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Sure, but Kurzweil's claim is merely that if DNA can encode everything the brain does with N bits of information, then regardless of how the DNA behaves, we hav

Sure, but Kurzweil's claim is merely that if DNA can encode everything the brain does with N bits of information, then regardless of how the DNA behaves, we have at least a loose estimate of the level of complexity in the brain. The problem is that DNA is not sufficient. Organisms don't grow from naked DNA in a vacuum; the DNA is always contained within a cell which is enclosed within a more complex bio structure (egg, womb, etc) which may be contained within the parent organism in the case of mammals. You need all of this information plus knowledge of the various interactions of the different environments to pursue Kurzweil's approach. The universe of necessary and sufficient information is much, much larger than the 3 billion base pairs in your DNA.

jacquesm 16y ago
For the moment we seem to have enough trouble modeling (relatively speaking, to the brain) simple proteins reliably. It's quite a leap of faith to make these statements about the technological future with so little in terms of progress to show in these fields for the last decades. I think the most impressive a-life demos are now almost 10 years old, the best we can really simulate is (drumroll) a cockroach. And personally I think that's a milestone achievement because it means that at least we have a principle that works. Going through the DNA route to get to a working brain seems to be a very roundabout way of getting there, it will require all of the embryonic mechanisms to be modeled accurately as well as something like the first several years out of the womb before you'd know if you had created something insane or something resembling intelligence. Assuming you'd recognize it as intelligent even if you succeeded, there may be more ways of being intelligent than we know about.
- ewjordan 16y ago
  Absolutely, I never said that I think modeling the brain would be easy (I don't) or that building it up from DNA is anywhere near the best way to attempt AI (IMO it's not). I simply don't buy the argument that the algorithms of cognition inherit any substantial amount of functionality from their physical implementation, and hence, I see Kurzweil's complexity estimate as somewhat reasonable.
  - jacquesm 16y ago
    > I simply don't buy the argument that the algorithms of cognition inherit any substantial amount of functionality from their physical implementation I'm somewhere in the middle on that. I wish for things biological to be clear-cut and deterministic enough that we can fully understand them the way we understand mechanical systems. But precisely because the brain is encoded in precious little DNA there is some evidence that there is more to it than meets the eye, after all if 50M of gzipped data can encode the whole thing why do we have such a hard time understanding it. There is enough repetition in there that some died-in-the-wool reverse engineer would have put 2 and 2 together by now if the secret was in the wiring or in some simple algorithm (ANNs for instance). Apparent order appearing from chaos is a field that has seen some study and the amount of complexity that can arise form simple starting data is quite amazing, witness the mandelbrot set and other fractal forms. It may be very hard to short-circuit such understanding and to 'divine' the workings of the formula without first going the long way around to understand the whole system rather than the 'seed' from which it grows. This is not simple mathematics where a simple equation on complex numbers gives you the mandelbrot set, it's possibly machinery interpreting an equation with 50 million terms. In different terms, given a very distorted (dissected) picture of the 3 dimensional mandelbrot set would you be able to figure out the formula that gave rise to it without prior knowledge of the mathematics involved? http://www.skytopia.com/project/fractal/2mandelbulb.html#epilogue http://www.skytopia.com/project/fractal/2mandelbulb.html#epi...
    - ewjordan 16y ago
      FWIW, I'm not too far away from you on this - I don't think we are going to find a very simple algorithm to do whatever general pattern recognition the brain does. All indications suggest that while there may be such an algorithm programmed in and repeated (a lot more times in humans than other animals), it's far too complex for us to simply read off or guess at. But it does suggest that we should be considering the functions that such higher level units might have, such that they become more intelligent as we compose them. Easier said than done,of course, especially since it's very difficult to actually observe brain dynamics in any detail.
ewjordan 16y ago
You need all of this information plus knowledge of the various interactions of the different environments to pursue Kurzweil's approach. Absolutely, and this is one reason why I think Kurzweil's approach overall is pretty foolish. But I do think that his information-content claim is within the bounds of reason (I've explained my stance on that many other places in this thread, the gist is that it's highly unlikely that the whole developmental process supplies a huge amount of information "for free", in the same sense that it's unlikely that you'll achieve a 10x compression in code size by using Python instead of Ruby to write a program) and it at least tells us that a solution to the problem of intelligence does exist that doesn't require (for instance) explicit hard-coding of every neural connection inside a brain. In fact, it requires massively less information to specify than that, so there's some hope that in the end we'll be able to come away with reasonable approximations to that algorithm since it must be "fairly simple" (meaning: more complicated than anything we've tackled before, but maybe still within the realm of possibility). That hints at the fact that AI researchers (at least those that don't buy Kurzweil's ridiculous "simulate-the-whole-thing!" approach) should be looking more into building out small "genomes" into massive structures rather than focusing too much on individual explicitly specified processing networks, because nature is clearly doing something like that, and it seems to work pretty well, letting it achieve a remarkably compact solution, given that evolution tends to produce very bloated code as a rule. There has been tentative research along these lines (http://en.wikipedia.org/wiki/HyperNEAT http://en.wikipedia.org/wiki/HyperNEAT, for example), but there needs to be more, particularly to figure out what sorts of things we actually want these massive structures to do, what sorts of processes we should be focusing on to control that building process, what types of units we need in order to make the processing that we're doing feasible, etc.

jacquesm 16y ago

For the moment we seem to have enough trouble modeling (relatively speaking, to the brain) simple proteins reliably. It's quite a leap of faith to make these statements about the technological future with so little in terms of progress to show in these fields for the last decades. I think the most impressive a-life demos are now almost 10 years old, the best we can really simulate is (drumroll) a cockroach. And personally I think that's a milestone achievement because it means that at least we have a principle that works. Going through the DNA route to get to a working brain seems to be a very roundabout way of getting there, it will require all of the embryonic mechanisms to be modeled accurately as well as something like the first several years out of the womb before you'd know if you had created something insane or something resembling intelligence. Assuming you'd recognize it as intelligent even if you succeeded, there may be more ways of being intelligent than we know about.

ewjordan 16y ago
Absolutely, I never said that I think modeling the brain would be easy (I don't) or that building it up from DNA is anywhere near the best way to attempt AI (IMO it's not). I simply don't buy the argument that the algorithms of cognition inherit any substantial amount of functionality from their physical implementation, and hence, I see Kurzweil's complexity estimate as somewhat reasonable.
- jacquesm 16y ago
  > I simply don't buy the argument that the algorithms of cognition inherit any substantial amount of functionality from their physical implementation I'm somewhere in the middle on that. I wish for things biological to be clear-cut and deterministic enough that we can fully understand them the way we understand mechanical systems. But precisely because the brain is encoded in precious little DNA there is some evidence that there is more to it than meets the eye, after all if 50M of gzipped data can encode the whole thing why do we have such a hard time understanding it. There is enough repetition in there that some died-in-the-wool reverse engineer would have put 2 and 2 together by now if the secret was in the wiring or in some simple algorithm (ANNs for instance). Apparent order appearing from chaos is a field that has seen some study and the amount of complexity that can arise form simple starting data is quite amazing, witness the mandelbrot set and other fractal forms. It may be very hard to short-circuit such understanding and to 'divine' the workings of the formula without first going the long way around to understand the whole system rather than the 'seed' from which it grows. This is not simple mathematics where a simple equation on complex numbers gives you the mandelbrot set, it's possibly machinery interpreting an equation with 50 million terms. In different terms, given a very distorted (dissected) picture of the 3 dimensional mandelbrot set would you be able to figure out the formula that gave rise to it without prior knowledge of the mathematics involved? http://www.skytopia.com/project/fractal/2mandelbulb.html#epilogue http://www.skytopia.com/project/fractal/2mandelbulb.html#epi...
  - ewjordan 16y ago
    FWIW, I'm not too far away from you on this - I don't think we are going to find a very simple algorithm to do whatever general pattern recognition the brain does. All indications suggest that while there may be such an algorithm programmed in and repeated (a lot more times in humans than other animals), it's far too complex for us to simply read off or guess at. But it does suggest that we should be considering the functions that such higher level units might have, such that they become more intelligent as we compose them. Easier said than done,of course, especially since it's very difficult to actually observe brain dynamics in any detail.

ewjordan 16y ago

You need all of this information plus knowledge of the various interactions of the different environments to pursue Kurzweil's approach. Absolutely, and this is one reason why I think Kurzweil's approach overall is pretty foolish. But I do think that his information-content claim is within the bounds of reason (I've explained my stance on that many other places in this thread, the gist is that it's highly unlikely that the whole developmental process supplies a huge amount of information "for free", in the same sense that it's unlikely that you'll achieve a 10x compression in code size by using Python instead of Ruby to write a program) and it at least tells us that a solution to the problem of intelligence does exist that doesn't require (for instance) explicit hard-coding of every neural connection inside a brain. In fact, it requires massively less information to specify than that, so there's some hope that in the end we'll be able to come away with reasonable approximations to that algorithm since it must be "fairly simple" (meaning: more complicated than anything we've tackled before, but maybe still within the realm of possibility). That hints at the fact that AI researchers (at least those that don't buy Kurzweil's ridiculous "simulate-the-whole-thing!" approach) should be looking more into building out small "genomes" into massive structures rather than focusing too much on individual explicitly specified processing networks, because nature is clearly doing something like that, and it seems to work pretty well, letting it achieve a remarkably compact solution, given that evolution tends to produce very bloated code as a rule. There has been tentative research along these lines (http://en.wikipedia.org/wiki/HyperNEAT http://en.wikipedia.org/wiki/HyperNEAT, for example), but there needs to be more, particularly to figure out what sorts of things we actually want these massive structures to do, what sorts of processes we should be focusing on to control that building process, what types of units we need in order to make the processing that we're doing feasible, etc.