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AI is entirely deterministic. ChatGPT and StableDiffusion and friends are fed endless amounts of random seeds along with every input to keep them from just sayi

by zaarn 4y ago

AI is entirely deterministic. ChatGPT and StableDiffusion and friends are fed endless amounts of random seeds along with every input to keep them from just saying always the same thing.

nicky0 4y ago
The output for a given input (absent random seeds) may be constant, but is the output deterministically correct?
- zaarn 4y ago
  What does that question even mean?
  - friendzis 4y ago
    Roughly, they are asking whether you can take an AI system and effectively reduce it to an analytic function and tell, without actually running the AI, what the output is going to be with a particular input.
  - nicky0 4y ago
    A generative AI may well be deterministic and generate repeatable output for a given inpiut, but that doesn't mean the output is correct for every input. It may merely generate the wrong answer consistently.
HPsquared 4y ago
"Deterministic" only insofar as it's repeatable, but their behaviour is not predictable. If the behaviour is not predictable, how can it be validated as being correct?
- zaarn 4y ago
  If for a given set of inputs there is a deterministic output, then the overall behaviour to a series of inputs is just as deterministic and predictable. I'm not sure what you mean with "is not predictable" when you also admit that it's repeatable.
  - taeric 4y ago
    I think the point is that reality is essentially a chaotic system. That is, yes, you can repeat a failure from an input once you've seen it, but the search space is too big to enumerate beforehand.
    - zaarn 4y ago
      That entirely depends on what exactly you implement here, it's entirely possible to implement an AI with continuous & linear properties, meaning that you can extrapolate it's behaviour between a set of inputs with decent accuracy and it won't suddenly change it's behaviour between continuous & linear inputs. But AI isn't different than existing software systems either. Both will take an input from reality and take actions upon it.
  - HPsquared 4y ago
    Predictable as in, can say in advance what it will do. Repeatable means you get the same output a second time, given the same input. Predictable means you can say in advance what it will do, and can then check the output against your prediction. If you can't predict the outcome, you can't validate the process, and can't guarantee its performance.
    - zaarn 4y ago
      That much is clear but any remotely continuous AI model is very predictable.
deleted 4y ago
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jhomedall 4y ago
That's useless in this case. You need to be able to prove that it will work with all inputs, and there are too many combinations of inputs to exhaustively enumerate.
- zaarn 4y ago
  That would be true of any software system we put into an airplane, yet we have deployed software into airplanes. If your AI has linear / continuous output, testing it should be no different than any other software.
  - jhomedall 4y ago
    There is no way to determine that a non-trivial neural network won't drastically diverge in output due to small changes in input (eg one pixel attacks on image classifiers). This is true for all current models I know of. Almost all neural network implementations have continuous outputs (ie the nodes in the output layer produce a value between 0 and 1). That doesn't change the above issue at all. This is much less of an issue with traditional methods

by zaarn 4y ago

AI is entirely deterministic. ChatGPT and StableDiffusion and friends are fed endless amounts of random seeds along with every input to keep them from just saying always the same thing.

nicky0 4y ago

The output for a given input (absent random seeds) may be constant, but is the output deterministically correct?

zaarn 4y ago
What does that question even mean?
- friendzis 4y ago
  Roughly, they are asking whether you can take an AI system and effectively reduce it to an analytic function and tell, without actually running the AI, what the output is going to be with a particular input.
- nicky0 4y ago
  A generative AI may well be deterministic and generate repeatable output for a given inpiut, but that doesn't mean the output is correct for every input. It may merely generate the wrong answer consistently.

HPsquared 4y ago

"Deterministic" only insofar as it's repeatable, but their behaviour is not predictable. If the behaviour is not predictable, how can it be validated as being correct?

zaarn 4y ago
If for a given set of inputs there is a deterministic output, then the overall behaviour to a series of inputs is just as deterministic and predictable. I'm not sure what you mean with "is not predictable" when you also admit that it's repeatable.
- taeric 4y ago
  I think the point is that reality is essentially a chaotic system. That is, yes, you can repeat a failure from an input once you've seen it, but the search space is too big to enumerate beforehand.
  - zaarn 4y ago
    That entirely depends on what exactly you implement here, it's entirely possible to implement an AI with continuous & linear properties, meaning that you can extrapolate it's behaviour between a set of inputs with decent accuracy and it won't suddenly change it's behaviour between continuous & linear inputs. But AI isn't different than existing software systems either. Both will take an input from reality and take actions upon it.
- HPsquared 4y ago
  Predictable as in, can say in advance what it will do. Repeatable means you get the same output a second time, given the same input. Predictable means you can say in advance what it will do, and can then check the output against your prediction. If you can't predict the outcome, you can't validate the process, and can't guarantee its performance.
  - zaarn 4y ago
    That much is clear but any remotely continuous AI model is very predictable.

deleted 4y ago

[deleted]

jhomedall 4y ago

That's useless in this case. You need to be able to prove that it will work with all inputs, and there are too many combinations of inputs to exhaustively enumerate.

zaarn 4y ago
That would be true of any software system we put into an airplane, yet we have deployed software into airplanes. If your AI has linear / continuous output, testing it should be no different than any other software.
- jhomedall 4y ago
  There is no way to determine that a non-trivial neural network won't drastically diverge in output due to small changes in input (eg one pixel attacks on image classifiers). This is true for all current models I know of. Almost all neural network implementations have continuous outputs (ie the nodes in the output layer produce a value between 0 and 1). That doesn't change the above issue at all. This is much less of an issue with traditional methods