Hi! I am currently a senior at Yale doing cognitive neuroscience research. I focus on computational modeling of human reinforcement learning and individual differences in decision-making. I am currently a Machine Learning Alignment Theory Scholar ("MATS") working under Evan Hubinger through the Stanford Existential Risks Initiative.
Thanks for your comment! I agree with both of your hesitations and I think I will make the relevant changes to the post: instead of 'totally unenforceable,' I'll say 'seems quite challenging to enforce.' I believe that this is true (and I hope that the broad takeaway from this post is basically the opposite of 'researchers need to stay out of the policy game,' so I'm not too concerned that I'd be incentivizing the wrong behavior).
To your point, 'logistically and politically inconceivable' is probably similarly overblown. I will change it to 'highly logistically and politically fraught.' You're right that the general failure of these policies shouldn't be equated with their inconceivability. (I am fairly confident that, if we were so inclined, we could go download a free copy of any movie or song we could dream of—I wouldn't consider this a case study of policy success—only of policy conceivability!).
Very interesting counterexample! I would suspect it gets increasingly sketchy to characterize 1/8th, 1/16th, etc. 'units of knowledge towards AI' as 'breakthroughs' in the way I define the term in the post.
I take your point that we might get our wires crossed when a given field looks like it's accelerating, but when we zoom in to only look at that field's breakthroughs, we find that they are decelerating. It seems important to watch out for this. Thanks for your comment!
The question is not "How can John be so sure that zooming into something narrower would only add noise?", the question is "How can Cameron be so sure that zooming into something narrower would yield crucial information without which we have no realistic hope of solving the problem?".
I am not 'so sure'—as I said in the previous comment, I have only claim(ed) it is probably necessary to, for instance, know more about AGI than just whether it is a 'generic strong optimizer.' I would only be comfortable making non-probabilistic claims about the necessity of particular questions in hindsight.
I don't think I'm making some silly logical error. If your question is, "Why does Cameron think it is probably necessary to understand X if we want to have any realistic hope of solving the problem?", well, I do not think this is rhetorical! I spend an entire post defending and elucidating each of these questions, and I hope by the end of the sequence, readers would have a very clear understanding of why I think each is probably necessary to think about (or I have failed as a communicator!).
It was never my goal to defend the (probable) necessity of each of the questions in this one post—this is the point of the whole sequence! This post is a glorified introductory paragraph.
I do not think, therefore, that this post serves as anything close to an adequate defense of this framework, and I understand your skepticism if you think this is all I will say about why these questions are important.
However, I don't think your original comment—or any of this thread, for that matter—really addresses any of the important claims put forward in this sequence (which makes sense, given that I haven't even published the whole thing yet!). It also seems like some of your skepticism is being fueled by assumptions about what you predict I will argue as opposed to what I will actually argue (correct me if I'm wrong!).
I hope you can find the time to actually read through the whole thing once it's published before passing your final judgment. Taken as a whole, I think the sequence speaks for itself. If you still think it's fundamentally bullshit after having read it, fair enough :)
Definitely agree that if we silo ourselves into any rigid plan now, it almost certainly won't work. However, I don't think 'end-to-end agenda' = 'rigid plan.' I certainly don't think this sequence advocates anything like a rigid plan. These are the most general questions I could imagine guiding the field, and I've already noted that I think this should be a dynamic draft.
...we do not currently possess a strong enough understanding to create an end-to-end agenda which has any hope at all of working; anything which currently claims to be an end-to-end agenda is probably just ignoring the hard parts of the problem.
What hard parts of the problem do you think this sequence ignores?
(I explicitly claim throughout the sequence that what I propose is not sufficient, so I don't think I can be accused of ignoring this.)
Hate to just copy and paste, but I still really don't see how it could be any other way: if we want to avoid futures in which AGI does bad stuff, then we need to think about avoiding (Q3/Q4) the bad stuff (Q2) that AGI (Q1) might do (and we have to do this all "before the deadline;" Q5). This is basically tautological as far as I can tell. Do you agree or disagree with this if-then statement?
I do think that finding necessary subquestions, or noticing that a given subquestion may not be necessary, is much easier than figuring out an end-to-end agenda.
Agreed. My goal was to enumerate these questions. When I noticed that they followed a fairly natural progression, I decided to frame them hierarchically. And, I suppose to your point, it wasn't necessarily easy to write this all up. I thought it would nonetheless be valuable to do so, so I did!
Thanks for linking the Rocket Alignment Problem—looking forward to giving it a closer read.
If it's possible that we could get to a point where AGI is no longer a serious threat without needing to answer the question, then the question is not necessary.
Agreed, this seems like a good definition for rendering anything as 'necessary.'
Our goal: minimize AGI-induced existential threats (right?).
My claim is that answering these questions is probably necessary for achieving this goal—i.e., P(achieving goal | failing to think about one or more of these questions) ≈ 0. (I say, "I am claiming that a research agenda that neglects these questions would probably not actually be viable for the goal of AGI safety work.")
That is, we would be exceedingly lucky if we achieve AGI safety's goal without thinking about
I really don't see how it could be any other way: if we want to avoid futures in which AGI does bad stuff, we need to think about avoiding (Q3/Q4) the bad stuff (Q2) that AGI (Q1) might do (and we have to do this all "before the deadline;" Q5). I propose a way to do this hierarchically. Do you see wiggle room here where I do not?
FWIW, I also don't really think this is the core claim of the sequence. I would want that to be something more like here is a useful framework for moving from point A (where the field is now) to point B (where the field ultimately wants to end up). I have not seen a highly compelling presentation of this sort of thing before, and I think it is very valuable in solving any hard problem to have a general end-to-end plan (which we probably will want to update as we go along; see Robert's comment).
I think most of the strategies in MIRI's general cluster do not depend on most of these questions.
Would you mind giving a specific example of an end-to-end AGI safety research agenda that you think does not depend on or attempt to address these questions? (I'm also happy to just continue this discussion off of LW, if you'd like.)
Thanks for taking the time to write up your thoughts! I appreciate your skepticism. Needless to say, I don't agree with most of what you've written—I'd be very curious to hear if you think I'm missing something:
[We] don't expect that the alignment problem itself is highly-architecture dependent; it's a fairly generic property of strong optimization. So, "generic strong optimization" looks like roughly the right level of generality at which to understand alignment...Trying to zoom in on something narrower than that would add a bunch of extra constraints which are effectively "noise", for purposes of understanding alignment.
Surely understanding generic strong optimization is necessary for alignment (as I also spend most of Q1 discussing). How can you be so sure, however, that zooming into something narrower would effectively only add noise? You assert this, but this doesn't seem at all obvious to me. I write in Q2: "It is also worth noting immediately that even if particular [alignment problems] are architecture-independent [your point!], it does not necessarily follow that the optimal control proposals for minimizing those risks would also be architecture-independent! For example, just because an SL-based AGI and an RL-based AGI might both hypothetically display tendencies towards instrumental convergence does not mean that the way to best prevent this outcome in the SL AGI would be the same as in the RL AGI."
By analogy, consider the more familiar 'alignment problem' of training dogs (i.e., getting the goals of dogs to align with the goals of their owners). Surely there are 'breed-independent' strategies for doing this, but it is not obvious that these strategies will be sufficient for every breed—e.g., Afghan Hounds are apparently way harder to train, than, say, Golden Retrievers. So in addition to the generic-dog-alignment-regime, Afghan hounds require some additional special training to ensure they're aligned. I don't yet understand why you are confident that different possible AGIs could not follow this same pattern.
On top of that, there's the obvious problem that if we try to solve alignment for a particular architecture, it's quite probable that some other architecture will come along and all our work will be obsolete. (At the current pace of ML progress, this seems to happen roughly every 5 years.)
I think that you think that I mean something far more specific than I actually do when I say "particular architecture," so I don't think this accurately characterizes what I believe. I describe my view in the next post.
[It's] the unknown unknowns that kill us. The move we want is not "brainstorm failure modes and then avoid the things we brainstormed", it's "figure out what we want and then come up with a strategy which systematically achieves it (automatically ruling out huge swaths of failure modes simultaneously)".
I think this is a very interesting point (and I have not read Eliezer's post yet, so I am relying on your summary), but I don't see what the point of AGI safety research is if we take this seriously. If the unknown unknowns will kill us, how are we to avoid them even in theory? If we can articulate some strategy for addressing them, they are not unknown unknowns; they are "increasingly-known unknowns!"
I spent the entire first post of this sequence devoted to "figuring out what we want" (we = AGI safety researchers). It seems like what we want is to avoid AGI-induced existential risks. (I am curious if you think this is wrong?) If so, I claim, here is a "strategy that might systematically achieve this:" we need to understand what we mean when we say AGI (Q1), figure out what risks are likely to emerge from AGI (Q2), mitigate these risks (Q3), and implement these mitigation strategies (Q4).
If by "figure out what we want," you mean "figure out what we want out of an AGI," I definitely agree with this (see Robert's great comment below!). If by "figure out what we want," you mean "figure out what we want out of AGI safety research," well, that is the entire point of this sequence!
I expect implementation to be relatively easy once we have any clue at all what to implement. So even if it's technically necessary to answer at some point, this question might not be very useful to think about ahead of time.
I completely disagree with this. It will definitely depend on the competitiveness of the relevant proposals, the incentives of the people who have control over the AGI, and a bunch of other stuff that I discuss in Q4 (which hasn't even been published yet—I hope you'll read it!).
in practice, when we multiply together probability-of-hail-Mary-actually-working vs probability-that-AI-is-coming-that-soon, I expect that number to basically-never favor the hail Mary.
When you frame it this way, I completely agree. However, there is definitely a continuous space of plausible timelines between "all-the-time-in-the-world" and "hail-Mary," and I think the probabilities of success [P(success|timeline) * P(timeline)] fluctuate non-obviously across this spectrum. Again, I hope you will withhold your final judgment of my claim until you see how I defend it in Q5! (I suppose my biggest regret in posting this sequence is that I didn't just do it all at once.)
Zooming out a level, I think the methodology used to generate these questions is flawed. If you want to identify necessary subquestions, then the main way I know how to do that is to consider a wide variety of approaches, and look for subquestions which are clearly crucial to all of them.
I think this is a bit uncharitable. I have worked with and/or talked to lots of different AGI safety researchers over the past few months, and this framework is the product of my having "consider[ed] a wide variety of approaches, and look for subquestions which are clearly crucial to all of them." Take, for instance, this chart in Q1—I am proposing a single framework for talking about AGI that potentially unifies brain-based vs. prosaic approaches. That seems like a useful and productive thing to be doing at the paradigm-level.
I definitely agree that things like how we define 'control' and 'bad outcomes' might differ between approaches, but I do claim that every approach I have encountered thus far operates using the questions I pose here (e.g., every safety approach cares about AGI architectures, bad outcomes, control, etc. of some sort). To test this claim, I would very much appreciate the presentation of a counterexample if you think you have one!
Thanks again for your comment, and I definitely want to flag that, in spite of disagreeing with it in the ways I've tried to describe above, I really do appreciate your skepticism and engagement with this sequence (I cite your preparadigmatic claim a number of times in it).
As I said to Robert, I hope this sequence is read as something much more like a dynamic draft of a theoretical framework than my Permanent Thoughts on Paradigms for AGI Safety™.
Hey Robert—thanks for your comment!
it seems very clear that we should update that structure to the best of our ability as we make progress in understanding the challenges and potentials of different approaches.
Definitely agree—I hope this sequence is read as something much more like a dynamic draft of a theoretical framework than my Permanent Thoughts on Paradigms for AGI Safety™.
"Aiming at good outcomes while/and avoiding bad outcomes" captures more conceptual territory, while still allowing for the investigation to turn out that avoiding bad outcomes is more difficult and should be prioritised. This extends to the meta-question of whether existential risk can be best adressed by focusing on avoiding bad outcomes, rather than developing a strategy to get to good outcomes (which are often characterised by a better abilitiy to deal with future risks) and avoid bad outcomes on the way there.
I definitely agree with the value of framing AGI outcomes both positively and negatively, as I discuss in the previous post. I am less sure that AGI safety as a field necessarily requires deeply considering the positive potential of AGI (i.e., as long as AGI-induced existential risks are avoided, I think AGI safety researchers can consider their venture successful), but, much to your point, if the best way of actually achieving this outcome is by thinking about AGI more holistically—e.g., instead of explicitly avoiding existential risks, we might ask how to build an AGI that we would want to have around—then I think I would agree. I just think this sort of thing would radically redefine the relevant approaches undertaken in AGI safety research. I by no means want to reject radical redefinitions out of hand (I think this very well could be correct); I just want to say that it is probably not the path of least resistance given where the field currently stands.
(And agreed on the self-control point, as you know. See directionality of control in Q3.)
Thanks for your comment—I entirely agree with this. In fact, most of the content of this sequence represents an effort to spell out these generalizations. (I note later that, e.g., the combinatorics of specifying every control proposal to deal with every conceivable bad outcome from every learning architecture is obviously intractable for a single report; this is a "field-sized" undertaking.)
I don't think this is a violation of the hierarchy, however. It seems coherent to both claim (a) given the field's goal, AGI safety research should follow a general progression toward this goal (e.g., the one this sequence proposes), and (b) there is plenty of productive work that can and should be done outside of this progression (for the reason you specify).
I look forward to hearing if you think the sequence walks this line properly!
Hi Tekhne—this post introduces each of the five questions I will put forward and analyze in this sequence. I will be posting one a day for the next week or so. I think I will answer all of your questions in the coming posts.
I doubt that carving up the space in this—or any—way would be totally uncontroversial (there are lots of value judgments necessary to do such a thing), but I think this concern only serves to demonstrate that this framework is not self-justifying (i.e., there is still lots of clarifying work to be done for each of these questions). I agree with this—that's why there I am devoting a post to each of them!
In order to minimize AGI-induced existential threats, I claim that we need to understand (i.e., anticipate; predict) AGI well enough (Q1) to determine what these threats are (Q2). We then need to figure out ways to mitigate these threats (Q3) and ways to make sure these proposals are actually implemented (Q4). How quickly we need to answer Q1-Q4 will be determined by how soon we expect AGI to be developed (Q5). I appreciate your skepticism, but I would counter that this seems actually like a fairly natural and parsimonious way to get from point A (where we are now) to point B (minimizing AGI-induced existential threats). That's why I claim that an AGI safety research agenda would need to answer these questions correctly in order to be successful.
Ultimately, I can only encourage you to wait for the rest of the sequence to be published before passing a conclusive judgment!
I liked this post a lot, and I think its title claim is true and important.
One thing I wanted to understand a bit better is how you're invoking 'paradigms' in this post wrt AI research vs. alignment research. I think we can be certain that AI research and alignment research are not identical programs but that they will conceptually overlap and constrain each other. So when you're talking about 'principles that carry over,' are you talking about principles in alignment research that will remain useful across various breakthroughs in AI research, or are you thinking about principles within one of these two research programs that will remain useful across various breakthroughs within that research program?
Another thing I wanted to understand better was the following:
Unlike many of the natural sciences (physics, chemistry, biology, etc.) whose explicit goals ostensibly are, as you've said, 'to figure out the constraints which govern the world,' I think that one thing that makes alignment research unique is that its explicit goal is not simply to gain knowledge about reality, but also to prevent a particular future outcome from occurring—namely, AGI-induced X-risks. Surely a necessary component for achieving this goal is 'to figure out the [relevant] constraints which govern the world,' but it seems pretty important to note (if we agree on this field-level goal) that this can't be the only thing that goes into a paradigm for alignment research. That is, alignment research can't only be about modeling reality; it must also include some sort of plan for how to bring about a particular sort of future. And I agree entirely that the best plans of this sort would be those that transcend content-level paradigm shifts. (I daresay that articulating this kind of plan is exactly the sort of thing I try to get at in my Paradigm-building for AGI safety sequence!)