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No it isn’t.
A fundamentally entropic phenomenon is discovered to be patterened. It would be like looking through a microscope and seeing voxels. How is this not strong evidence for simulation?
Of course, that’s exactly the kind of thing a simulation would type!
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The argument is fairly straightforward: Let n be the number of seed bits of the CSPRNG. Assume there exists an ANN that approximates the CSPRNG so that the number of possibly wrong output bits is at most polylogarithmic in n (otherwise you haven't really gained much insight from the ANN). Assume further that the ANN is sufficiently small so that a forward pass through the ANN takes at most polynomial time in n. Then…
The difference is that ANNs make approximations. It could be manageably small and learn an approximation of the PRNG code that succeeds for a large number of outputs, even if running a fully accurate network is impossible. I think the complexity of the PRNG recursive algorithm , when unrolled through time for large number of steps, is the relevant complexity here. The ANN is not necessarily trying to devise a new fun…
Of course, this is assuming that practical CSPRNGs conform to the theory, but we're just debating theory here.
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> That's somewhat equivalent of partially seeing the secret internal state Not just partially, xorshift128 is designed such that the output depends only on the last 4 results. So this is "just" learning the output function of xorshift as a neural network.
Perhaps you might consider how big that output function ideally is, then compare to the capacity of the neural network they used and the number of samples provided.
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It would also work for PCG. You're simply modeling the PRNG and learning the internal state from outputs. It's even easier using Z3 or your own SAT solver. I've broken pretty much all non-crypto PRNGS using Z3, and it's trivial to do so. You simply model the PRNG in Z3, with unknown constants, plug in a few values from the output (or even parts of output, or even non-consecutives ones, anything you derive from them,…
How do you pick how many constants and they're relationships?
Whether that’s simple depends is debatable, but for example would be if somebody open-sourced the server-side of their poker dealing site.
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No it isn’t.
A fundamentally entropic phenomenon is discovered to be patterened. It would be like looking through a microscope and seeing voxels. How is this not strong evidence for simulation?
I don’t see why finding underlying structure in what we expect to be pure noise has to mean our reality is a simulation. I guess it could be consistent with that, but couldn’t it also be consistent with some previously undiscovered physics, much like the development of quantum mechanics?
This is pretty hilarious. I just noticed that this is published by NCC Group. (Former NCC Group pentester here, and now I happen to be an ML engineer.) I don't think HN's negativity is warranted. But there's also some confusion, which I'd like to help clear up. People here seem confused about "Why? Why do this?" The answer is simply that it's an interesting problem. That's it. It doesn't need to be important, or a pr…
This is pretty hilarious. I just noticed that this is published by NCC Group. (Former NCC Group pentester here, and now I happen to be an ML engineer.) I don't think HN's negativity is warranted. But there's also some confusion, which I'd like to help clear up. People here seem confused about "Why? Why do this?" The answer is simply that it's an interesting problem. That's it. It doesn't need to be important, or a pr…
Earlier quoted context omitted.
How do you pick how many constants and they're relationships?
I think that’s the “You simply model the PRNG in Z3” part. Whether that’s simple depends is debatable, but for example would be if somebody open-sourced the server-side of their poker dealing site.
It's absolutely trivial, taking a few lines of code.
I've broken all sorts of code using similar tools.