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It's about computation improving the rate of computational progress

But computation doesn't improve the rate of computational progress. That's achieved by improvements in manufacturing. Computation does help a little, giving us better tools for chip design. But mainly it's manufacturing.

The only way for computational progress to exponentiate, past the limit of Moore's Law, is to use more physical resources - computers get literally bigger.

And so expensive that only the five richest crowned heads of Europe can afford them!

But computation doesn't improve the rate of computational progress.

As long as it's people developing computing devices, that holds. But when you have a machine that is capable of improving its own software, you get a self reinforcing loop where computation improves the rate of computational progress.

Intelligence doesn't even have to feed on itself. Imagine a human-level hardware designing AI. As long as Moore's law applies, it can double its computational power every 18 subjective months. From our point of view, that's 18 months, then 9, then 4.5…

If Moore's law where unlimited, such an AI would have infinite computational power in 36 months, with absolutely no increase in actual intelligence. But of course Moore's law is limited. Still, we could expect quite a bit of progress from a mere AI hardware designer.

I do not believe that that is what Moore's Law means or how it works.

Perhaps I just mistook your meaning - what do 'subjective months' mean? Why do they mean that the doubling time starts halving?

I mean this: http://dragonball.wikia.com/wiki/Hyperbolic_Time_Chamber

When hardware is twice as fast, the AI works twice as fast. In 9 months, it will do 18 months worth of work. Now keep the doubling up, and it will have done infinite work after 36 months.

This assertion is groundless. Why would Moore's law accelerate? In fact, Moore's law is currently decelerating and gains are increasingly harder to find. Further, Moore's law says nothing about computational speed, but only the density of transistors. These two are related but not identical.

Edit: also, from my understanding, the gains in transistor density largely depend on new discoveries in physics. Before a computer can aid in accelerating Moore's law it would need to be sufficiently advanced to generate new discoveries in physics, but at this point you'd have a computer smart enough that it Moore's law wouldn't matter much. Seems like putting the cart before the horse to me.

Transistor count and computational are so tightly correlated that's nitpick.

Now I did not assume "Moore's law" would accelerate. I assumed it would stay constant. The key point comes from the fact the AI (or AI hive) would trivially benefit from hardware speed-ups.

And yes, Moore's law won't really count, compared to the rest we will be able to do. I was just trying to be as conservative as possible. (Though Moore's law still holding until strong AI is quite wild).

Further, if we build a human-like intelligence on a chip, this would plausibly be a highly parallel algorithm. By that token, you wouldn't need more transistors-per-inch, you could use chips that snap-together with high bandwidth connections and perhaps some longer-distance traffic control connections.

The kind of design isn't an NP-complete problem but it certainly could be helped by a powerful AI.

Also, Moore's Law is breaking for speed but not for transistors-per-inch. That's why we're seeing multicode chip now.

So designing the next generation of Intel chips using the latest Intel chips isn't "computation improving the rate of computational progress"?

To increase "computational progress" on an exponential scale, the rate of improvement has to increase.

If the rate of improvement were constant, the progress would be linear.

In short, something is improving the rate (but it probably includes more designers and better methods, not just more complexity management).

are you stating that computer manufactures don't rely on computers to design the next generation manufacturing techniques? Or that faster/better software isn't going to increase the rate of improvement in computer manufacturing?

Material scientists , physicists, chemists, chip designers are all using software for modeling and exploration. And bigger/faster/better computers are helping drive innovations back into computer manufacturing. The process is feeding itself, and it has been for awhile.

Actually he misses a second point too ... the reason you don't see exponential growth in human-led research is that we each start out with "empty" brains. To become truly knowledgeable in a field may require a life-time.

I'm not arguing that the singularity is near either, but machines would have the advantage of creating offspring that are already as knowledgeable/intelligent as themselves. Something I couldn't do for my children.

How is throwing more (truly intelligent) computers at the problem any different than throwing more human brains at it? It's not just computing power that had been exploding, it is also the number of people trying to crack the nut of AI (for instance).

Because the human brains can only communicate and acquire knowledge at a limited rate. This causes the "exponential decrease of discovery impact of each succeeding researcher" that the original post refers to, but he misses the point completely. We can't 'stand on the shoulders of giants' until we've climbed up there, slowly and painfully. An AI can just download the lot. And share any new discoveries with the hive mind.

it's sci fi, but we haven't ruled out that human brains can't acquire knowledge at rates orders of magnitudes faster than current learning methods. Big advances in neuroscience and linguistics could make this possible.

Right now, the limits on our bandwidth is language and our sensory inputs. It's not completely ridiculous to think we could encode certain kinds of knowledge in more compact forms, and transmit them via an artificial sensory process that has higher higher bandwidth that audio/visual language based learning...

The singularity idea does not care how the self-improvement feedback occurs.