Paths To High-Level Machine Intelligence 2021-09-10T13:21:11.665Z


Comment by Daniel_Eth on Paths To High-Level Machine Intelligence · 2021-09-12T13:49:50.853Z · LW · GW

Thanks for the comments!

Re: The Hard Paths Hypothesis

I think it's very unlikely that Earth has seen other species as intelligent as humans (with the possible exception of other Homo species). In short, I suspect there is strong selection pressure for (at least many of) the different traits that allow humans to have civilization to go together. Consider dexterity – such skills allow one to use intelligence to make tools; that is, the more dexterous one is, the greater the evolutionary value of high intelligence, and the more intelligent one is, the greater the evolutionary value of dexterity. Similar positive feedback loops also seem likely between intelligence and: longevity, being omnivorous, having cumulative culture, hypersociality, language ability, vocal control, etc. 

Regarding dolphins and whales, it is true that many have more neurons than us, but they also have thin cortices, low neuronal packing densities, and low axonal conduction velocities (in addition to lower EQs than humans). 

Additionally, birds and mammals are both considered unusually intelligent for animals (more so than reptiles, amphibians, fish, etc), and both birds and mammals have seen (neurological evidence of) gradual trends of increasing (maximum) intelligence over the course of the past 100 MY or more (and even extant nonhuman great apes seem most likely to be somewhat smarter than their last common ancestors with humans). So if there was a previously intelligent species, I'd be scratching my head about when it would have evolved. While we can't completely rule out a previous species as smart as humans (we also can't completely rule out a previous technological species, for which all artifacts have been destroyed), I think the balance of evidence is pretty strongly against, though I'll admit that not everyone shares this view. Personally, I'd be absolutely shocked if there were 10+ (not very closely related) previous intelligent species, which is what would be required to reduce compute by just 1 OOM. (And even then, insofar as the different species shared a common ancestor, there still could be a hard step that the ancestor passed.)

But I do think it's the case that certain bottlenecks on Earth wouldn't be a bottleneck for engineers. For instance, I think there's a good chance that we simply got lucky in the past several hundred million years for the climate staying ~stable instead of spiraling into uninhabitable hothouse or snowball states (i.e., we may be subject to survivorship bias here); this seems very easy for human engineers to work around in simulations. The same is plausibly true for other bottlenecks as well.

Re: Brain imitation learning

My cop-out answer here is that this is already covered by the "other methods" section. My real answer is that the model isn't great at handling approaches that are intermediate between different methods. I agree it makes sense to continue to watch this space.

Comment by Daniel_Eth on Paths To High-Level Machine Intelligence · 2021-09-12T12:52:22.656Z · LW · GW


I agree that symbolic doesn't have to mean not bitter lesson-y (though in practice I think there are often effects in that direction). I might even go a bit further than you here and claim that a system with a significant amount of handcrafted aspects might still be bitter lesson-y, under the right conditions. The bitter lesson doesn't claim that the maximally naive and brute-force method possible will win, but instead that, among competing methods, more computationally-scalable methods will generally win over time (as compute increases). This shouldn't be surprising, as if methods A and B were both appealing enough to receive attention to begin with, then as compute increases drastically, we'd expect the method of the two that was more compute-leveraging to pull ahead. This doesn't mean that a different method C, which was more naive/brute-force than either A or B, but wasn't remotely competitive with A and B to begin with, would also pull ahead. Also, insofar as people are hardcoding in things that do scale well with compute (maybe certain types of biases, for instance), that may be more compatible with the bitter lesson than, say, hardcoding in domain knowledge.

Part of me also wonders what happens to the bitter lesson if compute really levels off. In such a world, the future gains from leveraging further compute don't seem as appealing, and it's possible larger gains can be had elsewhere.

Comment by Daniel_Eth on Paths To High-Level Machine Intelligence · 2021-09-12T12:17:38.723Z · LW · GW

I think very few people would explicitly articulate a view like that, but I also think there are people who hold a view along the lines of, "Moore will continue strong for a number of years, and then after that compute/$ will grow at <20% as fast" – in which case, if we're bottlenecked on hardware, whether Moore ends several years earlier vs later could have a large effect on timelines.

Comment by Daniel_Eth on Analogies and General Priors on Intelligence · 2021-08-24T17:12:02.491Z · LW · GW

One more crux that we should have included (under the section on "The Human Brain"):
"Human brain appears to be a scaled-up version of a more generic mammalian/primate brain"

Comment by Daniel_Eth on Analogies and General Priors on Intelligence · 2021-08-23T23:29:57.747Z · LW · GW

So just to be clear, the model isn't necessarily endorsing the claim, just saying that the claim is a potential crux.

Comment by Daniel_Eth on Covid 4/15: Are We Seriously Doing This Again · 2021-04-20T23:06:09.718Z · LW · GW

I think in practice allowing them to be sued for egregious malpractice would lead them to be more hesitant to approve, since I think people are much more likely to sue for damage from approved drugs than damage from being prevented from drugs, plus I think judges/juries would find those cases more sympathetic. I also think this standard would potentially cause them to be less likely to change course when they make a mistake and instead try to dig up evidence to justify their case.

Comment by Daniel_Eth on Covid 4/15: Are We Seriously Doing This Again · 2021-04-20T20:28:23.591Z · LW · GW

This is probably a good thing - I'd imagine that if you could sue the FDA, they'd be a lot more hesitant to approve anything.

Comment by Daniel_Eth on Birds, Brains, Planes, and AI: Against Appeals to the Complexity/Mysteriousness/Efficiency of the Brain · 2021-01-28T15:19:00.929Z · LW · GW

Yeah, that's fair - it's certainly possible that the things that make intelligence relatively hard for evolution may not apply to human engineers. OTOH, if intelligence is a bundle of different modules that all coexistent in humans and of which different animals have evolved in various proportions, that seems to point away from the blank slate/"all you need is scaling" direction.

Comment by Daniel_Eth on Birds, Brains, Planes, and AI: Against Appeals to the Complexity/Mysteriousness/Efficiency of the Brain · 2021-01-28T10:16:41.855Z · LW · GW

I think this is a good point, but I'd flag that the analogy might give the impression that intelligence is easier than it is - while animals have evolved flight multiple times by different paths (birds, insects, pterosaurs, bats) implying flight may be relatively easy, only one species has evolved intelligence.