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November 18, 2008


Julian Simon, The Great Breakthrough and Its Cause, puts the trigger of the industrial revolution on the total population. After reading it, I think that **communicating population** size might be a better criterion, that is not just the sheer number of people in an area, but the number of interacting people (non-serfs). Before someone protests the examples of China and India, he points out that until about 1400 China was leading, and goes on to state (my terms not his) that population size is necessary, but may not be sufficient without other factors.

"To stick my neck out further: I am liable to trust the Weak Inside View over a "surface" extrapolation, if the Weak Inside View drills down to a deeper causal level and the balance of support is sufficiently lopsided."

But there's the question of whether the balance of support is sufficiently lopsided, and if so, on which side. Your example illustrates this nicely:

"I will go ahead and say, "I don't care if you say that Moore's Law has held for the last hundred years. Human thought was a primary causal force in producing Moore's Law, and your statistics are all over a domain of human neurons running at the same speed. If you substitute better-designed minds running at a million times human clock speed, the rate of progress ought to speed up - qualitatively speaking.""

What you're not taking into account is that computers are increasingly used to help design and verify the next generation of chips. In other words, a greater amount of machine intelligence is required each generation just to keep the doubling time the same (or only slightly longer), never mind shorter.

Once we appreciate this, we can understand why: as the low hanging fruit is plucked, each new Moore's Law generation has to solve problems that are intrinsically more difficult. But we didn't think of that in advance. It's an explanation in hindsight.

That doesn't mean we can be sure the doubling time will still be 18 to 24 months, 60 years from now. It does mean we have no way to make a better prediction than that. It means that is the prediction on which rationalists should base their plans. Historically, those who based their plans on weak (or even strong) inside predictions of progress faster (or slower) than Moore's Law, like Nelson and Xanadu, or Star Bridge and their hypercomputers, have come to grief. Those who just looked at the graphs, have found success.

Right, so, this is an example of a disagreement I don't know how to resolve in any systematic way. If Robin comes in and says the same thing as Russell, which I doubt, I wouldn't know how the two of us ought to reconcile if we thought the other was as meta-rational as ourselves.

Basically, you've got - extrapolating Moore's Law on out, as if society's still around in one form or another and still has a smooth global tech progress metric - to where you've got "a billion times human computing power for $1000", whatever that means, which must be at least a million times as fast as a human brain serially because we already have chips that fast (they're just much less parallel).

And you've got Moore's Law continuing past this point at the same sidereal time rate, so that, after another 3,600 rotations of the Earth and ten slow orbits around the sun, computing speeds are a hundred times greater.

It's enough time for 10 million years of thought, if you were only running humans at a million times the clock speed; but this isn't human thought.

But they don't spike to the limits of design and then stop.

Instead, the equivalent of chips are just a hundred times faster, after Earth has swung around in its orbit ten times. Cuz that's Moore's Law. Doubling every eighteen months.

Now, I understand what thought you are performing here. You're thinking, "Nelson and Xanadu tried to second-guess Moore's Law, and they were wrong, so I'm sticking with Moore's Law." And that's where the graph extends. I get that.

But I don't know how to prosecute this disagreement any further. I'm using the Weak Inside View to predict a qualitative speedup. You're just extending the same graph on outward. What do I do with that? To me it just seems that I've reached the point of "Zombies! Zombies?"

It means that is the prediction on which rationalists should base their plans.

Even if that's the expected value, variance is also crucial.

The next shift may already have happened. It's called the Internet. But in 1860, nobody saw the burgeoning industrial revolution for what it was. In fact, by today's standards, it was still very inefficient and unproductive. But it created the paradigm shift by which new growth rates could be achieved.

BTW, there aren't just four such shifts. If you looked closely enough, you could find many more. The evolution of multicellular life. The evolution of sex / genetic exchange. The first tools. Writing. All of these paradigm shifts changed growth rates, although the curve looks rather flat by today's standards.

A serious problem with not quantifying predictions on the temporal axis is that many types of prediction then become unrefutable.

E.g. if you prophesy that we will be able to upload the human mind into a digital substrate, but don't say when, then if 2060 rolls around with no uploads in sight, you can say that the prediction is still correct - it just hasn't happened yet.

Unrefutable predictions have about the same status as unfalsifiable science.

Tim, that's an obvious problem but it doesn't mean I can magically conjure quantitative predictions out of thin air. If I don't know when AI will go self-improving, should I pretend that I do?

What you're not taking into account is that computers are increasingly used to help design and verify the next generation of chips.

That is taken into account here: "Machines are already heavily involved in the design of other machines. No-one could design a modern CPU without the use of computers. No one could build one without the help of sophisticated machinery."

It does mean we have no way to make a better prediction than that. It means that is the prediction on which rationalists should base their plans.

Not according to Robin Hanson. I'm not sure how much stock I put in an extrapolation from four data points, some of which are millions of years old - but the conclusion seems plausible: we have independent evidence that something big is coming, because we can see it on the horizon.

Now, I should clarify that I don't really expect Moore's Law to continue forever. Obviously the more you extrapolate it, the shakier the prediction becomes. But there is no point at which some other prediction method becomes more reliable. There is no time in the future about which we can say "we will deviate from the graph in this way", because we have no way to see more clearly than the graph.

I don't see any systematic way to resolve this disagreement either, and I think that's because there isn't any. This shouldn't come as a surprise -- if I had a systematic method of resolving all disagreements about the future, I'd be a lot richer than I am! At the end of the day, there's no substitute for putting our heads down, getting on with the work, and seeing who ends up being right.

But this is also an example of why I don't have much truck with Aumann's Agreement Theorem. I'm not disputing the mathematics of course, but I think cases where its assumptions apply, are the exception rather than the rule.

Eliezer, I'm actually a little surprised at that last comment. As a Bayesian, I recognize that reality doesn't care if I feel comfortable with whether or not I "know" an answer. Reality requires me to act on the basis of my current knowledge. If you think AI will go self-improving next year, you should be acting much differently than if you believe it will go self-improving in 2100. The difference isn't as stark at 2025 versus 2075, but it's still there.

What makes your unwillingness to commit even stranger is your advocacy that there's significant existential risk associated with self-improving AI. It's literally a life or death situation by you're own valuation. So how are you going to act, like it will happen sooner or later?

Tim -- I looked at your essay just now, and yes, your Visualization of the Cosmic All seems to agree with mine. (I think Robin's model also has some merit, except that I am not quite so optimistic about the timescales, and I am very much less optimistic about our ability to predict the distant future.)

Outside view works as long as you can usefully classify underlying structures using surface properties. It breaks when reality starts to ignore the joints at which you previously carved it. Thus, it's prudent to create big categories, with margins wide enough to capture most black swans.

Qualitative inside view can diverge from reality due to unanticipated circumstances, pointing in the wrong direction as a result. But both outside view and inside view are built on (the same) knowledge, not on reality itself. If qualitative inside view breaks the outside view, it shows a problem: categories of the outside view are not wide enough to capture even this (weakly) anticipated dynamic, when they are supposed to be black swan-proof, to survive things unanticipated. Either the inside view should be shown wrong, given current knowledge, or the outside view should be rebuilt to withstand the inside view.

I'll be downright indignant if Reality says "So what?" and has the superintelligence make slower progress than human engineers instead.

Maybe once it's secure from being overtaken by rivals it slows down to really nail down the safety aspect, and make sure the sky isn't tiled with representations of satisfied superintelligent utility functions...

MZ: I doubt there are many disagreements that there were other interesting inflection points. But Robin's using the best hard data on productivity growth that we have and it's hard to see those inflection points in the data. If someone can think of a way to get higher-resolution data covering those transitions, it would be fascinating to add them to our collection of historical cases.

Publicly not knowing earns humility points - save them and spend them wisely.

However, you're an expert, and people want to know what you think!

So, imagine you have a million valuable tokens, and have to spread them over the next 100 years. Imagine also that the value of the tokens gradually runs into diminishing returns as you get more of them - which makes you somewhat risk-averse. Imagine also that your longevity is independently assured. When some significant-machine-intelligence-milestone is reached, you get the tokens that you previously placed on that year. How would you spread the tokens?

there were other interesting inflection points. Robin's using the best hard data on productivity growth that we have and it's hard to see those inflection points in the data.

That's because the previous transitions occurred before Robin's data set starts.

For a while now I've viewed moores law as economically driven. There are generally advantages to having faster computers, but these computers have costs to develop. If there's a technological wall, funding starts going to it even before it's relevant in production chips. If a chipmaker stumbles onto an easy cheap advancement, it still pays to keep just ahead of their competitors, because they'll need money later for the next wall.
Moores law is the result of economic pressure to go a bit faster hitting technological barriers. So it's exponential but choppy.

If an AI achieves dominance, Then it won't have competition forcing it to be more efficient, and it will only spend resources optimizing chips if that fits it's goals. (if there's a payoff in it's own internal economy of resources)

Maybe it will run for a hundred years on the chip designs available at the time of its creation, before it decides it needs to improve them.

Not Likely, but faster chips are not a human female in a torn dress.

How do periods of stagnant growth, such as extinction level events in earth's history, effect the graphs? As the dinosaurs went extinct, did we jump straight to the start of the mammalian s-curve, or was there a prolonged growth plateau that when averaged out in the combined s-curve meta-graph, doesn't show up as being significant?

A singularity type phase-shift being so steep, even If growth were to grind down in the near future and become stagnant for 100s of years, wouldn't the meta-graph still show an overall fit when averaged out if the singularity occurred after some global catastrophe?

I guess I want to know what effect periods of <= 0 growth have on these meta-graphs.

The Austrians say that economics can only tell us qualitative rather than quantitative things. That's part of why many people don't take them seriously.

It seems reasonable to me to assign a ~1/4-1/2 probability to the previous series not continuing roughly as it has. So it would be only one or two bits of surprise for me.

I suspect it is near time for you to reveal to us your "weak inside view", i.e., the analysis that suggests to you that hand-coded AI is likely to appear in the next few decades, and that is likely to appear in the form of a single machine suddenly able to take over the world.

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