Ray Kurzweil Doesn’t Understand the Brain [Counterpoint]

PZ Myers-a biologist and associate professor at the University of Minnesota, Morris-has something or three to assert about Ray Kurzweil‘s claim that we’ll reverse engineer the brain by 2020. Personal attacks aside, he makes some strong points.

There he goes again, making up nonsense and making ridiculous claims that haven’t any relationship to reality. Ray Kurzweil should be in a position to spin out an excellent line of bafflegab, because he seems to have the tech media convinced that he’s a genius, when he’s actually just another Deepak Chopra for the computer science cognoscenti.

His latest claim is that we’ll be ready to reverse engineer the human brain within a decade. By reverse engineer, he signifies that we’ll have the capacity to write software that simulates the entire functions of the human brain. He’s not just speculating optimistically, though: he’s building his case on such awfully bad logic that I’m surprised anyone still pays attention to that kook.

Sejnowski says he agrees with Kurzweil’s assessment that about a million lines of code could be enough to simulate the human brain.

Here’s how that math works, Kurzweil explains: The design of the brain is inside the genome. The human genome has three billion base pairs or six billion bits, that’s about 800 million bytes before compression, he says. Eliminating redundancies and applying loss-less compression, that information may be compressed into about 50 million bytes, consistent with Kurzweil.

About half of this is the brain, which comes right down to 25 million bytes, or a million lines of code.

I’m very disappointed in Terence Sejnowski for going together with that nonsense.

See that sentence I bolded up there? That’s his fundamental premise, and it’s far utterly false. Kurzweil knows nothing about how the brain works. It’s design isn’t encoded within the genome: what’s inside the genome is a set of molecular tools wrapped up in bits of conditional logic, the regulatory component of the genome, that makes cells attentive to interactions with a posh environment. The brain unfolds during development, using essential cell:cell interactions, of which we understand only a tiny fraction. The result is a brain it is much, rather more than simply the sum of the nucleotides that encode a couple of thousand proteins. He has to simulate all of development from his codebase on the way to generate a brain simulator, and he isn’t even privy to the magnitude of that problem.

We cannot derive the brain from the protein sequences underlying it; the sequences are insufficient, in addition, because the nature of their expression depends on our environment and the history of several hundred billion cells, each plugging along interdependently. We haven’t even solved the sequence-to-protein-folding problem, that is a vital first step to executing Kurzweil’s clueless algorithm. And we have got absolutely no option to calculate in principle all of the possible interactions and functions of a single protein with the tens of thousands of alternative proteins within the cell!

Let me offer you a number of specific examples of just how wrong Kurzweil’s calculations are. Listed below are several proteins that I plucked at random from the NIH database; all play a job inside the human brain.

First up is RHEB (Ras Homolog Enriched in Brain). It’s a small protein, only 184 amino acids, which Kurzweil pretends could be reduced to about 12 bytes of code in his simulation. Here’s the fast 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 immune 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 alternative effect on MTORC2, producing inhibition of the upstream AKT pathway (Mavrakis et al., 2008).

Got that? You would’t understand RHEB until you know how it interacts with three other proteins, and how it fits into a posh 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. While you read deeper into the description, you discover that the general effect of RHEB is to modulate cell proliferation in a tightly controlled quantitative way. You aren’t going in an effort to simulate a complete brain until you know precisely and in complete detail exactly how this one protein works.

And it’s not just the single. It’s the entire proteins. Here’s another: FABP7 (Fatty Acid Binding Protein 7). This one is simply 132 amino acids long, so Kurzweil would compress it to 8 bytes. What does it do?

Anthony et al. (2005) identified a Cbf1 (147183)-binding site inside the promoter of the mouse Blbp gene. They found that this binding site was essential for all Blbp transcription in radial glial cells during central nervous system (CNS) development. Blbp expression was also significantly reduced within the forebrains of mice lacking the Notch1 (190198) and Notch3 (600276) receptors. Anthony et al. (2005) concluded that Blbp is a CNS-specific Notch target gene and suggested that Blbp mediates some aspects of Notch signaling in radial glial cells during development.

Again, what we know of its function is experimentally determined, not calculated from the sequence. It might be wonderful so one can take a sequence, plug it into a computer, and have it spit back a quantitative assessment of all of its interactions with other proteins, but we will’t try this, and even supposing we could, it wouldn’t answer the entire questions we’d have about its function, because we’d also have to know the state of the entire proteins within the cell, and the state of each of the proteins in adjacent cells, and the state of world and local signaling proteins inside the environment. It’s an insanely complicated situation, and Kurzweil thinks he can reduce it to a triviality.

To simplify it so a computer science guy can get it, Kurzweil has everything completely wrong. The genome shouldn’t be the program; it’s the information. The program is the ontogeny of the organism, that is an emergent property of interactions between the regulatory components of the genome and our surroundings, which uses that data to build species-specific properties of the organism. He doesn’t even comprehend the nature of the difficulty, and here he is pontificating on magic solutions completely free of facts and reason.

I’ll make a prediction, too. We won’t have the ability to plug a single unknown protein sequence into a computer and have it derive an entire description of all of its functions by 2020. Conceivably, we could replace this step with an entire, experimentally derived quantitative summary of the entire functions and interactions of every protein all for brain development and function, but I guarantee you that won’t happen either. And that’s just step one in building a simulation of the human brain derived from genomic data. It gets harder from there.

I’ll make yet one more prediction. The media shouldn’t end their infatuation with this pseudo-scientific dingbat, Kurzweil, despite how uninformed and ridiculous his claims get.

Illustration by Sam Spratt. Look into Sam’s portfolio and become partial to his Facebook Artist’s Page.

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This post is tagged: brain, counterpoint, Doesnt, kurzweil, Ray, Understand