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I don't think we're doing that much processing to do vision. It's never the case that even close to all the cells in the visual cortex are being used. It's alwa

I don't think we're doing that much processing to do vision. It's never the case that even close to all the cells in the visual cortex are being used. It's always a tiny fraction of that. Yeah, we have a bunch of cells that neuroscientists think are somehow related to doing vision, but there's no evidence that they're doing some kind of horribly computationally intensive thing all day long. If they were, we'd see it on MRI's as soon as we put a picture in front of somebody. No, I think we just haven't figured out what to do, not that we're limited by computational resources. I think there are a bunch of pretty strong assumptions hardwired into the brain, so we can see physical objects, but then you can fool us by showing us images that mess with the assumptions (aka optical illusions). (Not that this is a particularly useful idea, mind you. I'm not about to solve computer vision. It's just a random philosophical idea.)

mnemonicsloth 19y ago
It's never the case that even close to all the cells in the visual cortex are being used. It's always a tiny fraction of that. This is also true of any individual transistor in a processor. An ALU contains hardware to perform many operations, but at most performs one per cycle. You can increase utilization through parallelism and pipelining, but you pay for it with more synchronization and control hardware that only switches on to resolve conflicts. And nowadays, half of all the transistors in a CPU are part of a cache (every cycle, you need a word or two, but get the whole block). You can't conclude that they're not making a contribution to output from the fact that they're often inactive. No, I think we just haven't figured out what to do, not that we're limited by computational resources. Based on what evidence? We know that the brain is hugely complex. We know it can sort through hugely complex problem spaces at least some of the time. Doesn't it seem like wishful thinking to assume both that the brain is really inefficient and that most of the problems it solves actually have simple hueristic solutions? I'll step up and take ownership of the end result of this argument: If what you say is true and understanding is all we lack, why have these problems proved so stubborn?
- pixcavator 19y ago
  >If what you say is true and understanding is all we lack, why have these problems proved so stubborn? Understanding is hard, why is this so surprising?
  - mnemonicsloth 19y ago
    Because understanding is almost always easier than data collection and tool-building. Pure lack of understanding -- having all the data and having no idea what it means -- can keep a problem open for a generation or two. Einstein is famous because he solved a problem in EM physics that was at most 30 years old. Lack of data or meaningful investigative techniques can keep a subject crippled for centuries. Physics went from rolling marbles to detecting radio waves in the two hundred fifty years since Galileo, while biology was pretty much stagnant. Without organic chemistry, even scientists were willing to believe that living organisms were made of some special God-stuff. Contrast that to the last 20 years. The human genome has been sequenced by biologists on their way to designing new organisms while physicists have been stuck throwing models around since the 1970s. Petri dishes are cheaper than supercolliders. Now let's look at AI and machine vision. Our primary tools, brain imaging and computer simulation, are getting better every year. Maybe new ideas in algorithms or parallel architecture will bring "real" computer vision closer. In fact, they probably will. But even if they don't, this approach will definitely succeed eventually: http://news.bbc.co.uk/2/hi/technology/6600965.stm http://news.bbc.co.uk/2/hi/technology/6600965.stm
    - pixcavator 19y ago
      I don't have as many examples but here is one. Galileo discovered his principle by conducting very simple experiments with no special tools or a lot of data collection. How do you find the relevant data or build relevant tools if you don't have a clue about what you are supposed to discover? What computer vision needs is mathematics, in my opinion. And math has nothing to do with "data collection and tool-building". It is entirely about understanding. The link provides a good example of how people try to solve problems without any understanding. They don't know how the brain operates, yet they build its "model". And then they expect this model to solve the problem for them...

mnemonicsloth 19y ago

It's never the case that even close to all the cells in the visual cortex are being used. It's always a tiny fraction of that. This is also true of any individual transistor in a processor. An ALU contains hardware to perform many operations, but at most performs one per cycle. You can increase utilization through parallelism and pipelining, but you pay for it with more synchronization and control hardware that only switches on to resolve conflicts. And nowadays, half of all the transistors in a CPU are part of a cache (every cycle, you need a word or two, but get the whole block). You can't conclude that they're not making a contribution to output from the fact that they're often inactive. No, I think we just haven't figured out what to do, not that we're limited by computational resources. Based on what evidence? We know that the brain is hugely complex. We know it can sort through hugely complex problem spaces at least some of the time. Doesn't it seem like wishful thinking to assume both that the brain is really inefficient and that most of the problems it solves actually have simple hueristic solutions? I'll step up and take ownership of the end result of this argument: If what you say is true and understanding is all we lack, why have these problems proved so stubborn?

pixcavator 19y ago
>If what you say is true and understanding is all we lack, why have these problems proved so stubborn? Understanding is hard, why is this so surprising?
- mnemonicsloth 19y ago
  Because understanding is almost always easier than data collection and tool-building. Pure lack of understanding -- having all the data and having no idea what it means -- can keep a problem open for a generation or two. Einstein is famous because he solved a problem in EM physics that was at most 30 years old. Lack of data or meaningful investigative techniques can keep a subject crippled for centuries. Physics went from rolling marbles to detecting radio waves in the two hundred fifty years since Galileo, while biology was pretty much stagnant. Without organic chemistry, even scientists were willing to believe that living organisms were made of some special God-stuff. Contrast that to the last 20 years. The human genome has been sequenced by biologists on their way to designing new organisms while physicists have been stuck throwing models around since the 1970s. Petri dishes are cheaper than supercolliders. Now let's look at AI and machine vision. Our primary tools, brain imaging and computer simulation, are getting better every year. Maybe new ideas in algorithms or parallel architecture will bring "real" computer vision closer. In fact, they probably will. But even if they don't, this approach will definitely succeed eventually: http://news.bbc.co.uk/2/hi/technology/6600965.stm http://news.bbc.co.uk/2/hi/technology/6600965.stm
  - pixcavator 19y ago
    I don't have as many examples but here is one. Galileo discovered his principle by conducting very simple experiments with no special tools or a lot of data collection. How do you find the relevant data or build relevant tools if you don't have a clue about what you are supposed to discover? What computer vision needs is mathematics, in my opinion. And math has nothing to do with "data collection and tool-building". It is entirely about understanding. The link provides a good example of how people try to solve problems without any understanding. They don't know how the brain operates, yet they build its "model". And then they expect this model to solve the problem for them...