3 ms·

This paper specifically addresses microtubules and is definitely not neglecting the interior of neurons. I suggest you read the paper instead of just the abstra

• api 14y ago
  Microtubules, maybe, but that's only one of a billion things going on in there. Here's a pretty decent article: http://www.nature.com/news/2011/110615/pdf/474272a.pdf http://www.nature.com/news/2011/110615/pdf/474272a.pdf My point is that a neuron is not an equation in a connection model. It's a cell. You can't hand-wave away its identity as such or all the things that happen in cells. Even if nothing quantum is involved (and I didn't say it was... we don't know), including significant aspects of intra-cellular activity and including other cell types (glia, etc.) and other types of interactions adds exponentially to the classical computation requirements. I didn't mean that I dismissed the idea of classical computers simulating the brain or the implications. I just meant that I'm skeptical. Even if it is possible I'm very skeptical of it happening soon due to the absolutely insane computational requirements. My intuition is that it would require a leap on the order of vacuum tube ENIAC -> Intel Core i7. Meanwhile consumer demand for faster and faster chips is giving way to demand for slower but more energy efficient chips for mobile devices, which is subtracting from the economic incentive to continue Moore's Law. (AMD just bowed out of the x86 race for instance, leaving us with an Intel monopoly at the high end. Monopolies get lazy and stagnate.) Personally I'm much more optimistic about "wet" transhumanism-- life extension, augmentation, brain hacking, etc., than I am about "real" AI in the near term or mind uploading. I also see computers stagnating a little for a while the way aerospace did from the Apollo era until about now... entirely for economic rather than physical reasons.
  • simonster 14y ago
    > My point is that a neuron is not an equation in a connection model. It's a cell. You can't hand-wave away its identity as such or all the things that happen in cells. There's a huge philosophical difference between the idea that there is something going on in a brain that can't be computed (what the article suggests, and what you seem to suggest here) and the idea that a brain is hard to simulate (which is what you suggest in the next paragraph). As the posters above you have pointed out, the former is a fringe opinion unsupported by current evidence. The latter is a skeptical viewpoint to which many neuroscientists subscribe. Personally, I'm in the "I want to believe" camp. > My intuition is that it would require a leap on the order of vacuum tube ENIAC -> Intel Core i7. Meanwhile consumer demand for faster and faster chips is giving way to demand for slower but more energy efficient chips for mobile devices, which is subtracting from the economic incentive to continue Moore's Law. To a first approximation, speed, power consumption, and performance per dollar are all linear functions of transistor size. If you can make smaller transistors, you can make a more power-efficient chip with the same performance, or you can make a more powerful chip with the same energy consumption. I don't think people will stop innovating on this front until we hit a physical wall. There may only be one major desktop CPU maker, but there are plenty of competing fabrication plants. Since the brain is embarrassingly parallel, we only need to get within a few orders of magnitude of simulating a brain on a single chip, at which point we can just string a bunch of processors together. > Personally I'm much more optimistic about "wet" transhumanism-- life extension, augmentation, brain hacking, etc., than I am about "real" AI in the near term or mind uploading. If by "near term" you mean within the next 20 years or so, I agree. Beyond that, it's really hard to tell. If faster computers actually let us make scientific discoveries faster, the singularity people may well be right.
  • bascule 14y ago
    A cell is a physical system. A physical system can either: 1) Be sufficiently large that quantum effects are irrelevant, like a billiard ball, and thus it can be modeled deterministically inside of a computer. The Tegmark paper proposes this is how the brain works. 2) Rely on quantum mechanical effects (which are non-deterministic, but can still be simulated inside a computer if you trust a computer's opinion of "random"). Penrose, Searle, etc. would argue otherwise, that there's a method to the madness, and that there's something "special" about the quantum mechanical effect on microtubules. We still don't understand quantum mechanics very well, so if human consciousness really relies on quantum mechanics, there is arguably a bit of wiggle room here, especially if you're a physicist of the same caliber as Penrose. Microtubules are specifically of interest because they're one of the few brain structures small enough to arguably be subject to quantum mechanical events. Tegmark argues that they're not small enough and that it's irrelevant. The real question is: Is it possible to simulate a brain within a computer? - not - Is it really hard? Is it too hard for modern technology? Is this problem simply too complex for us to understand? Do we lack the technology to build sufficient understanding today? Etc. Etc. This article is claiming it's IMPOSSIBLE to simulate a brain within a computer. Proving that is a tall order.
    • lutusp 14y ago
      > Be sufficiently large that quantum effects are irrelevant ... This misunderstands the role of quantum theory in macroscopic systems. There is never a scale so large that quantum effects can be safely ignored. All that happens is that the specific effects, and probabilities, change. The Heisenberg Uncertainly principle doesn't build a wall between the microscopic and macroscopic realms, it makes a probabilistic prediction about quantum events at all scales -- larger scale, lower probability. But a macroscopic system that has a very low probability of exhibiting classic quantum behaviors as a whole, will nevertheless show quantum behaviors at some level. An easily understood example is a radioactive sample -- let's say a kilogram of uranium. The sample isn't going to behave like Schrodinger's cat, but its constituent atoms certainly will. In one sense, the uranium is a classical mass with no contribution from quantum theory. In another sense, it's highly influenced by quantum theory -- were this not so, there would be no nuclear disintegrations. An observer can examine the sample and, intent on demonstrating that it's a classical system, use the half-life equation to predict its future -- a' = a 2^-t/f a = activity level at time zero a' = activity level at time t t = time, consistent units f = half-life factor -- And the outcome looks very classical, but only because the sample is large. But the timing of the next disintegration is quantum-deterministic. So the uranium is a chimera -- part classical, part quantum. This is an example that makes the point very clearly, but all classical systems (and scales) possess quantum properties, usually not so obvious as it is with a radioactive sample. > Rely on quantum mechanical effects (which are non-deterministic, but can still be simulated inside a computer if you trust a computer's opinion of "random"). Quantum effects aren't merely random. What connects an event to quantum theory isn't its randomness, but the nature of the randomness -- its genesis. No matter how carefully I design a random number generator, I won't be able to imitate quantum entanglement unless the system is actually capable of this specific physical behavior.