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Is that always true ? I know nothing about AI whatsoever but wouldn’t the function have to satisfy certain properties for a NN to be able to learn from it? Prop

Is that always true ? I know nothing about AI whatsoever but wouldn’t the function have to satisfy certain properties for a NN to be able to learn from it? Properties that the discussed “3D verifier function “ wouldn’t necessarily have? It sounds very much like complexity theory, where you definitely have problems that are easier to verify than solve (see NP hard). (Otherwise I could do stuff like use a hash function to train a NN to compute input for hashes , for example, no?)

sniuff 8y ago
It will keep generating stuff until it improves score of the function, not try to satisfy the function.
- 3pt14159 8y ago
  Yeah, but we don't generate prime numbers with them by scoring them based on how few common factors they have. Pattern matching only works for certain things. At some point it's necessary to conceptualize at which point I'm pretty convinced you have hard AI.
kiallmacinnes 8y ago
I don't know much about AI and NN's either, so grain of salt and all that.. In theory, yes - you can. The training function could return a score - say the number of matching digits in the hash - and the NN will in theory learn what inputs produce the better output. If somehow, there is a weakness in the hash algorithm, it could stumble onto it - allowing it to get better at producing the right input to get the required output. The simpler it is to deterministically manipulate, the easier it should be for a NN to learn to manipulate - even of the function is just returning a boolean or a "rating between 1 and 10". So yea, good hash functions are unlikely to be learned and solved, but photos and videos aren't designed to be hash functions. (All that said, I'm willing to be bet the time and computing power you'd need to pour into this NN to break a good hash function is likely more than has ever existed in the sum of all past time + computing resources ;))
chongli 8y ago
It sounds very much like complexity theory, where you definitely have problems that are easier to verify than solve (see NP hard). I think you mean NP. NP hard includes problems that are not in NP, such as the halting problem. I'm not aware of any proof that photorealistic 3D rendering is in NP. If it is not, then verifying 3D cannot be said to be easy.
- krastanov 8y ago
  Nitpicking on your nitpick: you mean NP-complete, not NP (for instance, any P problem is an NP problem, but NP-complete problems are probably not P)
  - mygo 8y ago
    You’re correct, but I’m gonna have to nitpick your nitpick and say that NP-complete problems have not been proven (or disproven) to be in P. “Probably not” is an opinion.
    - valvar 8y ago
      It's a well-informed opinion that is based on the fact that what we do know indicates[1] that, probably, P≠NP. [1] https://en.wikipedia.org/wiki/P_versus_NP_problem#Reasons_to_believe_P_%E2%89%A0_NP https://en.wikipedia.org/wiki/P_versus_NP_problem#Reasons_to...
      - mygo 8y ago
        Publish a paper with the mathematical proof. “probably not” is neither here nor there. The main argument from your link is nobody has found an efficient algorithm for any of the 3,000+ studied problems after all this time. And yet it’s also true that in the same span of time, nobody has been able to prove the that it’s not, either. No matter how hard they have tried. “probably not” is just an opinion. I’ll wait for the formal proof. Till then it simply is not known whether or not.
- deleted 8y ago
  [deleted]
currymj 8y ago
its not always true (hash functions are in some sense designed by brilliant people to be as hard to learn as possible) but it’s true often enough to be surprising. if there was some score that would look at lighting etc, you probably could just tack it on to one of the loss functions somewhere and expect to see improvement. maybe not enough to beat the system; or perhaps it would increase the lighting score but make the image obviously unrealistic in other ways we haven’t thought of.
- confiscate 8y ago
  ya but in this case, wouldn't you have to program a really complex loss function, to track a lot of things like lighting, edge detection, granularity, video compressions artifacts etc. basically at this point you would have a lot of if-else statements, like a "Expert System" AI in the 80s. so much so that it would be better to use a NN to replace the loss/error/verification function instead of coding it by hand?
avivo 8y ago
I think this is a valuable framing. In other words, is there any computation problem which physics implicitly solves in the process of generating an image or video—and which is: 1) computationally intractable to forge. (e.g. requires simulating trillions of photons) 2) computationally tractable to verify Sadly, my immediate gut intuition is that there is not such a problem, for a variety of reasons; but hopefully I'm wrong!

sniuff 8y ago

It will keep generating stuff until it improves score of the function, not try to satisfy the function.

3pt14159 8y ago
Yeah, but we don't generate prime numbers with them by scoring them based on how few common factors they have. Pattern matching only works for certain things. At some point it's necessary to conceptualize at which point I'm pretty convinced you have hard AI.

kiallmacinnes 8y ago

I don't know much about AI and NN's either, so grain of salt and all that.. In theory, yes - you can. The training function could return a score - say the number of matching digits in the hash - and the NN will in theory learn what inputs produce the better output. If somehow, there is a weakness in the hash algorithm, it could stumble onto it - allowing it to get better at producing the right input to get the required output. The simpler it is to deterministically manipulate, the easier it should be for a NN to learn to manipulate - even of the function is just returning a boolean or a "rating between 1 and 10". So yea, good hash functions are unlikely to be learned and solved, but photos and videos aren't designed to be hash functions. (All that said, I'm willing to be bet the time and computing power you'd need to pour into this NN to break a good hash function is likely more than has ever existed in the sum of all past time + computing resources ;))

chongli 8y ago

It sounds very much like complexity theory, where you definitely have problems that are easier to verify than solve (see NP hard). I think you mean NP. NP hard includes problems that are not in NP, such as the halting problem. I'm not aware of any proof that photorealistic 3D rendering is in NP. If it is not, then verifying 3D cannot be said to be easy.

krastanov 8y ago
Nitpicking on your nitpick: you mean NP-complete, not NP (for instance, any P problem is an NP problem, but NP-complete problems are probably not P)
- mygo 8y ago
  You’re correct, but I’m gonna have to nitpick your nitpick and say that NP-complete problems have not been proven (or disproven) to be in P. “Probably not” is an opinion.
  - valvar 8y ago
    It's a well-informed opinion that is based on the fact that what we do know indicates[1] that, probably, P≠NP. [1] https://en.wikipedia.org/wiki/P_versus_NP_problem#Reasons_to_believe_P_%E2%89%A0_NP https://en.wikipedia.org/wiki/P_versus_NP_problem#Reasons_to...
    - mygo 8y ago
      Publish a paper with the mathematical proof. “probably not” is neither here nor there. The main argument from your link is nobody has found an efficient algorithm for any of the 3,000+ studied problems after all this time. And yet it’s also true that in the same span of time, nobody has been able to prove the that it’s not, either. No matter how hard they have tried. “probably not” is just an opinion. I’ll wait for the formal proof. Till then it simply is not known whether or not.
deleted 8y ago
[deleted]

currymj 8y ago

its not always true (hash functions are in some sense designed by brilliant people to be as hard to learn as possible) but it’s true often enough to be surprising. if there was some score that would look at lighting etc, you probably could just tack it on to one of the loss functions somewhere and expect to see improvement. maybe not enough to beat the system; or perhaps it would increase the lighting score but make the image obviously unrealistic in other ways we haven’t thought of.

confiscate 8y ago
ya but in this case, wouldn't you have to program a really complex loss function, to track a lot of things like lighting, edge detection, granularity, video compressions artifacts etc. basically at this point you would have a lot of if-else statements, like a "Expert System" AI in the 80s. so much so that it would be better to use a NN to replace the loss/error/verification function instead of coding it by hand?

avivo 8y ago

I think this is a valuable framing. In other words, is there any computation problem which physics implicitly solves in the process of generating an image or video—and which is: 1) computationally intractable to forge. (e.g. requires simulating trillions of photons) 2) computationally tractable to verify Sadly, my immediate gut intuition is that there is not such a problem, for a variety of reasons; but hopefully I'm wrong!