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It seems to me that Kurzweil is on rather strong grounds when he argues in effect that 25Mbytes is a safe conservative upper bound on the information needed to

by wnewman 15y ago

It seems to me that Kurzweil is on rather strong grounds when he argues in effect that 25Mbytes is a safe conservative upper bound on the information needed to specify a human infant brain. The relevant information content of the epigenetic stuff is unlikely to be tens of megabytes, and extremely unlikely to be hundreds of megabytes. Otherwise, it's hard to see how we could've overlooked such a high proportion of non-DNA design information being passed around in all the work being done on genetics. It's also hard to see how so much extra information would stay stable against mutation-ish pressures unless its copy error rate was much lower than DNA, and hard to see how we'd've overlooked all the machinery that would accomplish that. Moreover, I think 25M bytes is probably a very conservative upper bound, so that the relevant uncompressable complexity of what computer scientists need to design for general AI is likely no more than 1M bytes. A lot of actual brain stuff is likely to be description of the physical layer that silicon engineers won't care about, because they do the physical layer in a completely different way (silicon and masks and resists and hardest of hard UV and two low digital voltages, not wet tendrils groping toward each other in the dark and washing each other with neurotransmitters). A significant amount of actual brain stuff is likely to be application layer stuff that we don't need (e.g., the Bearnaise sauce effect, and fear of heights and snakes) and optimizations that we don't strictly need (all sorts of shortcuts for visual processing and language grammar and so forth, when more general-purpose mechanisms would still suffice to pass a Turing test). A lot of brain stuff is likely to be stuff in common with a fish, much of which we already know how to implement from scratch. And all brain stuff seems pretty likely to be encoded rather inefficiently: lots of twisty little protein substructures and nucleic acid binding sites are unlikely to be nearly as concise as the kind of mathematical or programming language notation that describes what's going on. When Kurzweil writes "do not appreciably add" I understand him to be willing to stand by roughly the quantitative information theoretical claims I made at first (25Mbytes, tens of Mbytes, hundreds of Mbytes). When you write "profoundly wrong ... add hugely" I am unable to tell what you are claiming. How many uncompressable bits of design information you are talking about? Perhaps you believe that natural selection pounded out and mitosis reliably propagates 200M uncompressable bytes of brain design information? or 1G bytes? As above, I think that is probably false. Or perhaps I should read "hugely" as "vitally" and understand that you merely mean that the epigenetic information might be less than a million uncompressable bytes but still if you corrupt it badly you have a dead or hopelessly moronic infant. If that's what you mean, I think you are factually correct, but also don't think that that fact contradicts Kurzweil's argument.

bh42222 15y ago
It seems to me that Kurzweil is on rather strong grounds when he argues in effect that 25Mbytes is a safe conservative upper bound on the information needed to specify a human infant brain. This is the best I could do: http://www.sciencedaily.com/releases/2005/01/050111115721.htm http://www.sciencedaily.com/releases/2005/01/050111115721.ht... I can't find the actual scientific papers. Anyway, form the article above: The lack of correlation between genome size and an organism’s complexity raised a question – how do complexity and diversity arise in higher life forms? Or to rephrase that, why is there no correlation between source code size and application complexity? Why are mice with 24.99Mb of DNA so much less than humans with 25Mb of DNA? Why is there not a linear relationship between the complexity of the animal and the complexity of it's DNA? Well...: RNA editing involves the process by which cells use their genetic code to manufacture proteins. More specifically, says Maas, RNA editing “describes the posttranscriptional alteration of gene sequences by mechanisms including the deletion, insertion and modification of nucleotides.” RNA editing, says Maas, can “increase exponentially the number of gene products generated from a single gene.” Increase the number of gene products from a single gene exponentially, that says. The paper I can't find describes how this process, as it takes place in the brain, is an almost exact match for the complexity difference between mice and humans. And yes, posttranscriptional alteration is much more fragile than good old double helix DNA. And no, evolution doesn't care. ...hard to see how we'd've overlooked all the machinery that would accomplish that. We didn't overlook it for long, shortly after the human genome project raised the question, we spotted it. See above. the relevant uncompressable complexity of what computer scientists need to design for general AI is likely no more than 1M bytes. What do you base this statement on? How many uncompressable bits of design information you are talking about? Scientists have already discovered that posttranscriptional alteration (a part of epigenetic information) adds huge complexity. How much more? I am absolutely not comfortable guessing at numbers of Mbytes because I know how little I know. And what about the actual cell machinery. As the egg is being formed inside the mother how much complexity does the way its machinery work add the what will happen after the egg is fertilized? Again, I dare not guess.
- bermanoid 15y ago
  You're saying a lot there, so rather than create a wall of text in response I'd like to boil it down a bit - assume N=25Mb, give or take an order of magnitude: Are you making the claim that the N bits of DNA involved in coding the brain can encode more than 2^N neural algorithms? Or do you think that the particular set of 2^N (assuming no redundancy, which is generous...) neural algorithms that N bits of DNA can encode are more likely to result in intelligence than a random sampling of algorithms of equivalent Kolmogorov complexity? Or are you claiming that epigenetic factors are able to reliably transmit significantly more than N bits of mission-critical data across the generations, and that epigenetic evolution is likely to thank for devising the human intelligence algorithm rather than evolution of DNA? Edit: looking over your post, I suspect that part of the misunderstanding is over the word "complexity". You seem to be focusing on the complexity of the products; these estimates focus on the complexity of the spec. In humans the difference is muddled because the spec goes through such ridiculously complicated machinery to become the product, but when it comes to designing algorithms, that complicated machinery might as well be a random shuffle for all it matters to the algorithm's proper functioning, so the Kolmogorov complexity that it adds is effectively zero.

by wnewman 15y ago

bh42222 15y ago

It seems to me that Kurzweil is on rather strong grounds when he argues in effect that 25Mbytes is a safe conservative upper bound on the information needed to specify a human infant brain. This is the best I could do: http://www.sciencedaily.com/releases/2005/01/050111115721.htm http://www.sciencedaily.com/releases/2005/01/050111115721.ht... I can't find the actual scientific papers. Anyway, form the article above: The lack of correlation between genome size and an organism’s complexity raised a question – how do complexity and diversity arise in higher life forms? Or to rephrase that, why is there no correlation between source code size and application complexity? Why are mice with 24.99Mb of DNA so much less than humans with 25Mb of DNA? Why is there not a linear relationship between the complexity of the animal and the complexity of it's DNA? Well...: RNA editing involves the process by which cells use their genetic code to manufacture proteins. More specifically, says Maas, RNA editing “describes the posttranscriptional alteration of gene sequences by mechanisms including the deletion, insertion and modification of nucleotides.” RNA editing, says Maas, can “increase exponentially the number of gene products generated from a single gene.” Increase the number of gene products from a single gene exponentially, that says. The paper I can't find describes how this process, as it takes place in the brain, is an almost exact match for the complexity difference between mice and humans. And yes, posttranscriptional alteration is much more fragile than good old double helix DNA. And no, evolution doesn't care. ...hard to see how we'd've overlooked all the machinery that would accomplish that. We didn't overlook it for long, shortly after the human genome project raised the question, we spotted it. See above. the relevant uncompressable complexity of what computer scientists need to design for general AI is likely no more than 1M bytes. What do you base this statement on? How many uncompressable bits of design information you are talking about? Scientists have already discovered that posttranscriptional alteration (a part of epigenetic information) adds huge complexity. How much more? I am absolutely not comfortable guessing at numbers of Mbytes because I know how little I know. And what about the actual cell machinery. As the egg is being formed inside the mother how much complexity does the way its machinery work add the what will happen after the egg is fertilized? Again, I dare not guess.

bermanoid 15y ago
You're saying a lot there, so rather than create a wall of text in response I'd like to boil it down a bit - assume N=25Mb, give or take an order of magnitude: Are you making the claim that the N bits of DNA involved in coding the brain can encode more than 2^N neural algorithms? Or do you think that the particular set of 2^N (assuming no redundancy, which is generous...) neural algorithms that N bits of DNA can encode are more likely to result in intelligence than a random sampling of algorithms of equivalent Kolmogorov complexity? Or are you claiming that epigenetic factors are able to reliably transmit significantly more than N bits of mission-critical data across the generations, and that epigenetic evolution is likely to thank for devising the human intelligence algorithm rather than evolution of DNA? Edit: looking over your post, I suspect that part of the misunderstanding is over the word "complexity". You seem to be focusing on the complexity of the products; these estimates focus on the complexity of the spec. In humans the difference is muddled because the spec goes through such ridiculously complicated machinery to become the product, but when it comes to designing algorithms, that complicated machinery might as well be a random shuffle for all it matters to the algorithm's proper functioning, so the Kolmogorov complexity that it adds is effectively zero.