[vlovich123]

That's kind of a philosophical question no? Is being able to model an analog behavior accurately the same thing as the analog behavior itself? While we know from math there's an equivalence / bounded error rate, it certainly seems like emulation in the digital domain is far more power intensive which would indicate that it's not the thing itself. A clearer example is photon collisions. Simulating that behavior on a computer is not the same thing as colliding the photons. Could be wrong though.

[kzrdude]

Does it matter if the simulations of photons is on an abacus or using a GPU? I think that's the question. Neither of those are "reality", just a simulation.

[vlovich123]

>>>> Maybe ML uses some weird shortcut, but how do we know the human brain doesn't use the same shortcut? If it's possible to use some simple hack to do something, why didn't we evolve to work that way?

>>> Brains are radically different from GPUs.

>> The same calculations can be performed by an abacus. What is doing the calculation is irrelevant. The question is what the calculation is

> Does it matter if the simulations of photons is on an abacus or using a GPU? I think that's the question. Neither of those are "reality", just a simulation.

I think so, yes, specifically with respect to the question of "how do we know the human brain doesn't use the same shortcut?". Simulations likely use very different shortcuts because they're optimizing for the structural design of a man-made machine that exists today and uses numerical and CS tricks to cheapen the computation cost while maintaining error rates on training data. The brain uses physical shortcuts to minimize energy expenditure, for survival of the host, and resiliency of the species (i.e. OK if flaws exist sometimes as long as the species survival is improved long-term). So not only is ML a fun-house mirror image of a brain (our model is extremely imperfect today), the optimization process is totally alien to how the brain figured out all its shortcuts.