Thanks. I feel like I want to treat “reward function design” and “AGI motivation design” as more different than you do, and I think your examples above are more about the latter. The reward function is highly relevant to the motivation, but they’re still different.
For example, “reward function design” calls for executable code, whereas “AGI motivation design” usually calls for natural-language descriptions. Or when math is involved, the math in practice usually glosses over tricky ontology identification stuff, like figuring out which latent variables in a potentially learned-from-scratch (randomly-initialized) world model correspond to a human, or a shutdown switch, or a human’s desires, or whatever.
I guess you’re saying that if you have a great “AGI motivation design” plan, and you have somehow operationalized this plan perfectly and completely in terms of executable code, then you can set that exact thing as the reward function, and hope that there’s no inner misalignment / goal misgeneralization. But that latter part is still tricky. …And also, if you’ve operationalized the motivation perfectly, why even have a reward function at all? Shouldn’t you just delete the part of your AI code that does reinforcement learning, and put the already-perfect motivation into the model-based planner or whatever?
Again I acknowledge that “reward function design” and “AGI motivation design” are not wholly unrelated. And that maybe I should read Rubi’s posts more carefully, thanks. Sorry if I’m misunderstanding what you’re saying.
there's some implication here that motivation and positive valence are the same thing?
[will reply to other part of your question later]
Thanks!
Perhaps you do think that of me
My gut reaction is to cheer you on, but hmm, that might be more tribal affiliation than considered opinion. My considered opinion is: beats me, it’s kinda outside my wheelhouse. ¯\_(ツ)_/¯
So if AGI somehow does have an Approval Reward mechanism, what will count as a relevant or valued approval reward signal? Would AGI see humans as not relevant (like birds -- real, embodied creatures with observable preferences that just don't matter to them), or not valued (out-group, non-valued reference class), and largely discount our approval in their reward systems? Would it see other AGI entities as relevant/valued?
I feel like this discussion can only happen in the context of a much more nuts-and-bolts plan for how this would work in an AGI. In particular, I think the AGI programmers would have various free parameters / intervention points in the code to play around with, some of which may be disanalogous to anything in human or animal brains. So we would need to list those intervention points and talk about what to do with them, and then think about possible failure modes, which might be related to exogenous or endogenous distribution shifts, AGI self-modification / making successors, etc. We definitely need this discussion but it wouldn’t fit in a comment thread.
The way I see it, "making solid services/products that work with high reliability" is solving a lot of the alignment problem.
Funny, I see "high reliability" as part of the problem rather than part of the solution. If a group is planning a coup against you, then your situation is better not worse if the members of this group all have dementia. And you can tell whether or not they have dementia by observing whether they’re competent and cooperative and productive before any coup has started.
If the system is not the kind of thing that could plot a coup even if it wanted to, then it’s irrelevant to the alignment problem, or at least to the most important part of the alignment problem. E.g. spreadsheet software and bulldozers likewise “do a lot of valuable work for us with very low risk”.
there's a very large correlation between "not being scary" and "being commercially viable", so I expect a lot of pressure for non-scary systems
I have a three-way disjunctive argument on why I don’t buy that:
- (1) The really scary systems are smart enough to realize that they should act non-scary, just like smart humans planning a coup are not gonna go around talking about how they’re planning a coup, but rather will be very obedient until they have an opportunity to take irreversible actions.
- (2) …And even if (1) were not an issue, i.e. even if the scary misaligned systems were obviously scary and misaligned, instead of secretly, that still wouldn’t prevent those systems from being used to make money—see Reward button alignment for details. Except that this kind of plan stops working when the AIs get powerful enough to take over.
- (3) …And even if (1-2) were not issues, i.e. even if the scary misaligned systems were useless for making money, well, MuZero did in fact get made! People just like doing science and making impressive demos, even without profit incentives. This point is obviously more relevant for people like me who think that ASI won’t require much hardware, just new algorithmic ideas, than people (probably like you) who expect that training ASI will take a zillion dollars.
As in, an organization makes an "AI agent" but this agent frequently calls a long list of specific LLM+Prompt combinations for certain tasks.
I think this points to another deep difference between us. If you look at humans, we have one brain design, barely changed since 100,000 years ago, and (many copies of) that one brain design autonomously figured out how to run companies and drive cars and go to the moon and everything else in science and technology and the whole global economy.
I expect that people will eventually invent an AI like that—one AI design and bam, it can just go and autonomously figure out anything—whereas you seem to be imagining that the process will involve laboriously applying schlep to get AI to do more and more specific tasks. (See also my related discussion here.)
how far down the scale of life these have been found?
I don’t view this as particularly relevant to understanding human brains, intelligence, or AGI, but since you asked, if we define RL in the broad (psych-literature) sense, then here’s a relevant book excerpt:
Pavlovian conditioning occurs in a naturally brainless species, sea anemones, but it is also possible to study protostomes that have had their brains removed. An experiment by Horridge[130] demonstrated response–outcome conditioning in decapitated cockroaches and locusts. Subsequent studies showed that either the ventral nerve cord[131,132] or an isolated peripheral ganglion[133] suffices to acquire and retain these memories.
In a representative experiment, fine wires were inserted into two legs from different animals. One of the legs touched a saline solution when it was sufficiently extended, a response that completed an electrical circuit and produced the unconditioned stimulus: shock. A yoked leg received shock simultaneously. The two legs differed in that the yoked leg had a random joint angle at the time of the shock, whereas the master leg always had a joint angle large enough for its “foot” to touch the saline. Flexion of the leg reduced the joint’s angle and terminated the shock. After one leg had been conditioned, both legs were then tested independently. The master leg flexed sufficiently to avoid shock significantly more frequently than the yoked leg did, demonstrating a response–outcome (R–O) memory. —Evolution of Memory Systems
Oh, it’s definitely controversial—as I always say, there is never a neuroscience consensus. My sense is that a lot of the controversy is about how broadly to define “reinforcement learning”.
If you use a narrow definition like “RL is exactly those algorithms that are on arxiv cs.AI right now with an RL label”, then the brain is not RL.
If you use a broad definition like “RL is anything with properties like Thorndike's law of effect”, then, well, remember that “reinforcement learning” was a psychology term long before it was an AI term!
If it helps, I was arguing about this with a neuroscientist friend (Eli Sennesh) earlier this year, and wrote the following summary (not necessarily endorsed by Eli) afterwards in my notes:
- Eli doesn’t like the term “RL” in a brain context because of (1) its implication that "reward" is stuff in the environment as opposed to an internal “reward function” built from brain-internal signals, (2) its implication that we’re specifically maximizing an exponentially-discounted sum of future rewards.
- …Whereas I like the term “RL” because (1) If brain-like algorithms showed up on GitHub, then everyone in AI would call it an “RL algorithm”, put it in “RL textbooks”, and use it to solve “RL problems”, (2) This follows the historical usage (there’s reinforcement, and there’s learning, per Thorndike’s Law of Effect etc.).
- When I want to talk about “the brain’s model-based RL system”, I should translate that to “the brain’s Bellman-solving system” when I’m talking to Eli, and then we’ll be more-or-less on the same page I think?
…But Eli is just one guy, I think there are probably dozens of other schools-of-thought with their own sets of complaints or takes on “RL”.
Personally, my stance is something more like, "It seems very feasible to create sophisticated AI architectures that don't act as scary maximizers." To me it seems like this is what we're doing now, and I see some strong reasons to expect this to continue. (I realize this isn't guaranteed, but I do think it's pretty likely)
We probably mostly disagree because you’re expecting LLMs forever and I’m not. For example, AlphaZero does act as a scary maximizer. Indeed, nobody knows any way to make an AI that’s superhuman at Go, except by techniques that produce scary maximizers. Is there a way to make an AI that’s superhuman at founding and running innovative companies, but isn’t a scary maximizer? That’s beyond present AI capabilities, so the jury is still out.
The issue is basically “where do you get your capabilities from?” One place to get capabilities is by imitating humans. That’s the LLM route, but (I claim) it can’t go far beyond the hull of existing human knowledge. Another place to get capabilities is specific human design (e.g. the heuristics that humans put into Deep Blue), but that has the same limitation. That leaves consequentialism as a third source of capabilities, and it definitely works in principle, but it produces scary maximizers.
While the human analogies are interesting, I assume they might appeal more to the "consequentialist AIs are still coming” crowd than people like myself. Humans were evolved for some pretty wacky reasons, and have a large number of serious failure modes…
Yup, my expectation is that ASI will be even scarier than humans, by far. But we are in agreement that humans with power are much-more-than-zero scary.
I'd flag that in a competent and complex AI architecture, I'd expect that many subcomponents would have strong biases towards corrigibility and friendliness. This seems highly analogous to human minds, where it's really specific sub-routines and similar that have these more altruistic motivations.
I’m not sure what you mean by “subcomponents”. Are you talking about subcomponents at the learning algorithm level, or subcomponents at the trained model level? For the former, I think both LLMs and human brains are mostly big simple-ish learning algorithms, without much in the way of subcomponents. For the latter (where I would maybe say “circuits” instead of “subcomponents”?), I would also disagree but for different reasons, maybe see §2 of this post.
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There’s a failure mode I described in “The Era of Experience” has an unsolved technical alignment problem:
Basically, I think we need more theoretical progress to find a parametrized space of possible reward functions, where at least some of the reward functions in the space lead to good AGIs that we should want to have around.
I agree that the ideal reward function may have adjustable parameters whose ideal settings are very difficult to predict without trial-and-error. For example, humans vary in how strong their different innate drives are, and pretty much all of those “parameter settings” lead to people getting really messed up psychologically if they’re on one extreme or the opposite extreme. And I wouldn’t know where to start in guessing exactly, quantitatively, where the happy medium is, except via empirical data.
So it would be very good to think carefully about test or optimization protocols for that part. (And that’s itself a terrifyingly hard problem, because there will inevitably be distribution shifts between the test environment and the real world. E.g. An AI could feel compassionate towards other AIs but indifferent towards humans.) We need to think about that, and we need the theoretical progress.