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Art isn't an easier problem than math. An artbot would have sounded more sci-fi than a mathbot only 2 years ago. Yet it only took the AI world 1.5 years to go

Art isn't an easier problem than math. An artbot would have sounded more sci-fi than a mathbot only 2 years ago. Yet it only took the AI world 1.5 years to go from drawing child scribbles to replicating top artists with like 90% similarity (I can barely tell the difference between AI and human drawn art anymore with the new NovelAI model). It won't be long before AI starts to go superhuman in art skills. It won't take long from a school-math model to math olympiad model (I'd say 1 year is enough), and going to unsolved conjectures won't be that long either (2-3 years?). We know from AlphaGo that its possible to make AI systems far superhuman at solving some abstract math problem.

RandomLensman 3y ago
AlphaGo solved Go?
- anonylizard 3y ago
  "Superhuman at solving" != "solved". AlphaGo didn't solve go (Ie, can the first mover guarantee a win?). However, it understood go at a far, far superior level to any human. A mathbot doens't have to solve math in general. It merely has to be better at solving math than any human mathematician to be considered ASI. And it only has to be better than the 'average' human mathematician to be extremely useful in accelerating math research.
  - RandomLensman 3y ago
    What mathematics was AlphaGo solving? What do you mean by solving there?
    - dimask 3y ago
      Proving that the first player has a winning strategy, or the optimal strategy for both players leads to draw.
      - falcor84 3y ago
        Just to nitpick, while unlikely, it is theoretically possible that the second player has a winning strategy
        k2enemy 3y ago
        To nitpick a little further, it actually is not possible that the second player has a winning strategy. For that to be the case, P2 would need a winning path of play no matter what P1's first move is. Suppose that P1 passes on their first move (which is a valid move). Then P2 has a winning path of play in which they put down the first stone. But P1 could have made that move and then they would be on the winning path.
        falcor84 3y ago
        Good point, I actually wasn't that they could pass the first move
        godelski 3y ago
        I'm not a game theorist, in RL, nor a big Go player; but I am having a hard time finding this argument convincing. Isn't the whole reason Go is impressive is because the enormous set of possible moves? Like we know that no computer could run ever game in the lifetime of the universe were it to even perform millions of moves a second. So of the I understand this number to be north of 10^500 for possible legal and playable games. So the difference of 1 doesn't seem meaningful. Is there something I'm missing or a more convincing argument? Because even if player 1 always locks out 90% of those possible future moves, that's still an absurdly large search space and it doesn't seem like it is meaningfully different.
        recursivecaveat 3y ago
        It's a proof by contradiction, like the halting problem proof. It doesn't rely on the actual playable games at all, but what the existence of a winning strategy would imply. If there was a guaranteed winning strategy for player 2 it would be contradictory because player 1 could execute it by passing their first turn then using the winning P2 strategy. In that game both players can't be guaranteed to win, so there must be some flaw in P2's supposed guaranteed win strategy. https://en.m.wikipedia.org/wiki/Strategy-stealing_argument https://en.m.wikipedia.org/wiki/Strategy-stealing_argument
  - tromp 3y ago
    > solve go (Ie, can the first mover guarantee a win?). Solving Go means determining by what margin the first player can win, or equivalently, at what komi for white the game is a theoretical draw. That also makes it dependent on the exact rule set used. E.g. 2x2 go is a +1 first player win with the Tromp-Taylor rules of Go, while the Japanese rules are not even sufficiently formalized to allow scoring a 2x2 game.
ekianjo 3y ago
> Art isn't an easier problem than math Not sure. Art is about approximate pattern recognition and if you have a large enough dataset it seems that you can definitely reproduce some of that. For math... it involves consistent reasoning from A to Z - which does not allow for any kind of mistake in the way. In Art you won't feel too bad if the shadows or the lights are a little weird or if a character has 7 fingers instead of 5 on one hand, but this kind of mishaps break everything in Math.
- anonylizard 3y ago
  Fingers are already mostly solved. The latest models draw hands better than most human artists. I think artists would disagree with your assessment of 'approximate pattern recognition'. Its more like： 1. Given a set of words describing what the user wants. 2. Arrange pixels in a grid 3. That maximizes the user rating On one hand it is tolerant of small errors. On the other hand its an extremely broad problem. Also to get a good user rating, it has to do 99 things right for every 1 thing it draws wrong.
  - larodi 3y ago
    Well this thing about fingers, etc in drawings. Lets put it this way - for us mere mortals the generative images look very much okay. To artists and people who actually draw something, well ... they very often spot inconsistencies in the whole production, including how fingers, arms, overall body posture, etc is presented. So it is exactly what we can expect - good enough on average, but actually a mediocre result of commonality. Thing is the wide audience chews in mediocrity all the time, and nobody seems to have been able to change this for ages...
    - disgruntledphd2 3y ago
      It's a weighted average over the prompts and data. Mediocrity is exactly what we'd expect from such an approach. Now, if we end up seeing mastery, that would be extremely interesting.
      - 3y ago
japoco 3y ago
I think art is much easier for LLM-style AI models to do compared to writing. To make a nice picture you just need to place pixels near each other in a way that looks good, and we all know LLMs are phenomenal at this. Good text on the other hand is not just text that has a good flow and fits the prompt. It must follow a line of thought, and LLMs don’t do that by design, even though we could argue wether they have that capability as an emergent one, but I don't believe that at all.
jszymborski 3y ago
> Art isn't an easier problem than math. Someone can tell you when a math problem is solved. Someone else can't tell you when you've successfully art'd with remotely the same degree of confidence. In so far as they can, however, many experts claim that AI cannot, indeed "do an art". Also, it's easier to formulate a hard math question (there are plenty of unsolved problems), but that's (IMHO) harder to do for art. Sure, you may think this is the first time the phrase "Astronaut riding a Llama and holding an avocado" was writ, but those are all well represented concepts in the dataset. For more abstract prompts, there really isn't a way to verify "correctness".
jack_riminton 3y ago
NovelAI isn't for drawing it's for writing, did you mean something else?
dxbydt 3y ago
> Art isn't an easier problem than math. If I was asked to draw a. the emptiness in your heart b. the lack of furniture in your room c. your empty bank balance d. starvation e. object returned by a python function with no return ... I could just submit an empty sheet of paper, & an artist would argue that my empty sheet of paper represents any/all of the above. Now, if I turn in the same empty paper at a math qualifier and argue that it represents the infinite set of real and complex numbers, ergo the answer to the posed qual problem must be in there, I'll get kicked out of that phd program in a jiffy.
- civilitty 3y ago
  > I could just submit an empty sheet of paper, & an artist would argue that my empty sheet of paper represents any/all of the above. And they'd be taken about as seriously as the ads taped to a urinal in a Museum of Modern Art washroom.
godelski 3y ago
> Art isn't an easier problem than math. In a way it is, in a way it isn't. You have to remember what is easy for machine isn't going to correlate to what is easy for us humans. Look at AI art. Closely. No, closer than that. All the detail is fucked up. Not just the hands, but the tiniest of things. Strokes, lighting, reflections, and consistency, and all that. But can I turn my friend into a convincing werewolf? Yes. Can I turn my cat into a human or Wonder Woman? No. The system isn't a "fancy copier" but it is a compression algorithm and the aforementioned tasks were only possible because lots of work training LoRAs, textual inversions, control nets, and so on (you could seriously improve GANs, VAEs, hell, even Boltzman Machines could probably do pretty well were any of these given the same research investment that diffusion has received. GANs come close but nuances like GANs having a magnitude fewer parameters). But let's look at math, can I consistently add numbers? No. The problem is that in math, all those tiny intricate details matter. Not only that, they matter at every single step. The thing here is that these are still pattern recognition machines. But they aren't generalized machines. You can't really derive out all of math from probability distributions (or at least cleanly, but still not convinced you can). The thing is that for math to work in AI we have to address the elephants in the room: math. Yeah, math. ML people don't like it. But we gotta address the axioms in the room that we're operating under. How do we move on from machines operating on manifolds? How do we make it so data are not distributional? How do we move away from a number of unmentioned axioms remains a large open problem in AI research. One that does not get anywhere serious enough of a conversation, especially within the community. Sure, maybe transformer circuits can learn some addition by learning how to do FFTs and add in the FFT space, but you're not going to get to Abstract Algebra that way. Ideally the AI can solve problems that have no algorithms, pun intended.

RandomLensman 3y ago

AlphaGo solved Go?

anonylizard 3y ago
"Superhuman at solving" != "solved". AlphaGo didn't solve go (Ie, can the first mover guarantee a win?). However, it understood go at a far, far superior level to any human. A mathbot doens't have to solve math in general. It merely has to be better at solving math than any human mathematician to be considered ASI. And it only has to be better than the 'average' human mathematician to be extremely useful in accelerating math research.
- RandomLensman 3y ago
  What mathematics was AlphaGo solving? What do you mean by solving there?
  - dimask 3y ago
    Proving that the first player has a winning strategy, or the optimal strategy for both players leads to draw.
    - falcor84 3y ago
      Just to nitpick, while unlikely, it is theoretically possible that the second player has a winning strategy
      - k2enemy 3y ago
        To nitpick a little further, it actually is not possible that the second player has a winning strategy. For that to be the case, P2 would need a winning path of play no matter what P1's first move is. Suppose that P1 passes on their first move (which is a valid move). Then P2 has a winning path of play in which they put down the first stone. But P1 could have made that move and then they would be on the winning path.
        falcor84 3y ago
        Good point, I actually wasn't that they could pass the first move
        godelski 3y ago
        I'm not a game theorist, in RL, nor a big Go player; but I am having a hard time finding this argument convincing. Isn't the whole reason Go is impressive is because the enormous set of possible moves? Like we know that no computer could run ever game in the lifetime of the universe were it to even perform millions of moves a second. So of the I understand this number to be north of 10^500 for possible legal and playable games. So the difference of 1 doesn't seem meaningful. Is there something I'm missing or a more convincing argument? Because even if player 1 always locks out 90% of those possible future moves, that's still an absurdly large search space and it doesn't seem like it is meaningfully different.
        recursivecaveat 3y ago
        It's a proof by contradiction, like the halting problem proof. It doesn't rely on the actual playable games at all, but what the existence of a winning strategy would imply. If there was a guaranteed winning strategy for player 2 it would be contradictory because player 1 could execute it by passing their first turn then using the winning P2 strategy. In that game both players can't be guaranteed to win, so there must be some flaw in P2's supposed guaranteed win strategy. https://en.m.wikipedia.org/wiki/Strategy-stealing_argument https://en.m.wikipedia.org/wiki/Strategy-stealing_argument
- tromp 3y ago
  > solve go (Ie, can the first mover guarantee a win?). Solving Go means determining by what margin the first player can win, or equivalently, at what komi for white the game is a theoretical draw. That also makes it dependent on the exact rule set used. E.g. 2x2 go is a +1 first player win with the Tromp-Taylor rules of Go, while the Japanese rules are not even sufficiently formalized to allow scoring a 2x2 game.

ekianjo 3y ago

> Art isn't an easier problem than math Not sure. Art is about approximate pattern recognition and if you have a large enough dataset it seems that you can definitely reproduce some of that. For math... it involves consistent reasoning from A to Z - which does not allow for any kind of mistake in the way. In Art you won't feel too bad if the shadows or the lights are a little weird or if a character has 7 fingers instead of 5 on one hand, but this kind of mishaps break everything in Math.

anonylizard 3y ago
Fingers are already mostly solved. The latest models draw hands better than most human artists. I think artists would disagree with your assessment of 'approximate pattern recognition'. Its more like： 1. Given a set of words describing what the user wants. 2. Arrange pixels in a grid 3. That maximizes the user rating On one hand it is tolerant of small errors. On the other hand its an extremely broad problem. Also to get a good user rating, it has to do 99 things right for every 1 thing it draws wrong.
- larodi 3y ago
  Well this thing about fingers, etc in drawings. Lets put it this way - for us mere mortals the generative images look very much okay. To artists and people who actually draw something, well ... they very often spot inconsistencies in the whole production, including how fingers, arms, overall body posture, etc is presented. So it is exactly what we can expect - good enough on average, but actually a mediocre result of commonality. Thing is the wide audience chews in mediocrity all the time, and nobody seems to have been able to change this for ages...
  - disgruntledphd2 3y ago
    It's a weighted average over the prompts and data. Mediocrity is exactly what we'd expect from such an approach. Now, if we end up seeing mastery, that would be extremely interesting.
    - 3y ago

japoco 3y ago

I think art is much easier for LLM-style AI models to do compared to writing. To make a nice picture you just need to place pixels near each other in a way that looks good, and we all know LLMs are phenomenal at this. Good text on the other hand is not just text that has a good flow and fits the prompt. It must follow a line of thought, and LLMs don’t do that by design, even though we could argue wether they have that capability as an emergent one, but I don't believe that at all.

jszymborski 3y ago

> Art isn't an easier problem than math. Someone can tell you when a math problem is solved. Someone else can't tell you when you've successfully art'd with remotely the same degree of confidence. In so far as they can, however, many experts claim that AI cannot, indeed "do an art". Also, it's easier to formulate a hard math question (there are plenty of unsolved problems), but that's (IMHO) harder to do for art. Sure, you may think this is the first time the phrase "Astronaut riding a Llama and holding an avocado" was writ, but those are all well represented concepts in the dataset. For more abstract prompts, there really isn't a way to verify "correctness".

jack_riminton 3y ago

NovelAI isn't for drawing it's for writing, did you mean something else?

dxbydt 3y ago

> Art isn't an easier problem than math. If I was asked to draw a. the emptiness in your heart b. the lack of furniture in your room c. your empty bank balance d. starvation e. object returned by a python function with no return ... I could just submit an empty sheet of paper, & an artist would argue that my empty sheet of paper represents any/all of the above. Now, if I turn in the same empty paper at a math qualifier and argue that it represents the infinite set of real and complex numbers, ergo the answer to the posed qual problem must be in there, I'll get kicked out of that phd program in a jiffy.

civilitty 3y ago
> I could just submit an empty sheet of paper, & an artist would argue that my empty sheet of paper represents any/all of the above. And they'd be taken about as seriously as the ads taped to a urinal in a Museum of Modern Art washroom.

godelski 3y ago

> Art isn't an easier problem than math. In a way it is, in a way it isn't. You have to remember what is easy for machine isn't going to correlate to what is easy for us humans. Look at AI art. Closely. No, closer than that. All the detail is fucked up. Not just the hands, but the tiniest of things. Strokes, lighting, reflections, and consistency, and all that. But can I turn my friend into a convincing werewolf? Yes. Can I turn my cat into a human or Wonder Woman? No. The system isn't a "fancy copier" but it is a compression algorithm and the aforementioned tasks were only possible because lots of work training LoRAs, textual inversions, control nets, and so on (you could seriously improve GANs, VAEs, hell, even Boltzman Machines could probably do pretty well were any of these given the same research investment that diffusion has received. GANs come close but nuances like GANs having a magnitude fewer parameters). But let's look at math, can I consistently add numbers? No. The problem is that in math, all those tiny intricate details matter. Not only that, they matter at every single step. The thing here is that these are still pattern recognition machines. But they aren't generalized machines. You can't really derive out all of math from probability distributions (or at least cleanly, but still not convinced you can). The thing is that for math to work in AI we have to address the elephants in the room: math. Yeah, math. ML people don't like it. But we gotta address the axioms in the room that we're operating under. How do we move on from machines operating on manifolds? How do we make it so data are not distributional? How do we move away from a number of unmentioned axioms remains a large open problem in AI research. One that does not get anywhere serious enough of a conversation, especially within the community. Sure, maybe transformer circuits can learn some addition by learning how to do FFTs and add in the FFT space, but you're not going to get to Abstract Algebra that way. Ideally the AI can solve problems that have no algorithms, pun intended.