Because it shows that with constant optimization pressure from natural selection and no intelligent insight, there were no diminishing returns to a search for better brain designs up to at least the human level. There were probably accelerating returns (with a low acceleration factor). There are no visible speedbumps, so far as I know.

Were the brain designs better because they were more powerful or more intelligent?

That is how many of the improvements were adding more resources to the brain (because they paid off in this evolutionary case), rather than adding more efficient programs/systems.

Pearson: I think you have a tendency to overlook our lack of knowledge of how the brain works. You talk of constant brain circuitry, when people add new hippocampal cells through their life. We also expand the brain areas devoted to fingers if we are born blind and use braille.

Pearson, "constant brains" means "brains with constant adaptation-algorithms, such as an adaptation-algorithm for rewiring via reinforcement" not "brains with constant synaptic networks". I think a bit of interpretive charity would have been in order here.

We don't know how deep the rabbit hole of adaptation goes. Are there constant adaptation-algortihms? Constant adaptation algorithms are not a prerequisite for an optimization process, evolution being the cannonical example. It gets by with a very changeable adaptation-algorithm embodied in the varieties of genetic transfer, reproduction rates etc. We have barely scratched the surface of adaption systems, assuming a constant adaptation-algorithm for intelligence is premature, as far as I am concerned.

I think you have a tendency to overlook our lack of knowledge of how the brain works. You talk of constant brain circuitry, when people add new hippocampal cells through their life. We also expand the brain areas devoted to fingers if we are born blind and use braille.

We don't know how else the brain rewires itself. In some sense all knowledge is wiring in the brain... I mean what else is it going to be. This is all invisible to us, and may throw a spanner in the works of any intelligence trying to improve a moving target.

By nanocomputer I meant rod-logic or whatever the state of the art in hypothetical computing is. I want to see how it compares to standard computing.

I think the lure of nano computing is supposed to be low power consumption and easy 3d stackability that that entails as well. It it not sufficient to have small components if they are in a 2D design and you can't have too many together without overheating.

Some numbers would be nice though.

How much of current R&D time is humans thinking, and how much is compiling projects, running computer simulations or doing physical experiments?

E.g. would having faster than human speed uploads, speed up getting results from the LHC by the ratio of their speed to us?

Also do you have some FLOPS per cubic centimeter estimations for nanocomputers? I looked at this briefly, and I couldn't find anything. It references a previous page that I can't find.

Do you have any evidence that insight is applicable to understanding and creating intelligences? Because without that recursion isn't powerful and magic doesn't start to get off the ground.

Another interesting innovation of evolution is the Hox genes. Basically evolution invented modularity, it saved the fact that 6 legs were useful and allowed the space of different lengths of all the legs to be searched, rather than tweaking each individually. So I would say the Homeobox system would count as an insight, for genes.

Why not? The space we search has been very useful apart from for finding the solution to the creation of a mind. Perhaps the space of minds is outside the current space of Turing machines we are currently searching. It would cert explain why no one has been very successful so far.

Not to say that we could never find a mind. Just that we might have trouble using a compressible search space.

Believer: The search space is compressible -

The space of behaviors of Turing Machines is not compressible, sub spaces are, but not the whole lot. What space do you expect the SeedAIs to be searching? If you could show that it is compressible and bound to have an uncountable number of better versions of the SeedAI, then you could convince me that I should worry about Fooming.

As such when I think of self-modifiers I think of them searching the space of Turing Machines, which just seems hard.

I'm sure I have shown this to you before Eliezer, but other people might be interested in the very high rate of mutation in the cone shell poison genes.

Presumably this is under genetic control so counts as another meta innovation. How many are there throughout the world, we really don't know. I don't think reasoning about a process we don't really understand (evolution) will help us about trying to predict what happens when things go recursive.