Jed_Harris

The arguments Eliezer describes are made, and his reactions are fair. But really the actual research community "grew out" of most of this stuff a while back. CYC and the "common sense" efforts were always a sideshow (in terms of research money and staff, not to mention results). Neural networks were a metonym for statistical learning for a while, then serious researchers figured out they needed to address statistical learning explicitly. Etc.

Admittedly there's always excessive enthusiasm for the current hot thing. A few years ago it was support vector machines, I'm not sure what now.

I recognize there's some need to deflate popular misconceptions, but there's also a... (read more)

Regarding serial vs. parallel:

The effect on progress is indirect and as a result hard to figure out with confidence.

We have gradually learned how to get nearly linear speedups from large numbers of cores. We can now manage linear speedups over dozens of cores for fairly structured computations, and linear speedup over hundreds of cores are possible in many cases. This is well beyond the near future number of cores per chip. For the purposes of this analysis I think we can assume that Intel can get linear speedups from increasing processors per chip, say for the next ten years.

But there are other issues.

More complicated / difficult programming models may... (read more)

I'll try to estimate as requested, but substituting fixed computing power for "riding the curve" (as Intel does now) is a bit of an apples to fruit cocktail comparison, so I'm not sure how useful it is. A more direct comparison would be with always having a computing infrastructure from 10 years in the future or past.

Even with this amendment, the (necessary) changes to design, test, and debugging processes make this hard to answer...

I'll think out loud a bit.

Here's the first quick guess I can make that I'm moderately sure of: The length of time to go through a design cycle (including shrinks and transitions to new processes) would scale pretty... (read 603 more words →)

I did work at Intel, and two years of that was in the process engineering area (running the AI lab, perhaps ironically).

The short answer is that more computing power leads to more rapid progress. Probably the relationship is close to linear, and the multiplier is not small.

Two examples:

• The speed of a chip is limited by critical paths. Finding these and verifying fixes depends on physically realistic simulations (though they make simplifying assumptions, which sometimes fail). Generally the better the simulation the tighter one can cut corners. The limit on simulation quality is typically computer power available (though it can also be understanding the physics well enough to cheat

On the one hand, Eliezer is right in terms of historical and technical specifics.

On the other hand neural networks for many are a metoynym for continuous computations vs. the discrete computations of logic. This was my reaction when the two PDP volumes came out in the 80s. It wasn't "Here's the Way." It was "Here's and example of how to do things differently that will certainly work better."

Note also that the GOFAI folks were not trying to use just one point in logic space. In the 70s we already knew that monotonic logic was not good enough (due to the frame problem among other things) so there was... (read more)

Poke's comment is interesting and I agree with his / her discussion of cultural evolution. But it also is possible to turn this point around to indicate a possible sweet spot in the fitness landscape that we are probably approaching. Conversely, however, I think the character of this sweet spot indicates scant likelihood of a very rapidly self-bootstrapping AGI.

Probably the most important and distinctive aspect of humans is our ability and desire to coordinate (express ourselves to others, imitate others, work with others, etc.). That ability and desire is required to engage in the sort of cultural evolution that Poke describes. It underlies the individual acquisition of language,... (read 449 more words →)

I largely agree with Robin's point that smaller incremental steps are necessary.

But Eliezer's point about big jumps deserves a reply. The transitions to humans and to atomic bombs do indicate something to think about -- and for that matter, so does the emergence of computers.

These all seem to me to be cases where the gradually rising or shifting capacities encounter a new "sweet spot" in the fitness landscape. Other examples are the evolution of flight, or of eyes, both of which happened several times. Or trees, a morphological innovation that arises in multiple botanical lineages.

Note that even for innovations that fit this pattern, e.g. computers and atomic bombs, enormous... (read more)

PK, Phil Goetz, and Larry D'Anna are making a crucial point here but I'm afraid it is somewhat getting lost in the noise. The point is (in my words) that lookup tables are a philosophical red herring. To emulate a human being they can't just map external inputs to external outputs. They also have to map a big internal state to the next version of that big external state. (That's what Larry's equations mean.)

If there was no internal state like this, a GLUT couldn't emulate a person with any memory at all. But by hypothesis, it does emulate a person (perfectly). So it must have this... (read more)