3 ms·

According to Max Tegmark's calculations, the importance of quantum mechanical effects on brain processes is negligible: http://arxiv.org/abs/quant-ph/9907009 h

by bascule 14y ago

According to Max Tegmark's calculations, the importance of quantum mechanical effects on brain processes is negligible: http://arxiv.org/abs/quant-ph/9907009 http://arxiv.org/abs/quant-ph/9907009 This paper posits the brain can be modeled as a classical (e.g. Newtonian, "billiard ball"-style) physical system

api 14y ago
This is based on the idea that the sum total of what the brain does can be explained by and represented by neural network type models. The conventional neural network model neglects the interior of the neuron. Gene regulatory networks for complex eukaryotes are on the order of neural networks in complexity and involve quantum-scale interactions, which opens the possibility of quantum effects being significant. Gene regulation within the neuron affects neural firing behavior and, more importantly, profoundly affects neural growth patterns and thus learning and longer term forms of cognition. This also neglects the possibility (now considered probable) that more cell types than just neurons are involved in brain activity: http://en.wikipedia.org/wiki/Gliotransmitter http://en.wikipedia.org/wiki/Gliotransmitter In short: the brain is not a neural network. Rather, those mathematical connectionist models are just that: models of aspects of the brain. We do not yet know to what extent these other mechanisms play a role, and what their role is. Given their nature it seems in both cases that their role might be more long-term, affecting long duration learning, planning, etc. It really seems to me as if the most ardent and enthusiastic adherents of the Kurzweilian vision are computer scientists who don't really respect the domain of biology and like to hand-wave away its complexity as "background noise." You can't do that. I say this as a lifelong computer programmer who has studied biology. Studying biology really blew away any notions I had of simple, classical computer programs becoming movie-style AI. The author is not making an anti-scientific "magic" argument. He is simply pointing out that biological systems are analog, embodied, electrochemical (and thus physical and possibly quantum), nonlinear complex systems, and he is being skeptical about the idea that such a system is going to yield readily to digital computer simulation. I agree with his skepticism. Prediction: brain simulations will simulate superficial brain behavior but they will not become sentient. More specific prediction: they will get stuck in closed cycle loops. They will not exhibit the higher order motivation, creativity, or learning behavior seen in brains, which is probably because these behaviors emerge from all the real embodied biophysical stuff the CS people are ignoring.
- bascule 14y ago
  This paper specifically addresses microtubules and is definitely not neglecting the interior of neurons. I suggest you read the paper instead of just the abstract before you give feedback.
  - 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.

by bascule 14y ago

api 14y ago

This is based on the idea that the sum total of what the brain does can be explained by and represented by neural network type models. The conventional neural network model neglects the interior of the neuron. Gene regulatory networks for complex eukaryotes are on the order of neural networks in complexity and involve quantum-scale interactions, which opens the possibility of quantum effects being significant. Gene regulation within the neuron affects neural firing behavior and, more importantly, profoundly affects neural growth patterns and thus learning and longer term forms of cognition. This also neglects the possibility (now considered probable) that more cell types than just neurons are involved in brain activity: http://en.wikipedia.org/wiki/Gliotransmitter http://en.wikipedia.org/wiki/Gliotransmitter In short: the brain is not a neural network. Rather, those mathematical connectionist models are just that: models of aspects of the brain. We do not yet know to what extent these other mechanisms play a role, and what their role is. Given their nature it seems in both cases that their role might be more long-term, affecting long duration learning, planning, etc. It really seems to me as if the most ardent and enthusiastic adherents of the Kurzweilian vision are computer scientists who don't really respect the domain of biology and like to hand-wave away its complexity as "background noise." You can't do that. I say this as a lifelong computer programmer who has studied biology. Studying biology really blew away any notions I had of simple, classical computer programs becoming movie-style AI. The author is not making an anti-scientific "magic" argument. He is simply pointing out that biological systems are analog, embodied, electrochemical (and thus physical and possibly quantum), nonlinear complex systems, and he is being skeptical about the idea that such a system is going to yield readily to digital computer simulation. I agree with his skepticism. Prediction: brain simulations will simulate superficial brain behavior but they will not become sentient. More specific prediction: they will get stuck in closed cycle loops. They will not exhibit the higher order motivation, creativity, or learning behavior seen in brains, which is probably because these behaviors emerge from all the real embodied biophysical stuff the CS people are ignoring.

bascule 14y ago
This paper specifically addresses microtubules and is definitely not neglecting the interior of neurons. I suggest you read the paper instead of just the abstract before you give feedback.
- 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.