One answer is "They will, and surprisingly quickly. But they will be a completely different set of complex interactions than are observed in the real world, because of some roundoff error in the binary representation of the Nth digit of some apparently unimportant constant. Unfortunately, because the system is complex, you'll probably spend the rest of your career trying to track down that error, and fail."

Another answer is: They would, if the simulation was comprehensive enough. Unfortunately, phase space is big. You just won't believe how vastly, hugely, mind-bogglingly big it is. Seriously: Your mind reels when confronted with the number of different molecular interactions going on inside the "simplest" single-celled prokaryote, so you abstract it away, almost as a reflex, to stop yourself from going mad. Then you abstract away the first-order abstraction. Then you keep going. Soon you begin to imagine that you can model an entire collection of a trillion organisms, just as a naive programmer imagines that they can rewrite Windows in three days if only they use a powerful enough language. It's a mere matter of programming!

And we even have an existence proof that such a thing is possible, given enough design time. Unfortunately, the existence proof says nothing about the odds of doing so very quickly -- in less than, say, a million years, which is very quick by historical standards.

The other answer contains an implicit assumption that's not obviously correct: you suggest that complexity only arises when you enumerate every possible dimension of phase space. But physical simulations have been very successful at reproducing complex behaviour from simple rules, without taking into account every particle's state vector.

Finally, did you really try to equate my statement to the statement that Windows could be written in three days? ...

As for this statement:

physical simulations have been very successful at reproducing complex behaviour from simple rules

Absolutely, but it doesn't follow that every complex behavior can be reproduced from simple rules. To overgeneralize from success in one field is the occupational illness of futurists. It's certainly a key problem for Singularitarians, who tend to get so enthusiastic about Moore's Law that they forget that most of the world has nothing to do with microelectronics.

So the real question then becomes does biology tolerate working on an approximation of the underlying physics and does that simulated biology still have the ability to exhibit intelligence. I think the first is a maybe, the second a yes but I couldn't give you any reasons why other than that it might be that our biology needs 'its' physics to operate and that probably anything Turing complete has the potential to exhibit intelligence, regardless of whether or not we find a way to achieve that.

Because so far, despite the best efforts of many geniuses and heroic computing resources, our simulations don't even reliably predict the real-world outcomes of far, far simpler systems. Ilya Prigogine won the Nobel Prize in chemistry for demonstrating that sufficiently complex systems display emergent behaviors that can never be entirely predicted by studying their components in isolation: http://en.wikipedia.org/wiki/Ilya_Prigogine#The_End_of_Certa...

Kurzweil is hopelessly out of his depth in these arguments and is talking nonsense. Personally I'll be very surprised if we can construct anything approaching human intelligence in my lifetime.

Put in another way, what construct would qualify as approaching human intelligence ?

For instance, an intelligence that could be taught to read English and then have a reasonable conversation about a contemporary novel, with its own insights into the style and themes of the book, would qualify.

That said, I do expect to see great strides in the sophistication of machines in the next 30 years. They don't have to think like people to be useful.

I think the point he is making is that if your goal is to simulate the human brain you also have to simulate and thus understand all the little details of biology because transistors don’t magically have the same properties as proteins.

My current project is a search engine for protein chain geometry. We only have ~20% of the known proteins in our database because the data on the other 80% isn't accurate enough to be useful.

My simpler point is that Kurzweil's not taking a useful measure for the size of the system we're solving. (By the way, he plays the same kind of trick on his audience when he's pointing out there are only a few billion neurons in the brain - as if that were the only level of complexity in the brain).

No, common English use of "it's hard" means something completely different from CS "hard". CS hard means NP-complete which to English translates as impossible. Impossible because of well understood mathematical reasons.

Quantum computers may solve it, indeed real life protein folding may have quantum computer-like properties.

If you're a computer guy, you should clearly understand what his fundamental disagreement with Kurzweil is about.

The point is, he seems to suggest that the genome is all you need, when clearly that's not true.

Even the 800MB of base pairs may have higher entropy than the machine language we're used to. 2000 lines of lisp or haskell are worlds away from 2000 lines of assembly.