The design of the brain is in the genome. The human genome has three billion base pairs or six billion bits, which is about 800 million bytes before compression, he says. Eliminating redundancies and applying loss-less compression, that information can be compressed into about 50 million bytes, according to Kurzweil.
About half of that is the brain, which comes down to 25 million bytes, or a million lines of code.
See that sentence I put in red up there? That’s his fundamental premise, and it is utterly false. Kurzweil knows nothing about how the brain works. It’s design is not encoded in the genome: what’s in the genome is a collection of molecular tools wrapped up in bits of conditional logic, the regulatory part of the genome, that makes cells responsive to interactions with a complex environment. The brain unfolds during development, by means of essential cell:cell interactions, of which we understand only a tiny fraction. The end result is a brain that is much, much more than simply the sum of the nucleotides that encode a few thousand proteins. He has to simulate all of development from his codebase in order to generate a brain simulator, and he isn’t even aware of the magnitude of that problem.
We cannot derive the brain from the protein sequences underlying it; the sequences are insufficient, as well, because the nature of their expression is dependent on the environment and the history of a few hundred billion cells, each plugging along interdependently. We haven’t even solved the sequence-to-protein-folding problem, which is an essential first step to executing Kurzweil’s clueless algorithm. And we have absolutely no way to calculate in principle all the possible interactions and functions of a single protein with the tens of thousands of other proteins in the cell!
Let me give you a few specific examples of just how wrong Kurzweil’s calculations are. Here are a few proteins that I plucked at random from the NIH database; all play a role in the human brain.
First up is RHEB (Ras Homolog Enriched in Brain). It’s a small protein, only 184 amino acids, which Kurzweil pretends can be reduced to about 12 bytes of code in his simulation. Here’s the short description.
MTOR (FRAP1; 601231) integrates protein translation with cellular nutrient status and growth signals through its participation in 2 biochemically and functionally distinct protein complexes, MTORC1 and MTORC2. MTORC1 is sensitive to rapamycin and signals downstream to activate protein translation, whereas MTORC2 is resistant to rapamycin and signals upstream to activate AKT (see 164730). The GTPase RHEB is a proximal activator of MTORC1 and translation initiation. It has the opposite effect on MTORC2, producing inhibition of the upstream AKT pathway (Mavrakis et al., 2008).
Got that? You can’t understand RHEB until you understand how it interacts with three other proteins, and how it fits into a complex regulatory pathway. Is that trivially deducible from the structure of the protein? No. It had to be worked out operationally, by doing experiments to modulate one protein and measure what happened to others. If you read deeper into the description, you discover that the overall effect of RHEB is to modulate cell proliferation in a tightly controlled quantitative way. You aren’t going to be able to simulate a whole brain until you know precisely and in complete detail exactly how this one protein works.
Dammit, and I thought Kurzweil was a schmot guy.
But let me poke a bit on Meyers, too: Simulating the brain down to the protein interactions isn’t going to work, either. The trick is going to be setting up a network of logic gates that can self-organize into a brain under the appropriate stimuli. And, no, we’re not going to understand how it works, either. [Yeah, that link goes to Meyers, too. He knows better.]
Then there’s this from the comments:
[Kurzweil thinks] he’ll be able to resurrect his dead father using DNA recovered from the latter’s grave plus records of his life. IOW, he believes in magic.
via SF critic James Nicoll.