Comment by william_s on The Main Sources of AI Risk? · 2019-03-22T18:55:56.945Z · score: 3 (2 votes) · LW · GW
• AI systems end up controlled by a group of humans representing a small range of human values (ie. an ideological or religious group that imposes values on everyone else). While not caused only by AI design, it is possible that design decisions could impact the likelihood of this scenario (ie. at what point are values loaded into the system/how many people's values are loaded into the system), and is relevant for overall strategy.
Comment by william_s on The Main Sources of AI Risk? · 2019-03-22T18:52:24.938Z · score: 6 (3 votes) · LW · GW
• Failure to learn how to deal with alignment in the many-humans, many-AIs case even if single-human, single-AI alignment is solved (which I think Andrew Critch has talked about). For example, AIs negotiating on behalf of humans take the stance described in https://arxiv.org/abs/1711.00363 of agreeing to split control of the future according to which human's priors are most accurate (on potentially irrelevant issues) if this isn't what humans actually want.
Comment by william_s on Some Thoughts on Metaphilosophy · 2019-03-08T18:58:55.169Z · score: 2 (2 votes) · LW · GW

Maybe one AI philosophy service could look like: would ask you a bunch of other questions that are simpler than the problem of qualia, then show you what those answers imply about the problem of qualia if you use some method of reconciling those answers.

Comment by william_s on Some Thoughts on Metaphilosophy · 2019-03-08T18:53:49.848Z · score: 1 (1 votes) · LW · GW

Re: Philosophy as interminable debate, another way to put the relationship between math and philosophy:

Philosophy as weakly verifiable argumentation

Math is solving problems by looking at the consequences of a small number of axiomatic reasoning steps. For something to be math, we have to be able to ultimately cash out any proof as a series of these reasoning steps. Once something is cashed out in this way, it takes a small constant amount of time to verify any reasoning step, so we can verify given polynomial time.

Philosophy is solving problems where we haven't figured out a set of axiomatic reasoning steps. Any non-axiomatic reasoning step we propose could end up having arguments that we hadn't thought of that would lead us to reject that step. And those arguments themselves might be undermined by other arguments, and so on. Each round of debate lets us add another level of counter-arguments. Philosophers can make progress when they have some good predictor of whether arguments are good or not, but they don't have access to certain knowledge of arguments being good.

Another difference between mathematics and philosophy is that in mathematics we have a well defined set of objects and a well-defined problem we are asking about. Whereas in philosophy we are trying to ask questions about things that exist in the real world and/or we are asking questions that we haven't crisply defined yet.

When we come up with a set of axioms and a description of a problem, we can move that problem from the realm of philosophy to the realm of mathematics. When we come up with some method we trust of verifying arguments (ie. replicating scientific experiments), we can move problems out of philosophy to other sciences.

It could be the case that philosophy grounds out in some reasonable set of axioms which we don't have access to now for computational reasons - in which case it could all end up in the realm of mathematics. It could be the case that, for all practical purposes, we will never reach this state, so it will remain in the "potentially unbounded DEBATE round case". I'm not sure what it would look like if it could never ground out - one model could be that we have a black box function that performs a probabilistic evaluation of argument strength given counter-arguments, and we go through some process to get the consequences of that, but it never looks like "here is a set of axioms".

Comment by william_s on Some Thoughts on Metaphilosophy · 2019-03-08T17:41:23.465Z · score: 4 (2 votes) · LW · GW

I guess it feels like I don't know how we could know that we're in the position that we've "solved" meta-philosophy. It feels like the thing we could do is build a set of better and better models of philosophy and check their results against held-out human reasoning and against each other.

I also don't think we know how to specify a ground truth reasoning process that we could try to protect and run forever which we could be completely confident would come up with the right outcome (where something like HCH is a good candidate but potentially with bugs/subtleties that need to be worked out).

I feel like I have some (not well justified and possibly motivated) optimism that this process yields something good fairly early on. We could gain confidence that we are in this world if we build a bunch of better and better models of meta-philosophy and observe at some point the models continue agreeing with each other as we improve them, and that they agree with various instantiations of protected human reasoning that we run. If we are in this world, the thing we need to do is just spend some time building a variety of these kinds of models and produce an action that looks good to most of them. (Where agreement is not "comes up with the same answer" but more like "comes up with an answer that other models think is okay and not disastrous to accept").

Do you think this would lead to "good outcomes"? Do you think some version of this approach could be satisfactory for solving the problems in Two Neglected Problems in Human-AI Safety?

Do you think there's a different kind of thing that we would need to do to "solve metaphilosophy"? Or do you think that working on "solving metaphilosophy" roughly caches out as "work on coming up with better and better models of philosophy in the model I've described here"?

Comment by william_s on Three AI Safety Related Ideas · 2019-03-08T17:30:25.571Z · score: 4 (2 votes) · LW · GW

A couple ways to implement a hybrid approach with existing AI safety tools:

Logical Induction: Specify some computationally expensive simulation of idealized humans. Run a logical inductor with the deductive process running the simulation and outputting what the humans say after time x in simulation, as well as statements about what non-idealized humans are saying in the real world. The inductor should be able to provide beliefs about what the idealized humans will say in the future informed by information from the non-idealized humans.

HCH/IDA: The HCH-humans demonstrate a reasoning process which aims to predict the output of a set of idealized humans using all available information (which can include running simulations of idealized humans or information from real humans). The way that the HCH tree using information about real humans involves looking carefully at their circumstances and asking things like "how do the real human's circumstances differ from the idealized human" and "is the information from the real human compromised in some way?"

Comment by william_s on Can HCH epistemically dominate Ramanujan? · 2019-02-27T22:58:06.230Z · score: 1 (1 votes) · LW · GW

It seems like for Filtered-HCH, the application in the post you linked to, you might be able to do a weaker version where you label any computation that you can't understand in kN steps as problematic, only accepting things you think you can efficiently understand. (But I don't think Paul is arguing for this weaker version).

Comment by william_s on Reinforcement Learning in the Iterated Amplification Framework · 2019-02-18T21:09:09.756Z · score: 4 (2 votes) · LW · GW
RL is typically about sequential decision-making, and I wasn't sure where the "sequential" part came in).

I guess I've used the term "reinforcement learning" to refer to a broader class of problems including both one-shot bandit problems and sequential decision making problems. In this view The feature that makes RL different from supervised learning is not that we're trying to figure out what how to act in an MDP/POMDP, but instead that we're trying to optimize a function that we can't take the derivative of (in the MDP case, it's because the environment is non-differentiable, and in the approval learning case, it's because the overseer is non-differentiable).

Comment by william_s on Some disjunctive reasons for urgency on AI risk · 2019-02-15T21:59:17.046Z · score: 2 (2 votes) · LW · GW

Re: scenario 3, see The Evitable Conflict, the last story in Isaac Asimov's "I, Robot":

"Stephen, how do we know what the ultimate good of Humanity will entail? We haven't at our disposal the infinite factors that the Machine has at its! Perhaps, to give you a not unfamiliar example, our entire technical civilization has created more unhappiness and misery than it has removed. Perhaps an agrarian or pastoral civilization, with less culture and less people would be better. If so, the Machines must move in that direction, preferably without telling us, since in our ignorant prejudices we only know that what we are used to, is good – and we would then fight change. Or perhaps a complete urbanization, or a completely caste-ridden society, or complete anarchy, is the answer. We don't know. Only the Machines know, and they are going there and taking us with them."
Comment by william_s on HCH is not just Mechanical Turk · 2019-02-13T00:15:16.037Z · score: 4 (2 votes) · LW · GW

Yeah, to some extent. In the Lookup Table case, you need to have a (potentially quite expensive) way of resolving all mistakes. In the Overseer's Manual case, you can also leverage humans to do some kind of more robust reasoning (for example, they can notice a typo in a question and still respond correctly, even if the Lookup Table would fail in this case). Though in low-bandwidth oversight, the space of things that participants could notice and correct is fairly limited.

Though I think this still differs from HRAD in that it seems like the output of HRAD would be a much smaller thing in terms of description length than the Lookup Table, and you can buy extra robustness by adding many more human-reasoned things into the Lookup Table (ie. automatically add versions of all questions with typos that don't change the meaning of a question into the Lookup Table, add 1000 different sanity check questions to flag that things can go wrong).

So I think there are additional ways the system could correct mistaken reasoning relative to what I would think the output of HRAD would look like, but you do need to have processes that you think can correct any way that reasoning goes wrong. So the problem could be less difficult than HRAD, but still tricky to get right.

Comment by william_s on The Argument from Philosophical Difficulty · 2019-02-11T17:47:18.818Z · score: 3 (2 votes) · LW · GW

Thanks, this position makes more sense in light of Beyond Astronomical Waste (I guess I have some concept of "a pretty good future" that is fine with something like a bunch of human-descended beings living a happy lives that misses out on the sort of things mentioned in Beyond Astronomical Waste, and "optimal future" which includes those considerations). I buy this as an argument that "we should put more effort into making philosophy work to make the outcome of AI better, because we risk losing large amounts of value" rather than "our efforts to get a pretty good future are doomed unless we make tons of progress on this" or something like that.

"Thousands of millions" was a typo.

Comment by william_s on Thoughts on reward engineering · 2019-02-10T22:31:38.378Z · score: 4 (2 votes) · LW · GW
What is the motivation for using RL here?

I see the motivation as given practical compute limits, it may be much easier to have the system find an action the overseer approves of instead of imitating the overseer directly. Using RL also allows you to use any advances that are made in RL by the machine learning community to try to remain competitive.

Comment by william_s on Thoughts on reward engineering · 2019-02-10T22:28:42.330Z · score: 4 (2 votes) · LW · GW
Would this still be a problem if we were training the agent with SL instead of RL?

Maybe this could happen with SL if SL does some kind of large search and finds a solution that looks good but is actually bad. The distilled agent would then learn to identify this action and reproduce it, which implies the agent learning some facts about the action to efficiently locate it with much less compute than the large search process. Knowing what the agent knows would allow the overseer to learn those facts, which might help in identifying this action as bad.

Comment by william_s on Reinforcement Learning in the Iterated Amplification Framework · 2019-02-10T22:09:27.681Z · score: 1 (1 votes) · LW · GW
I don't understand why we want to find this X* in the imitation learning case.

Ah, with this example the intent was more like "we can frame what the RL case is doing as finding X* , let's show how we could accomplish the same thing in the imitation learning case (in the limit of unlimited compute)".

The reverse mapping (imitation to RL) just consists of applying reward 1 to M2's demonstrated behaviour (which could be "execute some safe search and return the results), and reward 0 to everything else.

What is pM(X∗)?

is the probability of outputting (where is a stochastic policy)

M2("How good is answer X to Y?")∗∇log(pM(X))

This is the REINFORCE gradient estimator (which tries to increase the log probability of actions that were rated highly)

Comment by william_s on Announcement: AI alignment prize round 4 winners · 2019-02-10T19:06:00.792Z · score: 4 (2 votes) · LW · GW

I guess the question was more from the perspective of: if the cost was zero then it seems like it would worth running, so what part of the cost makes it not worth running (where I would think of cost as probably time to judge or availability of money to fund the contest).

Comment by william_s on The Argument from Philosophical Difficulty · 2019-02-10T19:02:57.074Z · score: 6 (3 votes) · LW · GW

One important dimension to consider is how hard it is to solve philosophical problems well enough to have a pretty good future (which includes avoiding bad futures). It could be the case that this is not so hard, but fully resolving questions so we could produce an optimal future is very hard or impossible. It feels like this argument implicitly relies on assuming that "solve philosophical problems well enough to have a pretty good future" is hard (ie. takes thousands of millions of years in scenario 4) - can you provide further clarification on whether/why you think that is the case?

Comment by william_s on Announcement: AI alignment prize round 4 winners · 2019-02-09T17:43:58.790Z · score: 6 (3 votes) · LW · GW

Slightly disappointed that this isn't continuing (though I didn't submit to the prize, I submitted to Paul Christiano's call for possible problems with his approach which was similarly structured). Was hoping that once I got further into my PhD, I'd have some more things worth writing up, and the recognition/a bit of prize money would provide some extra motivation to get them out the door.

What do you feel like is the limiting resource that keeps continuing this from being useful to continue in it's current form?

Comment by william_s on HCH is not just Mechanical Turk · 2019-02-09T17:10:56.001Z · score: 1 (1 votes) · LW · GW

Yeah, this is a problem that needs to be addressed. It feels like in the Overseers Manual case you can counteract this by giving definitions/examples of how you want questions to be interpreted, and in the Lookup Table case this can be addr by coordination within the team creating the lookup table

## Reinforcement Learning in the Iterated Amplification Framework

2019-02-09T00:56:08.256Z · score: 24 (6 votes)

## HCH is not just Mechanical Turk

2019-02-09T00:46:25.729Z · score: 36 (14 votes)
Comment by william_s on Can there be an indescribable hellworld? · 2019-01-31T20:03:23.882Z · score: 1 (1 votes) · LW · GW

Do you think you'd agree with a claim of this form applied to corrigibility of plans/policies/actions?

That is: If some plan/policy/action is uncorrigible, then A can provide some description of how the action is incorrigible.

Comment by william_s on Why we need a *theory* of human values · 2018-12-29T00:01:46.830Z · score: 3 (2 votes) · LW · GW
The better we can solve the key questions ("what are these 'wiser' versions?", "how is the whole setup designed?", "what questions exactly is it trying to answer?"), the better the wiser ourselves will be at their tasks.

I feel like this statement suggests that we might not be doomed if we make a bunch of progress, but not full progress on these statements. I agree with that assessment, but it felt on reading the post like the post was making the claim "Unless we fully specify a correct theory of human values, we are doomed".

I think that I'd view something like Paul's indirect normativity approach as requiring that we do enough thinking in advance to get some critical set of considerations known by the participating humans, but once that's in place we should be able to go from this core set to get the rest of the considerations. And it seems possible that we can do this without a fully-solved theory of human value (but any theoretical progress in advance we can make on defining human value is quite useful).

Comment by william_s on Three AI Safety Related Ideas · 2018-12-20T21:36:23.176Z · score: 6 (3 votes) · LW · GW

My interpretation of what you're saying here is that the overseer in step #1 can do a lot of things to bake in having the AI interpret "help the user get what they really want" in ways that get the AI to try to eliminate human safety problems for the step #2 user (possibly entirely), but problems might still occur in the short term before the AI is able to think/act to remove those safety problems.

It seems to me that this implies that IDA essentially solves the AI alignment portion of points 1 and 2 in the original post (modulo things happening before the AI is in control).

Comment by william_s on A comment on the IDA-AlphaGoZero metaphor; capabilities versus alignment · 2018-07-19T21:41:18.523Z · score: 1 (1 votes) · LW · GW

Correcting all problems in the subsequent amplification stage would be a nice property to have, but I think IDA can still work even if it corrects errors with multiple A/D steps in between (as long as all catastrophic errors are caught before deployment). For example, I could think of the agent initially using some rules for how to solve math problems where distillation introduces some mistake, but later in the IDA process the agent learns how to rederive those rules and realizes the mistake.

Comment by william_s on A general model of safety-oriented AI development · 2018-06-13T20:21:35.174Z · score: 8 (3 votes) · LW · GW

Shorter name candidates:

Inductively Aligned AI Development

Inductively Aligned AI Assistants

Comment by william_s on A general model of safety-oriented AI development · 2018-06-13T20:20:03.086Z · score: 6 (2 votes) · LW · GW

It's a nice property of this model that it prompts consideration of the interaction between humans and AIs at every step (to highlight things like risks of the humans having access to some set of AI systems for manipulation or moral hazard reasons).

Comment by william_s on Poker example: (not) deducing someone's preferences · 2018-06-13T18:53:25.062Z · score: 4 (1 votes) · LW · GW

In the higher dimensional belief/reward space, do you think that it would be possible to significantly narrow down the space of possibilities (so this argument is saying "be bayesian with respect to reward/beliefs, picking policies that work over a distribution) or are you more pessimistic than that, thinking that the uncertainty would be so great in higher dimensional spaces that it would not be possible to pick a good policy?

Comment by william_s on Amplification Discussion Notes · 2018-06-01T19:04:19.114Z · score: 12 (3 votes) · LW · GW

Open Question: Working with concepts that the human can’t understand

Question: when we need to assemble complex concepts by learning/interacting with the environment, rather than using H's concepts directly, and when those concepts influence reasoning in subtle/abstract ways, how do we retain corrigibility/alignment?

Paul: I don't have any general answer to this, seems like we should probably choose some example cases. I'm probably going to be advocating something like "Search over a bunch of possible concepts and find one that does what you want / has the desired properties."

E.g. for elegant proofs, you want a heuristic that gives successful lines of inquiry higher scores. You can explore a bunch of concepts that do that, evaluate each one according to how well it discriminates good from bad lines of inquiry, and also evaluate other stuff like "What would I infer from learning that a proof is elegant other than that it will work" and make sure that you are OK with that.

Andreas: Suppose you don't have the concepts of "proof" and "inquiry", but learned them (or some more sophisticated analogs) using the sort of procedure you outlined below. I guess I'm trying to see in more detail that you can do a good job at "making sure you're OK with reasoning in ways X" in cases where X is far removed from H's concepts. (Unfortunately, it seems to be difficult to make progress on this by discussing particular examples, since examples are necessarily about concepts we know pretty well.)

This may be related to the more general question of what sorts of instructions you'd give H to ensure that if they follow the instructions, the overall process remains corrigible/aligned.

Comment by william_s on Amplification Discussion Notes · 2018-06-01T19:04:01.100Z · score: 9 (2 votes) · LW · GW

Open Question: Severity of “Honest Mistakes”

In the discussion about creative problem solving,Paul said that he was concerned about problems arising when the solution generator was deliberately searching for a solution with harmful side effects. Other failures could occur where the solution generator finds a solution with harmful side effects without “deliberately searching” for it. The question is how bad these “honest mistakes” would end up being.

Paul: I also want to make the further claim that such failures are much less concerning than what-I'm-calling-alignment failures, which is a possible disagreement we could dig into (I think Wei Dai disagrees or is very unsure).

## Amplification Discussion Notes

2018-06-01T19:03:35.294Z · score: 41 (10 votes)
Comment by william_s on Challenges to Christiano’s capability amplification proposal · 2018-05-26T22:58:13.323Z · score: 10 (2 votes) · LW · GW
I would solve X-and-only-X in two steps:
First, given an agent and an action which has been optimized for undesirable consequence Y, we'd like to be able to tell that the action has this undesirable side effect. I think we can do this by having a smarter agent act as an overseer, and giving the smarter agent suitable insight into the cognition of the weaker agent (e.g. by sharing weights between the weak agent and an explanation-generating agent). This is what I'm calling informed oversight.
Second, given an agent, identify situations in which it is especially likely to produce bad outcomes, or proofs that it won't, or enough understanding of its internals that you can see why it won't. This is discussed in “Techniques for Optimizing Worst-Case Performance.”

Paul, I'm curious whether you'd see as necessary for these techniques to work to have that the optimization target is pretty good/safe (but not perfect): ie some safety comes from the fact that the agents optimized for approval or imitation only have a limited class of Y's that they might also end up being optimized for.

Comment by william_s on Challenges to Christiano’s capability amplification proposal · 2018-05-26T22:54:32.134Z · score: 16 (3 votes) · LW · GW
So I also don't see how Paul expects the putative alignment of the little agents to pass through this mysterious aggregation form of understanding, into alignment of the system that understands Hessian-free optimization.

My model of Paul's approach sees the alignment of the subagents as just telling you that no subagent is trying to actively sabotage your system (ie. by optimizing to find the worst possible answer to give you), and that the alignment comes from having thought carefully about how the subagents are supposed to act in advance (in a way that could potentially be run just by using a lookup table).

Comment by william_s on Resolving human values, completely and adequately · 2018-05-16T18:23:05.370Z · score: 5 (2 votes) · LW · GW

Glad to see this work on possible structure for representing human values which can include disagreement between values and structured biases.

I had some half-formed ideas vaguely related to this, which I think map onto an alternative way to resolve self reference.

Rather than just having one level of values that can refer to other values on the same level (which potentially leads to a self-reference cycle), you could instead explicitly represent each level of value, with level 0 values referring to concrete reward functions, level 1 values endorsing or negatively endorsing level 0 values, and generally level n values only endorsing or negatively endorsing level n-1 values. This might mean that you have some kinds of values that end up being duplicated between multiple levels. For any n, there's a unique solution to the level of endorsement for every concrete value. We can then consider the limit as n->infinity as the true level of endorsement. This allows for situations where the limit fails to converge (ie. it alternates between different values at odd and even levels), which seems like a way to handle self reference contradictions (possibly also the all-or-nothing problem if it results from a conflict between meta-levels).

I think this maps into the case where we don't distinguish between value levels if we define an function that just adjusts the endorsement of each value by the values that directly to refer to it. Then iterating this function n times gives the equivalent of having an n-level meta-hierarchy.

I think there might be interesting work in mapping this strategy into some simple value problem, and then trying to perform bayesian value learning in that setting with some reasonable prior over values/value endorsements.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-24T18:18:04.818Z · score: 4 (1 votes) · LW · GW

Ah, right. I guess I was balking at moving from exorbitant to exp(exorbitant). Maybe it's better to think of this as reducing the size of fully worked initial overseer example problems that can be produced for training/increasing the number of amplification rounds that are needed.

So my argument is more an example of what a distilled overseer could learn as an efficient approximation.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-24T16:42:01.486Z · score: 4 (1 votes) · LW · GW

I guess what we're trying to unpack is "the mechanism that makes decisions from that database", and whether it can be efficient. If you store all experience with no editing, you're left with the same problem of "what decisions in the tree do you change based on experience (X,y) and how do you change them?" And it seems like the most naive way of doing this is too inefficient (looking at all data at all decision nodes).

If you summarize experience (what I'm interpreting "decide how to update some small sketch" as meaning), then the question is can you produce that summary in an efficient way, and efficiently use it at runtime?

The gradient-descent style solution is meant to gesture how this could be done in a way analogous to how we use parametric models rather than non-parametric models in ML for decently sized datasets. In advance, bake in a set of parameters/information that could influence each decision, then do updates by having the overseer look for ways to change the decision parameters. But when we do this in an oversight model, we can make the decisions/parameters interpretable sentences.

Rough example of how I see the overall process going:

1. amplified overseer acts in an environment with a breakable vase, but the overseer does not know that the vase is breakable

2. First time it acts, some overseer node N looks into the database for any information related to the vase, and returns nothing, and proceeds in it decision

3. The vase breaks, and the negative feedback is given to the amplified overseer

4. Amplified overseer runs some process to figure out that it could have made a better decision if the database contained the knowledge that vases are breakable, and so updates the database. (The thing that Wei Dai are trying to unpack is, can this step be run in a reasonable amount of time, given that each node of the overseer has only a limited understanding of the original decision process)

5. Second time it acts, the amplified overseer process is the same, up until the node N that does the database lookup is run. This time, the lookup returns that the vase is breakable, so the overseer runs a different decision path and treats the vase with more care.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-23T23:57:13.728Z · score: 9 (2 votes) · LW · GW
What if the current node is responsible for the error instead of one of the subqueries, how do you figure that out?

I think you'd need to form the decomposition in such a way that you could fix any problem through perturbing something in the
world representation (an extreme version is you have the method for performing every operation contained in the world representation and looked up, so you can adjust it in the future).

When you do backprop, you propagate the error signal through all the nodes, not just through a single path that is "most responsible" for the error, right? If you did this with meta-execution, wouldn't it take an exponential amount of time?

One step of this method, as in backprop, is the same time complexity as the forward pass (running meta-execution forward, which I wouldn't call exponential complexity, as I think the relevant baseline is the number of nodes in the meta-execution forward tree). You only need to process each node once (when the backprop signal for it's output is ready), and need to do a constant amount of work at each node (figure out all the ways to perturb the nodes input).

The catch is that, as with backprop, maybe you need to run multiple steps to get it to actually work.

And what about nodes that are purely symbolic, where there are multiple ways the subnodes (or the current node) could have caused the error, so you couldn't use the right answer for the current node to figure out what the right answer is from each subnode? (Can you in general structure the task tree to avoid this?)

The default backprop answer to this is to shrug and adjust all of the inputs (which is what you get from taking the first order gradient). If this causes problems, then you can fix them in the next gradient step. That seems to work in practice for backprop in continuous models. Discrete models like this it might be a bit more difficult - if you start to try out different combinations to see if they work, that's where you'd get exponential complexity. But we'd get to counter this by potentially having cases where, based on understanding the operation, we could intelligently avoid some branches - I think this could potentially wash out to linear complexity in the number of forward nodes if it all works well.

I wonder if we're on the right track at all, or if Paul has an entirely different idea about this.

So do I :)

Comment by william_s on Can corrigibility be learned safely? · 2018-04-23T18:33:51.958Z · score: 9 (2 votes) · LW · GW

Huh, I hadn't thought of this as trying to be a direct analogue of gradient descent, but now that I think about your comment that seems like an interesting way to approach it.

A human debugging a translation software could look at the return value of some high-level function and ask "is this return value sensible" using their own linguistic intuition, and then if the answer is "no", trace the execution of that function and ask the same question about each of the function it calls. This kind of debugging does not seem available to meta-execution trying to debug itself, so I just don't see any way this kind of learning / error correction could work.

I'm curious now whether you could run a more efficient version of gradient descent if you replace the gradient at each step with an overseer human who can harness some intuition to try to do better than the gradient.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-23T15:55:45.522Z · score: 4 (1 votes) · LW · GW
What if the field of linguistics as a whole is wrong about some concept or technique, and as a result all of the humans are wrong about that? It doesn't seem like using different random seeds would help, and there may not be another approach that can be taken that avoids that concept/technique.

Yeah, I don't think simple randomness would recover from this level of failure (only that it would help with some kinds of errors, where we can sample from a distribution that doesn't make that error sometimes). I don't know if anything could recover from this error in the middle of a computation without reinventing the entire field of linguistics from scratch, which might be too to ask. However, I think it could be possible to recover from this error if you get feedback about the final output being wrong.

But in IDA, H is fixed and there's no obvious way to figure out which parts of a large task decomposition tree was responsible for the badly translated sentence and therefore need to be changed for next time.

I think that the IDA task decomposition tree could be created in such a way that you can reasonably trace back which part was responsible for the misunderstanding/that needs to be changed. The structure you'd need for this is that given a query, you can figure out which of it's children would need to be corrected to get the correct result. So if you have a specific word to correct, you can find the subagent that generated that word, then look at it's inputs, see which input is correct, trace where that came from, etc. This might need to be deliberately engineered into the task decomposition (in the same way that differently written programs accomplishing the same task could be easier or harder to debug).

## Understanding Iterated Distillation and Amplification: Claims and Oversight

2018-04-17T22:36:29.562Z · score: 70 (19 votes)
Comment by william_s on Utility versus Reward function: partial equivalence · 2018-04-16T17:03:02.145Z · score: 4 (1 votes) · LW · GW

Ah, misunderstood that, thanks.

Comment by william_s on Utility versus Reward function: partial equivalence · 2018-04-16T15:08:30.412Z · score: 4 (1 votes) · LW · GW

Say w2a is the world where the agent starts in w2 and w2b is the world that results after the agent moves from w1 to w2.

Without considering the agent's memory part of the world, it seems like the problem is worse: the only way to distinguish between w2a and w2b is the agent's memory of past events, so it seems that leaving the agent's memory over the past out of the utility function requires U(w2a) = U(w2b)

Comment by william_s on Two clarifications about "Strategic Background" · 2018-04-15T17:01:39.498Z · score: 8 (2 votes) · LW · GW

Would you think that the following approach would fit within "in addition to making alignment your top priority and working really hard to over-engineer your system for safety, also build the system to have the bare minimum of capabilities" and possibly work, or would you think that it would be hopelessly doomed?

• Work hard on designing the system to be safe
• But there's some problem left over that you haven't been able to fully solve, and think will manifest at a certain scale (level of intelligence/optimization power/capabilities)
• Run the system, but limit scale to stay well within the range where you expect it to behave well
Comment by william_s on Utility versus Reward function: partial equivalence · 2018-04-13T16:02:55.058Z · score: 3 (1 votes) · LW · GW

I'm trying to wrap my head around the case where there are two worlds, w1 and w2; w2 is better than w1, but moving from w1 to w2 is bad (ie. kill everyone and replacing them with different people who are happier, and we think this is bad).

I think for the equivalence to work in this case, the utility function U also needs to depend on your current state - if it's the same for all states, then the agent would always prefer to move from w1 to w2 and erase it's memory of the past when maximizing the utility function, wheras it would act correctly with the reward function.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-10T15:09:59.476Z · score: 3 (1 votes) · LW · GW
how does IDA recover from an error on H's part?

Error recovery could be supported by having a parent agent running multiple versions of a query in parallel with different approaches (or different random seeds).

And also, how does it improve itself using external feedback

I think this could be implemented as: part of the input for a task is a set of information on background knowledge relevant to the task (ie. model of what the user wants, background information about translating the language). The agent can have a task "Update [background knowledge] after receiving [feedback] after providing [output] for task [input]", which outputs a modified version of [background knowledge], based on the feedback.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-09T17:11:24.636Z · score: 3 (1 votes) · LW · GW
The only way I know how to accomplish this is to have IDA emulate the deep learning translator at a very low level, with H acting as a "human transistor" or maybe a "human neuron", and totally ignore what H knows about translation including the meanings of words.

The human can understand the meaning of the word it sees, the human just can't know the context (the words that it doesn't see), and so can't use their understanding of that context.

The could try to guess possible contexts for the word and leverage their understanding of those contexts ("what are some examples of sentences where the word could be used ambiguously?"), but they aren't allowed to know if any of their guesses actually apply to the text they are currently working on (and so their answer is independent of the actual text they are currently working on).

Comment by william_s on Can corrigibility be learned safely? · 2018-04-07T14:40:23.513Z · score: 3 (1 votes) · LW · GW

Okay, I agree that we're on the same page. Amplify(X,n) is what I had in mind.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-05T20:29:22.676Z · score: 3 (1 votes) · LW · GW

Was thinking of things more in line with Paul's version, not this finding ambiguity definition, where the goal is to avoid doing some kind of malign optimization during search (ie. untrained assistant thinks it's a good idea to use the universal prior, then you show them What does the universal prior actually look like?, and afterwards they know not to do that).

Comment by william_s on Can corrigibility be learned safely? · 2018-04-04T22:04:23.262Z · score: 8 (2 votes) · LW · GW
Can you give an example of natural language instruction (for humans operating on small inputs) that can't be turned into a formal algorithm easily?

Any set of natural language instructions for humans operating on small inputs can be turned into a lookup table by executing the human on all possible inputs (multiple times on each input, if you want to capture a stochastic policy).

The with the following "Consider the sentence [s1] w [s2]", and have the agent launch queries of the form "Consider the sentence [s1] w [s2], where we take w to have meaning m". Now, you could easily produce this behaviour algorithmically if you have a dictionary. But in a world without dictionaries, suitably preparing a human to answer this query takes much less effort than producing a dictionary.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-04T21:52:57.420Z · score: 3 (1 votes) · LW · GW
By "corrigible" here did you mean Paul's definition which doesn't include competence in modeling the user and detecting ambiguities, or what we thought "corrigible" meant (where it does include those things)?

Thinking of "corrigible" as "whatever Paul means when he says corrigible". The idea applies to any notion of corrigibility which allows for multiple actions and does not demand that the action returned be one that is the best possible for the user

Comment by william_s on Can corrigibility be learned safely? · 2018-04-04T19:49:11.861Z · score: 3 (1 votes) · LW · GW

What is the difference between "core after a small number of amplification steps" and "core after a large number of amplification steps" that isn't captured in "larger effective computing power" or "larger set of information about the world", and allows the highly amplified core to solve these problems?

Comment by william_s on Can corrigibility be learned safely? · 2018-04-04T17:45:01.393Z · score: 3 (1 votes) · LW · GW

I'm a little confused about what this statement means. I thought that if you have an overseer that implements some reasoning core, and consider amplify(overseer) with infinite computation time and unlimited ability to query the world (ie. for background information on what humans seem to want, how they behave, etc.), then amplify(overseer) should be able to solve any problem that an agent produced by iterating IDA could solve.

Did you mean to say that

• "already highly competent at these tasks" means that the core should be able to solve these problems without querying the world at all, and this is not likely to be possible?
• you don't expect to find a core such that only one round of amplification of amplify(overseer) can solve practical tasks in any reasonable amount of time/number of queries?
• There is some other way that the agent produced by IDA would be more competent than the original amplified overseer?
Comment by william_s on Can corrigibility be learned safely? · 2018-04-04T15:45:26.875Z · score: 17 (4 votes) · LW · GW
Among people I've had significant online discussions with, your writings on alignment tend to be the hardest to understand and easiest to misunderstand.

Additionally, I think that there are ways to misunderstand the IDA approach that leave out significant parts of the complexity (ie. IDA based off of humans thinking for a day with unrestricted input, without doing the hard work of trying to understand corrigibility and meta-philosophy beforehand), but can seem to be plausible things to talk about in terms of "solving the AI alignment problem" if one hasn't understood the more subtle problems that would occur. It's then easy to miss the problems and feel optimistic about IDA working while underestimating the amount of hard philosophical work that needs to be done, or to incorrectly attack the approach for missing the problems completely.

(I think that these simpler versions of IDA might be worth thinking about as a plausible fallback plan if no other alignment approach is ready in time, but only if they are restricted in terms of accomplishing specific tasks to stabilise the world, restricted in how far the amplification is taking, replaced with something better as soon as possible, etc. I also think that working on simple versions of IDA can help make progress on issues that would be required for fully scalable IDA, ie. the experiments that Ought is running.).

Comment by william_s on Can corrigibility be learned safely? · 2018-04-03T19:10:32.587Z · score: 13 (3 votes) · LW · GW

I would see the benefits of humans vs. algorithms being that giving a human a bunch of natural language instructions would be much easier (but harder to verify) than writing down a formal algorithm. Also, the training could just cover how to avoid taking incorrigible actions, and the Overseer could still use their judgement of how to perform competently within the space of corrigible outputs.

Comment by william_s on Can corrigibility be learned safely? · 2018-04-03T19:08:22.234Z · score: 8 (2 votes) · LW · GW

Trying to understand the boundary lines around incorrigibility, looking again at this example from Universality and Security Amplification

For example, suppose meta-execution asks the subquestion “What does the user want?”, gets a representation of their values, and then asks the subquestion “What behavior is best according to those values?” I’ve then generated incorrigible behavior by accident, after taking innocuous steps.

It sounds like from this that this only counts as incorrigible if the optimization in “What behavior is best according to those values?” is effectively optimizing for something that the user doesn't want, but is not incorrigible if it is optimizing for something that the user doesn't want in a way that the user can easily correct? (so incorrigibilty requires something more than just being malign)

One way to describe this is that the decomposition is incorrigible if the models of the user that are used in “What behavior is best according to those values?” are better than the models used in “What does the user want?” (as this could lead the AI to maximize an approximation V* of the user's values V and realize that if the AI reveals to the user that they are maximizing V*, the user will try to correct what the AI is doing, which will perform worse on V*).

So acceptable situations are where both subqueries get the same user models, the first subquery gets a user better model than the second, or the situation where “What behavior is best according to those values?” is performing some form of mild optimization. Is that roughly correct?

## Improbable Oversight, An Attempt at Informed Oversight

2017-05-24T17:43:53.000Z · score: 2 (2 votes)

## Informed Oversight through Generalizing Explanations

2017-05-24T17:43:39.000Z · score: 1 (1 votes)

## Proposal for an Implementable Toy Model of Informed Oversight

2017-05-24T17:43:13.000Z · score: 1 (1 votes)