedingly intelligent tech people" aren't grasping? Something qualitative? Do you think people in machine learning think of "learning" as literally meaning the same thing as the colloquial usage? What, precisely, are you attacking here? All the harsh anti-machine-learning viewpoints with no clarity are becoming exhausting.
The GP described the "hype machine" and the implication that deep learning is step to AGI. As far as I can tell, the "hype machine" is real in sense that popular articles describe current methods as steps towards our broad concept of intelligence.
Certainly, someone close enough to the technical process of deep learning will admit that it essentially an extension of logistic regression without any "larger" implications - at least some deep learning researchers are always clear to distinguish the activity from "human intelligence" (and even if a given research never parrots the hype train's mantra, they know it's there and inherently play some part).
But more a minimum assertion of deep learning is that it "generalizes well". And what does "well" mean in this context? In the few situations where data can be generated by the process, like Alpha-Go, it can make a good average approximation of a function but in most situations of deep learning it means "generalizes like a human" - especially image recognition.
This comes together in the process of training AIs. Researchers take data that they hope represents a pattern of inputs and output in a human decision making process and assume they can construct a good approximation of a function that underlies this data. A variety of things can go wrong - the input data can be selective in ways the researchers don't understand (there was a discussion about a large database of images from the net being biased just by the tendency of photographers to center their main subject), there can be no unambiguous "function" - loan/parole AI that's inherently biased because it associated data that isn't legitimate, objective criteria for the decision sought), and so-forth. Some tech people are aware of the problems here to but this stuff is going out the door and being used in decisions affecting people's lives. Merely noting possible problems isn't enough here. These "exceedingly smart people" are still handing off their creations to other people are taking them as something akin to miraculous decision makers.
The question we should be asking is how much retraining had to occur to accomplish the new task? If it's significantly less than to the accomplish the original task, the algorithm has transferred its latent knowledge from the original task to the new task, which is significant.
Humans have orders of magnitude more neurons, more complicated neurons, more intricate neural structures, and their training data is larger and more varied.
Well if arguments by reduction are on the table then "learning" is just a fancy way to say "complex chemical process in the hippocampus that we don't understand that well".
Agreed except for training data is larger. Training data is often far smaller for humans. You probably saw a few cats before generalizing and understanding what a cat looks like. A neural net might require hundreds of thousands if not more samples to be a robust classifier for cats. AlphaGo et al look at tens of millions of games, humans look at a small fraction.
This doesn't have much to do with the algorithms, and is more to do with the engineering decisions that went into AlphaGo and AlphaZero. They are designed to play one combinatorial game really well. With a bit of additional efffort and a lot of additional compute, you could expand the model to account for multiple rule / scale variations, maybe even different combinatorial games.
I think it's quite important to look at the distinction between the actual agent in play and the learning algorithm used.
The learning algorithm AlphaGo uses is somewhat general, and can handle different games (e.g. you can put chess or Go through the algorithm and it functions well for either).
The output of this algorithm, however, is a specialised agent. The agent is not general. If I create a chess agent and give it Go or chess with different rules, it will perform very poorly.
Creating general learning algorithms is arguably a somewhat easier task than creating a general agent, since learning algorithms are typically run for a long time while an agent often has to make time constrained decisions.
The holy grail of AGI is to make the learning algorithm and the agent the same thing, and have them be general. Then you have an agent which can rapidly adapt to its environment and self-modify as needed. We are still a long way off a system that would do this in terms of current research.
The distinction you’re making between agent and algorithm is meaningless for the point I was trying to make, which is that the only connection between this DeepMind research (agent, algorithm, whatever) and AGI have in common is the word “general”.
Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.
> Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.
But the point I was making is precisely that the "general learning" tech is in fact somewhat general. AlphaGo and certainly AlphaZero's learning tech generalises to Go, chess, and a few other games. That's relatively general in the domain of board games, in my humble opinion.
The reason this isn't close to AGI is because it's not the agent doing the learning, and so while a relatively general learning algorithm produces the agent, the agent itself is not general even in the field of board games.
You appear to be completely missing the point of my root comment, which is that AlphaGo’s tech isn’t nearly as general as it’s made out to be, even if you stick to Go.
> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board.
It doesn’t even generalize to the same game with a different board shape. Whereas a human Go master could easily do so.
DeepMind is essentially hacking the common usage of the word “general” in order so that they can make claims about “general” intelligence. And it’s working!
But the training process does generalise. The same training process produces an agent that works on a 19x19 board, or a standard Go board, or even a game of chess.
How is that not general? Sure it doesn't work for all problems but in the domain of board games it definitely feels very general.
The agent the training algorithm produces may not be general, but out of what I've read I've only ever seen DeepMind claim generality of the learning algorithm, not the agent.
I think the GP was noting the problem that AI can easily encounter situations beyond what it was designed and simply fail while human intelligence involves a more robust combination of behaviors and thus humans can generalize in a much wider variety of situations.
If the system designer has to know the parameters of the challenge the system is up again, it should be obvious you can always add another parameter that the designer didn't know about and get a situation where the system will fail. This is much more of a problem in "real world situations" which no designer can fully describe.
This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos.
Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.
Give the same challenge to DeepMind’s “superhuman” game-playing machine and it will be an absolute patzer.
This is an obvious indicator that the state of the art in so-called “machine learning” doesn’t involve any actual learning in the way it is normally applied to intelligent systems like humans or animals.
I am continually amazed by the failure of otherwise exceedingly intelligent tech people to grasp this problem.