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"The correct level of detail for the emergence of intelligent behaviour is likely somewhere in-between." I think this is your speculation. :) "Classical compu

"The correct level of detail for the emergence of intelligent behaviour is likely somewhere in-between." I think this is your speculation. :) "Classical computers take an exponential amount of time" It is not a matter of time it takes to simulate, but of the details. You don't know which details matter and which don't. You don't know if you need infinite precision or you can stop at Plank's level. Thus, since you don't know a lot of things, you can't be sure that what you simulated was indeed the real thing or something that you imagined/theorized to be the real thing.

chriswarbo 12y ago
> I think this is your speculation. :) True, but I'm hedging my bets ;) > You don't know if you need infinite precision or you can stop at Plank's[sic] level. Objectively: we know that infinite precision computation is no obstacle, since we can just do everything symbolically in proof-space rather than numerically in value-space. Whether we can call that "simulation" is a matter of semantics, but it's "only" an exponential slowdown. Empirically: we know that the Planck-level interactions have no observable effects on macro-scale phenomena. If they did, we wouldn't need to build particle accelerators! Brains do not exploit particle physics for their abilities; there is no point simulating at any scale lower than up/down quarks + electrons + photons. They don't even exploit nuclear physics; there's no point simulating at a scale below the atomic. Brains do exploit chemistry, so an atomically-precise simulation may be justified. Of course, that's only if we want a faithful simulation. There's nothing to say that protein-level simulations won't be perfectly fine, or even cell-level, if the relevant lower-level effects can be approximated. Some current approaches operate at the level of mini-columns, and work well on standard AI tasks like computer vision. These coarser-grained simulations are less precise than the finer-grained ones, but all that matters is that they exhibit intelligent behaviour (however we're defining it). Even if some atomic-level quirk were being exploited by biological brains, we may still be better off without it. Again, it's all a matter of resource usage (intelligence per CPU-second/GB/dollar/etc.). A coarse-grained simulation might not be as intelligent as a fine-grained simulation, but it will run much faster; instead of spending more resources to run the fine-grained simulation, why not spend those same resources running the coarse-grained simulation at faster than real-time, with a larger virtual brain?
zAy0LfpBZLC8mAC 12y ago
"Thus, since you don't know a lot of things, you can't be sure that what you simulated was indeed the real thing or something that you imagined/theorized to be the real thing." That's a useless distinction as there is nothing where you could "know the real thing", you _always_ work with theories, without exceptions. When you take your first step out of bed in the morning, you don't know the _real_ behaviour of the floor (it's made of atoms, after all), you "only" use a relatively high-level theory of solid materials to predict that it will support you before you step onto it. There is no guarantee that that will work out, but there is fundamentally no way to do better than that, all of science works that way, _everything_ we know about the world is "a theory", even the atom theory is a theory, and the quantum theory is a theory, it's all about modeling as best as we can, none of it is "proven to be the real thing", and no scientist ever even tries to prove something to be "the ultimate reality", all that matters to science is to make models more precise, and to figure out what those models seem to be sufficiently precise to use for - and then use them, which we do very successfully indeed.

chriswarbo 12y ago

> I think this is your speculation. :) True, but I'm hedging my bets ;) > You don't know if you need infinite precision or you can stop at Plank's[sic] level. Objectively: we know that infinite precision computation is no obstacle, since we can just do everything symbolically in proof-space rather than numerically in value-space. Whether we can call that "simulation" is a matter of semantics, but it's "only" an exponential slowdown. Empirically: we know that the Planck-level interactions have no observable effects on macro-scale phenomena. If they did, we wouldn't need to build particle accelerators! Brains do not exploit particle physics for their abilities; there is no point simulating at any scale lower than up/down quarks + electrons + photons. They don't even exploit nuclear physics; there's no point simulating at a scale below the atomic. Brains do exploit chemistry, so an atomically-precise simulation may be justified. Of course, that's only if we want a faithful simulation. There's nothing to say that protein-level simulations won't be perfectly fine, or even cell-level, if the relevant lower-level effects can be approximated. Some current approaches operate at the level of mini-columns, and work well on standard AI tasks like computer vision. These coarser-grained simulations are less precise than the finer-grained ones, but all that matters is that they exhibit intelligent behaviour (however we're defining it). Even if some atomic-level quirk were being exploited by biological brains, we may still be better off without it. Again, it's all a matter of resource usage (intelligence per CPU-second/GB/dollar/etc.). A coarse-grained simulation might not be as intelligent as a fine-grained simulation, but it will run much faster; instead of spending more resources to run the fine-grained simulation, why not spend those same resources running the coarse-grained simulation at faster than real-time, with a larger virtual brain?

zAy0LfpBZLC8mAC 12y ago

"Thus, since you don't know a lot of things, you can't be sure that what you simulated was indeed the real thing or something that you imagined/theorized to be the real thing." That's a useless distinction as there is nothing where you could "know the real thing", you _always_ work with theories, without exceptions. When you take your first step out of bed in the morning, you don't know the _real_ behaviour of the floor (it's made of atoms, after all), you "only" use a relatively high-level theory of solid materials to predict that it will support you before you step onto it. There is no guarantee that that will work out, but there is fundamentally no way to do better than that, all of science works that way, _everything_ we know about the world is "a theory", even the atom theory is a theory, and the quantum theory is a theory, it's all about modeling as best as we can, none of it is "proven to be the real thing", and no scientist ever even tries to prove something to be "the ultimate reality", all that matters to science is to make models more precise, and to figure out what those models seem to be sufficiently precise to use for - and then use them, which we do very successfully indeed.