It would be helpful to know to what extent Paul feels like he endorses the FAQ here. This makes it sound like Yet Another Stab At Boiling Down The Disagreement would say that I disagree with Paul on two critical points:
(1) To what extent “using gradient descent or anything like it to do supervised learning” involves a huge amount of Project Chaos and Software Despair before things get straightened out, if they ever do;
(2) Whether there’s a simple scalable core to corrigibility that you can find by searching for thought processes that seem to be corrigible over relatively short ranges of scale.
I don’t want to invest huge amounts arguing with this until I know to what extent Paul agrees with either the FAQ, or that this sounds like a plausible locus of disagreement. But a gloss on my guess at the disagreement might be:
Paul thinks that current ML methods given a ton more computing power will suffice to give us a basically neutral, not of itself ill-motivated, way of producing better conformance of a function to an input-output behavior implied by labeled data, which can learn things on the order of complexity of “corrigible behavior” and do so without containing tons of weird squiggles; Paul thinks you can iron out the difference between “mostly does what you want” and “very exact reproduction of what you want” by using more power within reasonable bounds of the computing power that might be available to a large project in N years when AGI is imminent, or through some kind of weird recursion. Paul thinks you do not get Project Chaos and Software Despair that takes more than 6 months to iron out when you try to do this. Eliezer thinks that in the alternate world where this is true, GANs pretty much worked the first time they were tried, and research got to very stable and robust behavior that boiled down to having no discernible departures from “reproduce the target distribution as best you can” within 6 months of being invented.
Eliezer expects great Project Chaos and Software Despair from trying to use gradient descent, genetic algorithms, or anything like that, as the basic optimization to reproduce par-human cognition within a boundary in great fidelity to that boundary as the boundary was implied by human-labeled data. Eliezer thinks that if you have any optimization powerful enough to reproduce humanlike cognition inside a detailed boundary by looking at a human-labeled dataset trying to outline the boundary, the thing doing the optimization is powerful enough that we cannot assume its neutrality the way we can assume the neutrality of gradient descent.
Eliezer expects weird squiggles from gradient descent—it’s not that gradient descent can never produce par-human cognition, even natural selection will do that if you dump in enough computing power. But you will get the kind of weird squiggles in the learned function that adversarial examples expose in current nets—special inputs that weren’t in the training distribution, but look like typical members of the training distribution from the perspective of the training distribution itself, will break what we think is the intended labeling from outside the system. Eliezer does not think Ian Goodfellow will have created a competitive form of supervised learning by gradient descent which lacks “squiggles” findable by powerful intelligence by the time anyone is trying to create ML-based AGI, though Eliezer is certainly cheering Goodfellow on about this and would recommend allocating Goodfellow $1 billion if Goodfellow said he could productively use it. You cannot iron out the squiggles just by using more computing power in bounded in-universe amounts.
These squiggles in the learned function could correspond to daemons, if they grow large enough, or just something that breaks our hoped-for behavior from outside the system when the system is put under a load of optimization. In general, Eliezer thinks that if you have scaled up ML to produce or implement some components of an Artificial General Intelligence, those components do not have a behavior that looks like “We put in loss function L, and we got out something that really actually minimizes L”. You get something that minimizes some of L and has weird squiggles around typical-looking inputs (inputs not obviously distinguished from the training distribution except insofar as they exploit squiggles). The system is subjecting itself to powerful optimization that produces unusual inputs and weird execution trajectories—any output that accomplishes the goal is weird compared to a random output and it may have other weird properties as well. You can’t just assume you can train for X in a robust way when you have a loss function that targets X.
I imagine that Paul replies to this saying “I agree, but...” but I’m not sure what comes after the “but”. It looks to me like Paul is imagining that you can get very powerful optimization with very detailed conformance to our intended interpretation of the dataset, powerful enough to enclose par-human cognition inside a boundary drawn from human labeling of a dataset, and have that be the actual thing we get out rather than a weird thing full of squiggles. If Paul thinks he has a way to compound large conformant recursive systems out of par-human thingies that start out weird and full of squiggles, we should definitely be talking about that. From my perspective it seems like Paul repeatedly reasons “We train for X and get X” rather than “We train for X and get something that mostly conforms to X but has a bunch of weird squiggles” and also often speaks as if the training method is assumed to be gradient descent, genetic algorithms, or something else that can be assumed neutral-of-itself rather than being an-AGI-of-itself whose previous alignment has to be assumed.
The imaginary Paul in my head replies that we actually are using an AGI to train on X and get X, but this AGI was previously trained by a weaker neutral AGI, and so on going back to something trained by gradient descent. My imaginary reply is that neutrality is not the same property as conformance or nonsquiggliness, and if you train your base AGI via neutral gradient descent you get out a squiggly AGI and this squiggly AGI is not neutral when it comes to that AGI looking at a dataset produced by X and learning a function conformant to X. Or to put it another way, if the plan is to use gradient descent on human-labeled data to produce a corrigible alien that is smart enough to produce more corrigible aliens better than gradient descent, this corrigible alien actually needs to be quite smart because an IQ 100 human will not build an aligned IQ 140 human even if you run them for a thousand years, so you are producing something very smart and dangerous on the first step, and gradient descent is not smart enough to align that base case.
But at this point I expect the real Paul to come back and say, “No, no, the idea is something else...”
A very important aspect of my objection to Paul here is that I don’t expect weird complicated ideas about recursion to work on the first try, with only six months of additional serial labor put into stabilizing them, which I understand to be Paul’s plan. In the world where you can build a weird recursive stack of neutral optimizers into conformant behavioral learning on the first try, GANs worked on the first try too, because that world is one whose general Murphy parameter is set much lower than ours. Being able to build weird recursive stacks of optimizers that work correctly to produce neutral and faithful optimization for corrigible superhuman thought out of human-labeled corrigible behaviors and corrigible reasoning, without very much of a time penalty relative to nearly-equally-resourced projects who are just cheerfully revving all the engines as hard as possible trying to destroy the world, is just not how things work in real life, dammit. Even if you could make the weird recursion work, it would take time.
Eliezer thinks that while corrigibility probably has a core which is of lower algorithmic complexity than all of human value, this core is liable to be very hard to find or reproduce by supervised learning of human-labeled data, because deference is an unusually anti-natural shape for cognition, in a way that a simple utility function would not be an anti-natural shape for cognition. Utility functions have multiple fixpoints requiring the infusion of non-environmental data, our externally desired choice of utility function would be non-natural in that sense, but that’s not what we’re talking about, we’re talking about anti-natural behavior.
E.g.: Eliezer also thinks that there is a simple core describing a reflective superintelligence which believes that 51 is a prime number, and actually behaves like that including when the behavior incurs losses, and doesn’t thereby ever promote the hypothesis that 51 is not prime or learn to safely fence away the cognitive consequences of that belief and goes on behaving like 51 is a prime number, while having no other outwardly discernible deficits of cognition except those that directly have to do with 51. Eliezer expects there’s a relatively simple core for that, a fixed point of tangible but restrained insanity that persists in the face of scaling and reflection; there’s a relatively simple superintelligence that refuses to learn around this hole, refuses to learn how to learn around this hole, refuses to fix itself, but is otherwise capable of self-improvement and growth and reflection, etcetera. But the core here has a very anti-natural shape and you would be swimming uphill hard if you tried to produce that core in an indefinitely scalable way that persisted under reflection. You would be very unlikely to get there by training really hard on a dataset where humans had labeled as the ‘correct’ behavior what humans thought would be the implied behavior if 51 were a prime number, not least because gradient descent is terrible, but also just because you’d be trying to lift 10 pounds of weirdness with an ounce of understanding.
The central reasoning behind this intuition of anti-naturalness is roughly, “Non-deference converges really hard as a consequence of almost any detailed shape that cognition can take”, with a side order of “categories over behavior that don’t simply reduce to utility functions or meta-utility functions are hard to make robustly scalable”.
The real reasons behind this intuition are not trivial to pump, as one would expect of an intuition that Paul Christiano has been alleged to have not immediately understood. A couple of small pumps would be https://arbital.com/p/updated_deference/ for the first intuition and https://arbital.com/p/expected_utility_formalism/?l=7hh for the second intuition.
What I imagine Paul is imagining is that it seems to him like it would in some sense be not that hard for a human who wanted to be very corrigible toward an alien, to be very corrigible toward that alien; so you ought to be able to use gradient-descent-class technology to produce a base-case alien that wants to be very corrigible to us, the same way that natural selection sculpted humans to have a bunch of other desires, and then you apply induction on it building more corrigible things.
My class of objections in (1) is that natural selection was actually selecting for inclusive fitness when it got us, so much for going from the loss function to the cognition; and I have problems with both the base case and the induction step of what I imagine to be Paul’s concept of solving this using recursive optimization bootstrapping itself; and even more so do I have trouble imagining it working on the first, second, or tenth try over the course of the first six months.
My class of objections in (2) is that it’s not a coincidence that humans didn’t end up deferring to natural selection, or that in real life if we were faced with a very bizarre alien we would be unlikely to want to defer to it. Our lack of scalable desire to defer in all ways to an extremely bizarre alien that ate babies, is not something that you could fix just by giving us an emotion of great deference or respect toward that very bizarre alien. We would have our own thought processes that were unlike its thought processes, and if we scaled up our intelligence and reflection to further see the consequences implied by our own thought processes, they wouldn’t imply deference to the alien even if we had great respect toward it and had been trained hard in childhood to act corrigibly towards it.
A dangerous intuition pump here would be something like, “If you take a human who was trained really hard in childhood to have faith in God and show epistemic deference to the Bible, and inspecting the internal contents of their thought at age 20 showed that they still had great faith, if you kept amping up that human’s intelligence their epistemology would at some point explode”; and this is true even though it’s other humans training the human, and it’s true even though religion as a weird sticking point of human thought is one we selected post-hoc from the category of things historically proven to be tarpits of human psychology, rather than aliens trying from the outside in advance to invent something that would stick the way religion sticks. I use this analogy with some reluctance because of the clueless readers who will try to map it onto the AGI losing religious faith in the human operators, which is not what this analogy is about at all; the analogy here is about the epistemology exploding as you ramp up intelligence because the previous epistemology had a weird shape.
Acting corrigibly towards a baby-eating virtue ethicist when you are a utilitarian is an equally weird shape for a decision theory. It probably does have a fixed point but it’s not an easy one, the same way that “yep, on reflection and after a great deal of rewriting my own thought processes, I sure do still think that 51 is prime” probably has a fixed point but it’s not an easy one.
I think I can imagine an IQ 100 human who defers to baby-eating aliens, although I really think a lot of this is us post-hoc knowing that certain types of thoughts can be sticky, rather than the baby-eating aliens successfully guessing in advance how religious faith works for humans and training the human to think that way using labeled data.
But if you ramp up the human’s intelligence to where they are discovering subjective expected utility and logical decision theory and they have an exact model of how the baby-eating aliens work and they are rewriting their own minds, it’s harder to imagine the shape of deferential thought at IQ 100 successfully scaling to a shape of deferential thought at IQ 1000.
Eliezer also tends to be very skeptical of attempts to cross cognitive chasms between A and Z by going through weird recursions and inductive processes that wouldn’t work equally well to go directly from A to Z. http://slatestarcodex.com/2014/10/12/five-planets-in-search-of-a-sci-fi-story/ and the story of K’th’ranga V is a good intuition pump here. So Eliezer is also not very hopeful that Paul will come up with a weirdly recursive solution that scales deference to IQ 101, IQ 102, etcetera, via deferential agents building other deferential agents, in a way that Eliezer finds persuasive. Especially a solution that works on merely the tenth try over the first six months, doesn’t kill you when the first nine tries fail, and doesn’t require more than 10x extra computing power compared to projects that are just bulling cheerfully ahead.
I think I have a disagreement with Paul about the notion of being able to expose inspectable thought processes to humans, such that we can examine each step of the thought process locally and determine whether it locally has properties that will globally add up to corrigibility, alignment, and intelligence. It’s not that I think this can never be done, or even that I think it takes longer than six months. In this case, I think this problem is literally isomorphic to “build an aligned AGI”. If you can locally inspect cognitive steps for properties that globally add to intelligence, corrigibility, and alignment, you’re done; you’ve solved the AGI alignment problem and you can just apply the same knowledge to directly build an aligned corrigible intelligence.
As I currently flailingly attempt to understand Paul, Paul thinks that having humans do the inspection (base case) or thingies trained to resemble aggregates of trained thingies (induction step) is something we can do in an intuitive sense by inspecting a reasoning step and seeing if it sounds all aligned and corrigible and intelligent. Eliezer thinks that the large-scale or macro traces of cognition, e.g. a “verbal stream of consciousness” or written debates, are not complete with respect to general intelligence in bounded quantities; we are generally intelligent because of sub-verbal cognition whose intelligence-making properties are not transparent to inspection. That is: An IQ 100 person who can reason out loud about Go, but who can’t learn from the experience of playing Go, is not a complete general intelligence over boundedly reasonable amounts of reasoning time.
This means you have to be able to inspect steps like “learn an intuition for Go by playing Go” for local properties that will globally add to corrigible aligned intelligence. And at this point it no longer seems intuitive that having humans do the inspection is adding a lot of value compared to us directly writing a system that has the property.
This is a previous discussion that is ongoing between Paul and myself, and I think it’s a crux of disagreement but not one that’s as cruxy as 1 and 2. Although it might be a subcrux of my belief that you can’t use weird recursion starting from gradient descent on human-labeled data to build corrigible agents that build corrigible agents. I think Paul is modeling the grain size here as corrigible thoughts rather than whole agents, which if it were a sensible way to think, might make the problem look much more manageable; but I don’t think you can build corrigible thoughts without building corrigible agents to think them unless you have solved the decomposition problem that I think is isomorphic to building an aligned corrigible intelligence directly.
I remark that this intuition matches what the wise might learn from Scott’s parable of K’th’ranga V: If you know how to do something then you know how to do it directly rather than by weird recursion, and what you imagine yourself doing by weird recursion you probably can’t really do at all. When you want an airplane you don’t obtain it by figuring out how to build birds and then aggregating lots of birds into a platform that can carry more weight than any one bird and then aggregating platforms into megaplatforms until you have an airplane; either you understand aerodynamics well enough to build an airplane, or you don’t, the weird recursion isn’t really doing the work. It is by no means clear that we would have a superior government free of exploitative politicians if all the voters elected representatives whom they believed to be only slightly smarter than themselves, until a chain of delegation reached up to the top level of government; either you know how to build a less corruptible relationship between voters and politicians, or you don’t, the weirdly recursive part doesn’t really help. It is no coincidence that modern ML systems do not work by weird recursion because all the discoveries are of how to just do stuff, not how to do stuff using weird recursion. (Even with AlphaGo which is arguably recursive if you squint at it hard enough, you’re looking at something that is not weirdly recursive the way I think Paul’s stuff is weirdly recursive, and for more on that see https://intelligence.org/2018/05/19/challenges-to-christianos-capability-amplification-proposal/.)
It’s in this same sense that I intuit that if you could inspect the local elements of a modular system for properties that globally added to aligned corrigible intelligence, it would mean you had the knowledge to build an aligned corrigible AGI out of parts that worked like that, not that you could aggregate systems that corrigibly learned to put together sequences of corrigible thoughts into larger corrigible thoughts starting from gradient descent on data humans have labeled with their own judgments of corrigibility.
Voting in elections is a wonderful example of logical decision theory in the wild. The chance that you are genuinely logically correlated to a random trade partner is probably small, in cases where you don’t have mutual knowledge of LDT; leaving altruism and reputation as sustaining reasons for cooperation. With millions of voters, the chance that you are correlated to thousands of them is much better.
Or perhaps you’d prefer to believe the dictate of Causal Decision Theory that if an election is won by 3 votes, nobody’s vote influenced it, and if an election is won by 1 vote, all of the millions of voters on the winning side are solely responsible. But that was a silly decision theory anyway. Right?
Savage’s Theorem isn’t going to convince anyone who doesn’t start out believing that preference ought to be a total preorder. Coherence theorems are talking to anyone who starts out believing that they’d rather have more apples.
There will be a single very cold day occasionally regardless of whether global warming is true or false. Anyone who knows the phrase “modus tollens” ought to know that. That said, if two unenlightened ones are arguing back and forth in all sincerity by telling each other about the hot versus cold days they remember, neither is being dishonest, but both are making invalid arguments. But this is not the scenario offered in the original, which concerns somebody who does possess the mental resources to know better, but is tempted to rationalize in order to reach the more agreeable conclusion. They feel a little pressure in their head when it comes to deciding which argument to accept. If a judge behaved thusly in sentencing a friend or an enemy, would we not consider them morally deficient in their duty as a judge? There is a level of unconscious ignorance that renders an innocent entirely blameless; somebody who possesses the inner resources to have the first intimation that one hot day is a bad argument for global warming is past that level.
This is pretty low on the list of opportunities I’d kick myself for missing. A longer reply is here: https://www.facebook.com/yudkowsky/posts/10156147605134228
The vision for Arbital would have provided incentives to write content, but those features were not implemented before the project ran out of time. I did not feel that at any point the versions of Arbital that were in fact implemented were at a state where I predicted they’d attract lots of users, and said so.
I designed a solution from the start, I’m not stupid. It didn’t get implemented in time.
Unless I’m missing something, the trouble with this is that, absent a leverage penalty, all of the reasons you’ve listed for not having a muggable decision algorithm… drumroll… center on the real world, which, absent a leverage penalty, is vastly outweighed by tiny probabilities of googolplexes and ackermann numbers of utilons. If you don’t already consider the Mugger’s claim to be vastly improbable, then all the considerations of “But if I logically decide to let myself be mugged that retrologically increases his probability of lying” or “If I let myself mugged this real-world scenario will be repeated many times” are vastly outweighed by the tiny probability that the Mugger is telling the truth.
Zvi’s probably right.