Strong minds are the most structurally rich things ever. That doesn’t mean they have high algorithmic complexity; obviously brains are less algorithmically complex than entire organisms, and the relevant aspects of brains are presumably considerably simpler than actual brains. But still, IDK, it just seems weird to me to expect to make such an object “by default” or something? Craig Venter made a quasi-synthetic lifeform—but how long would it take us to make a minimum viable unbounded invasive organic replicator actually from scratch, like without copying DNA sequences from existing lifeforms?
I think I don’t understand this argument. In creating AI we can draw on training data, which breaks the analogy to making a replicator actually from scratch (are you using a premise that this is a dead end, or something, because “Nearly all [thinkers] do not write much about the innards of their thinking processes...”?). We’ve seen that supervised (EDIT: unsupervised) learning and RL (and evolution) can create structural richness (if I have the right idea of what you mean) out of proportion to the understanding that went into them. Of course this doesn’t mean any particular learning process is able to create a strong mind, but, idk, I don’t see a way to put a strong lower bound on how much more powerful a learning process is necessary, and ISTM observations so far suggest ‘less than I would have guessed’.
(EDIT: Maybe (you’d say) I should be drawing such a strong lower bound — or a lower bound on the needed difference from current techniques, not ‘power level’ — from the point about sample efficiency...? Like maybe I should think that we don’t have a good enough sample space to learn over and will probably have to jump far outside it; this comment seems in that direction.)
(Nor do I get what view you’re paraphrasing as ‘expecting to make a strong mind “by default”’. Did LLMs or AlphaZero come about “by default”?)
(EDIT: I feel like I get “by default” more after looking again at your “Let me restate my view again” passage here.)
I think my timelines would have been considered normalish among X-risk people 15 years ago? And would have been considered shockingly short by most AI people.
Unfortunately I can’t find the written artifact that came out of it, but I (very imperfectly) recall a large conversation around SIAI in 2010 where, IIRC, a 2040 median was pretty typical. I agree that “X-risk people” more broadly had longer timelines, and “most AI people” much longer.
I think most of the difference is in how we’re updating, rather than on priors? IDK.
Yeah, in particular it seems like I’m updating more than you from induction on the conceptual-progress-to-capabilities ratio we’ve seen so far / on what seem like surprises to the ‘we need lots of ideas’ view. (Or maybe you disagree about observations there, or disagree with that frame.) (The “missing update” should weaken this induction, but doesn’t invalidate it IMO.)
I think I don’t understand this argument. In creating AI we can draw on training data, which breaks the analogy to making a replicator actually from scratch (are you using a premise that this is a dead end, or something, because “Nearly all [thinkers] do not write much about the innards of their thinking processes...”?).
You’re technically right that the analogy is broken in that way, yeah. Likewise, if someone gleans substantial chunks of the needed Architecture by looking at scans of brains. But yes, as you say, I think the actual data (in both cases) doesn’t directly tell you what you need to know, by any stretch. (To riff on an analogy from Kabir Kumar: it’s sort of like trying to infer the inner workings of a metal casting machine, purely by observing price fluctuations for various commodities. It’s probably possible in theory, but staring at the price fluctuations—which are a highly mediated / garbled / fuzzed emanation from the “guts” of various manufacturing processes—is not a good way to discover the important ideas about how casting machines can work. Cf. https://www.lesswrong.com/posts/unCG3rhyMJpGJpoLd/koan-divining-alien-datastructures-from-ram-activations )
We’ve seen that supervised learning and RL (and evolution) can create structural richness (if I have the right idea of what you mean) out of proportion to the understanding that went into them.
Not sure I buy the claims about SL and RL. In the case of SL, it’s only going “a little ways away from the data”, in terms of the structure you get. Or so I claim uncertainly. (Hm… maybe the metaphor of “distance from the data” is quite bad.… really I mean “it’s only exploring a pretty impoverished sector in structurespace, partly due to data and partly due to other Architecture”.) In the case of RL, what are the successes in terms of gaining new learned structure? There’s going to be some—we can point to AlphaZero, and maybe some robotics things—but I’m skeptical that this actually represents all that much structural richness. The actual NNs in AlphaZero would have some nontrivial structure, but hard to tell how much, and it’s going to be pretty narrow / circumscribed, e.g. it wouldn’t represent most interesting math concepts.
Anyway, the claim is of course true of evolution. The general point is true, that learning systems can be powerful, and specifically high-leverage in various ways (e.g. lots of learning from small algorithmic complexity fingerprint as with evolution or Solomonoff induction, or from fairly small compute as in humans).
Of course this doesn’t mean any particular learning process is able to create a strong mind, but, idk, I don’t see a way to put a strong lower bound on how much more powerful a learning process is necessary,
Right, no one knows. Could be next month that everyone dies from AGI. The only claims I’d really argue strongly would be claims like
If you have median 2029 or similar, either you’re overconfident or you know something dispositive that I don’t know.
If you have probability of AGI by 2029 less than .05%, either you’re overconfident or you know something dispositive that I don’t know.
Besides my comments about the bitter lesson and about the richness of evolution’s search, I’ll also say that it just seems to me like there’s lots of ideas—at the abstract / fundamental / meta level of learning and thinking—that have yet to be put into practice in AI. I wrote in the OP:
The self-play that evolution uses (and the self-play that human children use) is much richer, containing more structural ideas, than the idea of having an agent play a game against a copy of itself.
IME if you think about these sorts of things—that is, if you think about how the 2.5 known great and powerful optimization processes (evolution, humans, humanity/science) do their impressive thing that they do—if you think about that, you see lots of sorts of feedback arrangements and ways of exploring the space of structures / algorithms, many of which are different in some fundamental character from what’s been tried so far in AI. And, these things don’t add up, in my head, to a general intelligence—though of course that is only a deficiency in my imagination, one way or another.
(EDIT: Maybe (you’d say) I should be drawing such a strong lower bound from the point about sample efficiency...?)
I don’t personally lean super heavily on the sample efficiency thing. I mean, if we see a system that’s truly only trained on some human data that’s of size less than 10x the amount that a well-read adult human has read (plus compute / thinking), and it performs like GPT-4 or similar, that would be really weird and surprising, and I would be confused, and I’d be somewhat more scared. But I don’t think it would necessarily imply that you’re about to get AGI.
Conversely, I definitely don’t think that high sample complexity strongly implies that you’re not about to get AGI. (Well, I guess if you’re about to get AGI, there should probably be spikes in sample efficiency in specific areas—e.g. you’d be able to invent much more interesting math with little or no data, whereas previously you had to train on vast math corpora. But we don’t necessarily have to observe these domain spikes before dying of nanopox.)
Yeah, in particular it seems like I’m updating more than you from induction on the conceptual-progress-to-capabilities ratio we’ve seen so far / on what seem like surprises to the ‘we need lots of ideas’ view. (Or maybe you disagree about observations there, or disagree with that frame.) (The “missing update” should weaken this induction, but doesn’t invalidate it IMO.)
Yeah… To add a bit of color, I’d say I’m pretty wary of mushing. Like, we mush together all “capabilities” and then update on how much “capabilities” our current learning programs have. I don’t feel like that sort of reasoning ought to work very well. But I haven’t yet articulated how mushing is anything more specific than categorization, if it is more specific. Maybe what I mean by mushing is “sticking to a category and hanging lots of further cognition (inferences, arguments, plans) on the category, without putting in suitable efforts to refine the category into subcategories”. I wrote:
We should have been trying hard to retrospectively construct new explanations that would have predicted the observations. Instead we went with the best PREEXISTING explanation that we already had.
I think I don’t understand this argument. In creating AI we can draw on training data, which breaks the analogy to making a replicator actually from scratch (are you using a premise that this is a dead end, or something, because “Nearly all [thinkers] do not write much about the innards of their thinking processes...”?). We’ve seen that supervised (EDIT: unsupervised) learning and RL (and evolution) can create structural richness (if I have the right idea of what you mean) out of proportion to the understanding that went into them. Of course this doesn’t mean any particular learning process is able to create a strong mind, but, idk, I don’t see a way to put a strong lower bound on how much more powerful a learning process is necessary, and ISTM observations so far suggest ‘less than I would have guessed’.
(EDIT: Maybe (you’d say) I should be drawing such a strong lower bound — or a lower bound on the needed difference from current techniques, not ‘power level’ — from the point about sample efficiency...? Like maybe I should think that we don’t have a good enough sample space to learn over and will probably have to jump far outside it; this comment seems in that direction.)
(Nor do I get what view you’re paraphrasing as ‘expecting to make a strong mind “by default”’. Did LLMs or AlphaZero come about “by default”?)
(EDIT: I feel like I get “by default” more after looking again at your “Let me restate my view again” passage here.)
Unfortunately I can’t find the written artifact that came out of it, but I (very imperfectly) recall a large conversation around SIAI in 2010 where, IIRC, a 2040 median was pretty typical. I agree that “X-risk people” more broadly had longer timelines, and “most AI people” much longer.
Yeah, in particular it seems like I’m updating more than you from induction on the conceptual-progress-to-capabilities ratio we’ve seen so far / on what seem like surprises to the ‘we need lots of ideas’ view. (Or maybe you disagree about observations there, or disagree with that frame.) (The “missing update” should weaken this induction, but doesn’t invalidate it IMO.)
You’re technically right that the analogy is broken in that way, yeah. Likewise, if someone gleans substantial chunks of the needed Architecture by looking at scans of brains. But yes, as you say, I think the actual data (in both cases) doesn’t directly tell you what you need to know, by any stretch. (To riff on an analogy from Kabir Kumar: it’s sort of like trying to infer the inner workings of a metal casting machine, purely by observing price fluctuations for various commodities. It’s probably possible in theory, but staring at the price fluctuations—which are a highly mediated / garbled / fuzzed emanation from the “guts” of various manufacturing processes—is not a good way to discover the important ideas about how casting machines can work. Cf. https://www.lesswrong.com/posts/unCG3rhyMJpGJpoLd/koan-divining-alien-datastructures-from-ram-activations )
Not sure I buy the claims about SL and RL. In the case of SL, it’s only going “a little ways away from the data”, in terms of the structure you get. Or so I claim uncertainly. (Hm… maybe the metaphor of “distance from the data” is quite bad.… really I mean “it’s only exploring a pretty impoverished sector in structurespace, partly due to data and partly due to other Architecture”.) In the case of RL, what are the successes in terms of gaining new learned structure? There’s going to be some—we can point to AlphaZero, and maybe some robotics things—but I’m skeptical that this actually represents all that much structural richness. The actual NNs in AlphaZero would have some nontrivial structure, but hard to tell how much, and it’s going to be pretty narrow / circumscribed, e.g. it wouldn’t represent most interesting math concepts.
Anyway, the claim is of course true of evolution. The general point is true, that learning systems can be powerful, and specifically high-leverage in various ways (e.g. lots of learning from small algorithmic complexity fingerprint as with evolution or Solomonoff induction, or from fairly small compute as in humans).
Right, no one knows. Could be next month that everyone dies from AGI. The only claims I’d really argue strongly would be claims like
If you have median 2029 or similar, either you’re overconfident or you know something dispositive that I don’t know.
If you have probability of AGI by 2029 less than .05%, either you’re overconfident or you know something dispositive that I don’t know.
Besides my comments about the bitter lesson and about the richness of evolution’s search, I’ll also say that it just seems to me like there’s lots of ideas—at the abstract / fundamental / meta level of learning and thinking—that have yet to be put into practice in AI. I wrote in the OP:
IME if you think about these sorts of things—that is, if you think about how the 2.5 known great and powerful optimization processes (evolution, humans, humanity/science) do their impressive thing that they do—if you think about that, you see lots of sorts of feedback arrangements and ways of exploring the space of structures / algorithms, many of which are different in some fundamental character from what’s been tried so far in AI. And, these things don’t add up, in my head, to a general intelligence—though of course that is only a deficiency in my imagination, one way or another.
I don’t personally lean super heavily on the sample efficiency thing. I mean, if we see a system that’s truly only trained on some human data that’s of size less than 10x the amount that a well-read adult human has read (plus compute / thinking), and it performs like GPT-4 or similar, that would be really weird and surprising, and I would be confused, and I’d be somewhat more scared. But I don’t think it would necessarily imply that you’re about to get AGI.
Conversely, I definitely don’t think that high sample complexity strongly implies that you’re not about to get AGI. (Well, I guess if you’re about to get AGI, there should probably be spikes in sample efficiency in specific areas—e.g. you’d be able to invent much more interesting math with little or no data, whereas previously you had to train on vast math corpora. But we don’t necessarily have to observe these domain spikes before dying of nanopox.)
Yeah… To add a bit of color, I’d say I’m pretty wary of mushing. Like, we mush together all “capabilities” and then update on how much “capabilities” our current learning programs have. I don’t feel like that sort of reasoning ought to work very well. But I haven’t yet articulated how mushing is anything more specific than categorization, if it is more specific. Maybe what I mean by mushing is “sticking to a category and hanging lots of further cognition (inferences, arguments, plans) on the category, without putting in suitable efforts to refine the category into subcategories”. I wrote: