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It doesn't work in the sense everybody most wants it to work, which is to slap a cheap CCD camera or three on top of a mobile robot and have it tell the robot e

It doesn't work in the sense everybody most wants it to work, which is to slap a cheap CCD camera or three on top of a mobile robot and have it tell the robot everything about its surroundings, like human vision can. Yeah, if you point a camera at a very restricted specific set of objects, all lit the same way, and all in generally the same basic configuration, you can do some vaguely useful statistics to get some information out of it, but that's kind of a painfully limiting thing. Put it this way: At Anybots, we have a robot with 16 cameras on its head. We pump all those cameras back to an operator station, stitch them all together in a vaguely fitting-together way, and then project them onto a big bank of monitors. Plop a human operator in front of the monitors, and he knows exactly the orientation of the robot, the configuration of its limbs, and has basically a 3d map of the entire scene and a model of the lighting, minus occlusions. I'm pretty sure nobody knows how to write a computer vision system to do that. I'm not even sure that anybody knows how to use the overlap in the cameras to figure out how to make a coherent nice projection of the cameras onto the monitors.

DaniFong 19y ago
Sure, it doesn't work like magic. There's still a decent case for a book...
bluishgreen 19y ago
Computer vision is classified under AI in wikipedia. That pretty much sums up what we are trying to arrive at here.
mnemonicsloth 19y ago
It's interesting -- I started writing this exact definition of "works" in my post above. Anthropomorphic bias, right? Even with this understanding, though, I can't wrap my brain around what you're saying above. Computer vision doesn't "work" (for our definition of "work") because it's not feasible to get enough processing power together: O( 3e10 ) neurons * 100 Hz/neuron = more cycles than you can rent on an NSF grant. We've definitely got some exponential growing to do before we can even start meaningful experimentation. Does that mean no work should be done in the meantime?
- pixcavator 19y ago
  Yes, it is a bias. I think it is a big mistake to try to solve the problem by emulating how humans see because we don't know how humans see (here's a rant of mine on a related topic http://inperc.com/blog2/2007/10/12/%e2%80%9cbrain-inspired%e2%80%9d-and-%e2%80%9cnature-inspired%e2%80%9d-a-rant/ http://inperc.com/blog2/2007/10/12/%e2%80%9cbrain-inspired%e...).
- ecuzzillo 19y ago
  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?

DaniFong 19y ago

Sure, it doesn't work like magic. There's still a decent case for a book...

bluishgreen 19y ago

Computer vision is classified under AI in wikipedia. That pretty much sums up what we are trying to arrive at here.

mnemonicsloth 19y ago

It's interesting -- I started writing this exact definition of "works" in my post above. Anthropomorphic bias, right? Even with this understanding, though, I can't wrap my brain around what you're saying above. Computer vision doesn't "work" (for our definition of "work") because it's not feasible to get enough processing power together: O( 3e10 ) neurons * 100 Hz/neuron = more cycles than you can rent on an NSF grant. We've definitely got some exponential growing to do before we can even start meaningful experimentation. Does that mean no work should be done in the meantime?

pixcavator 19y ago
Yes, it is a bias. I think it is a big mistake to try to solve the problem by emulating how humans see because we don't know how humans see (here's a rant of mine on a related topic http://inperc.com/blog2/2007/10/12/%e2%80%9cbrain-inspired%e2%80%9d-and-%e2%80%9cnature-inspired%e2%80%9d-a-rant/ http://inperc.com/blog2/2007/10/12/%e2%80%9cbrain-inspired%e...).
ecuzzillo 19y ago
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?