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.
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.
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.
Are you making the claim that the N bits of DNA involved in coding the brain can encode more than 2^N neural algorithms?
That's exactly what the article above explains. Did you read it?
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?
I am not sure I understand the question. Are you asking if I believe the brain is a large but mostly simply designed neural network? If that is the question, then no.
Or are you claiming that epigenetic factors are able to reliably transmit significantly more than N bits of mission-critical data across the generations
I am claiming that do get a human you must "host" the human genome in a pre-existing human. Sticking it in a mouse will not result in a human. What does that imply?
that epigenetic evolution is likely to thank for devising the human intelligence algorithm rather than evolution of DNA?
I don't see two kind of evolutions there. It's all just human evolution genome and all. After all, it's not like human dna is out there evolving in something else besides humans.
In humans the difference is muddled because the spec goes through such ridiculously complicated machinery to become the product
Yes!
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
What implies that? How do you go form yes a hugely complex compiler is necessary, to no we can just randomly shuffle the code and it'll be just as good?
How many bits does it take to describe the string "aaaaaaa"? Not many. How many bits to describe the human genome to a scientist? I'll just gzip it and email it and were done, awesome!
How many bits to describe a human brain or how to turn that genome into a human brain? Well lets see, its a complex self-modifying process, the human brain expands the number of sequence products exponentially and interestingly the mouse brain does not do this.
In mice the complexity difference between their brain and their genome is linear. In humans it is not.
In mice the Kolmogorov complexity of their brain is equal to the Kolmogorov complexity of their genome + some linear factor.
In humans it's the Kolmogorov complexity of our genome + a lot more.
How much is "a lot more"? No idea.
Is all of this inherited? Yes, partly through the genome, partly through the fact that that genome must be planted in a pre-existing human. Again, if you swap it out with a mice genome humans won't be giving birth to healthy mice and mice won't be producing humans.
You can move a simple sequence across species, like a glowing protein form jellyfish to rabbits for example. You can not move whole genomes in higher order life forms.
I think the disagreement between early and late singularity people often comes down to is the human brain mostly a large but simple mass of neurons or not.
I think computer scientist are often in the it's just a large neural network camp. Brain scientists are in the it's much more complicated than that camp. As a computer scientist and software engineer who's worked in biotech for many years, I agree with the brain scientists.
I've already replied to some of this, but re: your mice vs. humans example, my views on this are that the fundamental algorithmic innovation that makes humans so intelligent was already present in mice, and almost all critters in the "bigger than a bug" families. Fundamentally we do process information in the same way as mice, it's merely a matter of turning up some of the intensity knobs (or more likely, adding a few more well-tuned layers to the network that already exists) to let humans take intelligent thought to new realms of utility.
This is the best I could do: 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.