All of Rob Bensinger's Comments + Replies

Does "par-human reasoning" mean at the level of an individual human or at the level of all of humanity combined?

If it's the former, what human should we compare it against? 50th percentile? 99.999th percentile?

I partly answered that here, and I'll edit some of this into the post:

By 'matching smart human performance... across all the scientific work humans do in that field' I don't mean to require that there literally be nothing humans can do that the AI can't match. I do expect this kind of AI to quickly (or immediately) blow humans out of the water, but t

Thanks for posting this, Andrea_Miotti and remember! I noticed a lot of substantive errors in the transcript (and even more errors in vonk's Q&A transcript), so I've posted an edited version of both transcripts. I vote that you edit your own post to include the revisions I made.

Here's a small sample of the edits I made, focusing on ones where someone may have come away from your transcript with a wrong interpretation or important missing information (as opposed to, e.g., the sentences that are just very hard to parse in the original transcript because ... (read more)

How about the distinction between (A) “An AGI kills every human, and the people who turned on the AGI didn’t want that to happen” versus (B) “An AGI kills every human, and the people who turned on the AGI did want that to happen”?

I think the misuse vs. accident dichotomy is clearer when you don't focus exclusively on "AGI kills every human" risks. (E.g., global totalitarianism risks strike me as small but non-negligible if we solve the alignment problem. Larger are risks that fall short of totalitarianism but still involve non-morally-humble developers dam... (read more)

FYI, the timestamp is for the first Discord message. If the log broke out timestamps for every part of the message, it would look like this:

[2:21 PM]

It's about the size of the information bottleneck. The human genome is 3 billion base pairs drawn from 4 possibilities, so 750 megabytes. Let's say 90% of that is junk DNA, and 10% of what's left is neural wiring algorithms. So the code that wires a 100-trillion-synapse human brain is about 7.5 megabytes. Now an adult human contains a lot more information than this. Your spinal cord is about 70 million neurons

I don't know Nate's response, but his take on agent-foundations-ish research in A note about differential technological development (and the fact that he and MIRI have been broadly pro-interpretability-work to date) might help clarify how he thinks about cases like this.

[...]

I feel relatively confident that a large percentage of people who do capabilities work at OpenAI, FAIR, DeepMind, Anthropic, etc. with justifications like "well, I'm helping with alignment some too" or "well, alignment will be easier when we get to the brink" (more often EA-adjacent th

The genre of plans that I'd recommend to groups currently pushing the capabilities frontier is: aim for a pivotal act that's selected for being (to the best of your knowledge) the easiest-to-align action that suffices to end the acute risk period. Per Eliezer on Arbital, the "easiest-to-align" condition probably means that you want the act that requires minimal cognitive abilities, out of the set of acts that suffice to prevent the world from being destroyed:

In the context of AI alignment, the "Principle of Minimality" or "Principle of Least Everything" sa

The definitions given in the post are:

• ASI-boosted humans — We solve all of the problems involved in aiming artificial superintelligence at the things we’d ideally want.

[...]

• misaligned AI — Humans build and deploy superintelligent AI that isn’t aligned with what we’d ideally want.

I'd expect most people to agree that "We solve all of the problems involved in aiming artificial superintelligence at the things we'd ideally want" yields outcomes that are about as good as possible, and I'd expect most of the disagreement to turn (either overtly or in some su... (read more)

Predictions, using the definitions in Nate's post:

My example with the 100 million referred to question 1.

Yeah, I'm also talking about question 1.

I do think that stuff only matters (to me) if it's in some sense causally connected to my life and experiences.

Seems obviously false as a description of my values (and, I'd guess, just about every human's).

Consider the simple example of a universe that consists of two planets: mine, and another person's. We don't have spaceships, so we can't interact. I am not therefore indifferent to whether the other person is being horribly tortured for thousands of years.

If I... (read more)

But, two rooms with trillion people each is virtually the same as one room with two trillion. The returns on interactions with additional people fall off exponentially past the Dunbar number.

You're conflating "would I enjoy interacting with X?" with "is it good for X to exist?". Which is almost understandable given that Nate used the "two people can have more fun in the same room" example to illustrate why utility isn't linear in population. But this comment has an IMO bizarre amount of agreekarma (26 net agreement, with 11 votes), which makes me wonder if... (read more)

(And we aren't perfect recognizers of 'functional, safe-to-use nanofactory' or other known-to-me things that might save the world.)

Also from Ronny: 

There's also an important disanalogy between generating/recognizing faces and learning 'human values', which is that humans are perfect human face recognizers but not perfect recognizers of worlds high in 'human values'.

That means that there might be world states or plans in the training data or generated by adversarial training that look to us, and ML trained to recognize these things the way we recognize them, like they are awesome, but are actually awful.

Ronny Fernandez asked me, Nate, and Eliezer for our take on Twitter. Copying over Nate's reply:

briefly: A) narrow non-optimizers can exist but won't matter; B) wake me when the allegedly maximally-facelike image looks human; C) ribosomes show that cognition-bound superpowers exist; D) humans can't stack into superintelligent corps, but if they could then yes plz value-load

(tbc, I appreciate Katja saying all that. Hooray for stating what you think, and hooray again when it's potentially locally unpopular! If I were less harried I might give more than a twee

e.g. by trying to apply standards of epistemic uncertainty to the state of this essence? 

I would say that there's a logical object that a large chunk of human moral discourse is trying to point at — something like "the rules of the logical game Morality", analogous to "the rules of the logical game Chess". Two people can both be discussing the same logical object "the rules of Chess", but have different beliefs about what that logical object's properties are. And just as someone can be mistaken or uncertain bout the rules of chess — or about their int... (read more)

I also contend that it's the more epistemically humble position, because you're not saying that it's for sure that a pivotal act should be performed, but just that it's quite plausible given the specifics of the current world situation

The thing I'd say in favor of this position is that I think it better fits the evidence. I think the problem with the opposing view is that it's wrong, not that it's more confident. E.g., if I learned that Nate assigns probability .9 to "a pivotal act is necessary" (for some operationalization of "necessary") while Critch ass... (read more)

"The goal should be to cause the future to be great on its own terms"

What the heck is this supposed to mean? Great according to the Inherent Essence Of Goodness that lives inside futures, rather than as part of human evaluations?

The rest of the quote explains what this means:

The goal should be to cause the future to be great on its own terms, without locking in the particular moral opinions of humanity today — and without locking in the moral opinions of any subset of humans, whether that’s a corporation, a government, or a nation.

(If you can't s

The wisest moves we've made as a species to date (ending slavery? ending smallpox? landing on the moon?) didn't particularly look like "worldwide collaborations".

I think Nate might've been thinking of things like:

• Having all AGI research occur in one place is good (ceteris paribus), because then the AGI project can take as much time as it needs to figure out alignment, without worrying that some competitor will destroy the world with AGI if they go too slowly.
• This is even truer if the global coordination is strong enough to prevent other x-risks (e.g., bio-

Seems right to me.

I'd guess Nate might say one of:

• Current SotA systems are very opaque — we more-or-less can't inspect or intervene on their thoughts — and it isn't clear how we could navigate to AI approaches that are far less opaque, and that can carry forward to AGI. (Though it seems very likely such approaches exist somewhere in the space of AI research approaches.)
• Much more generally: we don't have a alignment approach that could realistically work fast (say, within ten months of inventing AGI rather than ten years), in the face of a sharp left turn, given inevitable p

Alex Lintz's take: https://forum.effectivealtruism.org/posts/eggdG27y75ot8dNn7/three-pillars-for-avoiding-agi-catastrophe-technical 

Thanks for the update, Ajeya! I found the details here super interesting.

I already thought that timelines disagreements within EA weren't very cruxy, and this is another small update in that direction: I see you and various MIRI people and Metaculans give very different arguments about how to think about timelines, and then the actual median year I tend to hear is quite similar.

(And also, all of the stated arguments on all sides continue to seem weak/inconclusive to me! So IMO there's not much disagreement, and it would be very easy for all of us to be wro... (read more)

Some added context for this list: Nate and Eliezer expect the first AGI developers to encounter many difficulties in the “something forces you to stop and redesign (and/or recode, and/or retrain) large parts of the system” category, with the result that alignment adds significant development time.

By default, safety-conscious groups won't be able to stabilize the game board before less safety-conscious groups race ahead and destroy the world. To avoid this outcome, humanity needs there to exist an AGI group that…

• is highly safety-conscious.
• has a la

One caveat to the claim that we should prioritize serial alignment work over parallelizable work, is that this assumes an omniscient and optimal allocator of researcher-hours to problems.

Why do you think it assumes that?

This isn't a coincidence; the state of alignment knowledge is currently "we have no idea what would be involved in doing it even in principle, given realistic research paths and constraints", very far from being a well-specified engineering problem. Cf. https://intelligence.org/2013/11/04/from-philosophy-to-math-to-engineering/.

If you succeed at the framework-inventing "how does one even do this?" stage, then you can probably deploy an enormous amount of engineering talent in parallel to help with implementation, small iterative improvements, building-upon-foundations, targeting-established-metrics, etc. tasks.

From A central AI alignment problem: capabilities generalization, and the sharp left turn:

Suppose that the fictional team OpenMind is training up a variety of AI systems, before one of them takes that sharp left turn. Suppose they've put the AI in lots of different video-game and simulated environments, and they've had good luck training it to pursue an objective that the operators described in English. "I don't know what those MIRI folks were talking about; these systems are easy to direct; simple training suffices", they say. At the same time, they apply

(Most of the QR-upvotes at the moment are from me. I think 1-4 are all good questions, for Nate or others; but I'm extra excited about people coming up with ideas for 3.)

Nate's follow-up post is now up: On how various plans miss the hard bits of the alignment challenge.

When I think about the strawberry problem, it seems unnatural, and perhaps misleading of our attention, since there's no guarantee there's even a reasonable solution.

Why would there not be a solution?

On my model, the point of ass numbers isn't to demand perfection of your gut (e.g., of the sort that would be needed to avoid multiple-stage fallacies when trying to conditionalize a lot), but to:

1. Communicate with more precision than English-language words like 'likely' or 'unlikely' allow. Even very vague or uncertain numbers will, at least some of the time, be a better guide than natural-language terms that weren't designed to cover the space of probabilities (and that can vary somewhat in meaning from person to person).
2. At least very vaguely and roughly b

Collecting all of the quantitative AI predictions I know of MIRI leadership making on Arbital (let me know if I missed any):

• Aligning an AGI adds significant development time: Eliezer 95%
• Almost all real-world domains are rich: Eliezer 80%
• Complexity of value: Eliezer 97%, Nate 97%
• Distant superintelligences can coerce the most probable environment of your AI: Eliezer 66%
• Meta-rules for (narrow) value learning are still unsolved: Eliezer 95%
• Natural language understanding of "right" will yield normativity: Eliezer 10%
• Relevant powerful agents will be highly optim

Note that I advocate for considering much more weird solutions, and also thinking much more weird world states when talking with the "general world".  While in contrast, on LW and AF, I'd like to see more discussion of various "boring" solutions on which the world can roughly agree.

Can I get us all to agree to push for including pivotal acts and pivotal processes in the Overton window, then? :) I'm happy to publicly talk about pivotal processes and encourage people to take them seriously as options to evaluate, while flagging that I'm ~2-5% on them be... (read more)

• With pretty high confidence, you expect sharp left turn to happen (in almost all trajectories)
• This is to a large extent based on the belief that at some point "systems start to work really well in domains really far beyond the environments of their training" which is roughly the same as "discovering a core of generality" and few other formulations. These systems will be in some meaningful sense fundamentally different from eg Gato

That's right, though the phrasing "discovering a core of generality" here sounds sort of mystical and mysterious to me, which ma... (read more)

In my view, in practice, the pivotal acts framing actually pushes people to consider a more narrow space of discrete powerful actions, "sharp turns", "events that have a game-changing impact on astronomical stakes". 

My objection to Critch's post wasn't 'you shouldn't talk about pivotal processes, just pivotal acts'. On the contrary, I think bringing in pivotal processes is awesome.

My objection (more so to "Pivotal Act" Intentions, but also to the new one) is specifically to the idea that we should socially shun the concept of "pivotal acts", and socia... (read more)

An example of a possible "pivotal act" I like that isn't "melt all GPUs" is:

Use AGI to build fast-running high-fidelity human whole-brain emulations. Then run thousands of very-fast-thinking copies of your best thinkers. Seems to me this plausibly makes it realistic to keep tabs on the world's AGI progress, and locally intervene before anything dangerous happens, in a more surgical way rather than via mass property destruction of any sort.

Looking for pivotal acts that are less destructive (and, more importantly for humanity's sake, less difficult to align)... (read more)

Some hopefully-unnecessary background info for people attempting this task:

A description of corrigibility Eliezer wrote a few months ago: "'corrigibility' is meant to refer to the sort of putative hypothetical motivational properties that prevent a system from wanting to kill you after you didn't build it exactly right".

An older description of "task-directed AGI" he wrote in 2015-2016: "A task-based AGI is an AGI intended to follow a series of human-originated orders, with these orders each being of limited scope", where the orders can be "accomplished using bounded amounts of effort and resources (as opposed to the goals being more and more fulfillable using more and more effort)."

Ronny Fernandez on Twitter:

I think I don’t like AI safety analogies with human evolution except as illustrations. I don’t think they’re what convinced the people who use those analogies, and they’re not what convinced me. You can convince yourself of the same things just by knowing some stuff about agency.

Corrigibility, human values, and figure-out-while-aiming-for-human-values, are not short description length. I know because I’ve practiced finding the shortest description lengths of things a lot, and they just don’t seem like the right sort of thing.

Also

From an Eliezer comment:

Interventions on the order of burning all GPUs in clusters larger than 4 and preventing any new clusters from being made, including the reaction of existing political entities to that event and the many interest groups who would try to shut you down and build new GPU factories or clusters hidden from the means you'd used to burn them, would in fact really actually save the world for an extended period of time and imply a drastically different gameboard offering new hopes and options. [...]

If Iceland did this, it would plausibly need... (read more)

I kind of like the analogous idea of an alignment target as a repeller cone / dome.

Corrigibility is a repeller. Human values aren't a repeller, but they're a very narrow target to hit.

A lot of models of what can or can't work in AI alignment depends on intuitions about whether to expect "true discontinuities" or just "steep bits".

Note that Nate and Eliezer expect there to be some curves you can draw after-the-fact that shows continuity in AGI progress on particular dimensions. They just don't expect these to be the curves with the most practical impact (and they don't think we can identify the curves with foresight, in 2022, to make strong predictions about AGI timing or rates of progress).

Quoting Nate in 2018:

On my model, the key point

I'm not Eliezer, but my high-level attempt at this:

[...] The things I'd mainly recommend are interventions that:

• Help ourselves think more clearly. (I imagine this including a lot of trying-to-become-more-rational, developing and following relatively open/honest communication norms, and trying to build better mental models of crucial parts of the world.)
• Help relevant parts of humanity (e.g., the field of ML, or academic STEM) think more clearly and understand the situation.
• Help us understand and resolve major disagreements. (Especially current disagreements

I think most worlds that successfully navigate AGI risk have properties like:

• AI results aren't published publicly, going back to more or less the field's origin.
• The research community deliberately steers toward relatively alignable approaches to AI, which includes steering away from approaches that look like 'giant opaque deep nets'.
  • This means that you need to figure out what makes an approach 'alignable' earlier, which suggests much more research on getting de-confused regarding alignable cognition.
    • Many such de-confusions will require a lot of software ex

I understand the first part of your comment as "sure, it's possible for minds to care about reality, but we don't know how to target value formation so that the mind cares about a particular part of reality." Is this a good summary? 

Yes!

I was, first, pointing out that this problem has to be solvable, since the human genome solves it millions of times every day! 

True! Though everyone already agreed (e.g., EY asserted this in the OP) that it's possible in principle. The updatey thing would be if the case of the human genome / brain development sugg... (read more)

Why is the process by which humans come to reliably care about the real world, not a process we could leverage analogously to make AIs care about the real world? 

Maybe I'm not understanding your proposal, but on the face of it this seems like a change of topic. I don't see Eliezer claiming 'there's no way to make the AGI care about the real world vs. caring about (say) internal experiences in its own head'. Maybe he does think that, but mostly I'd guess he doesn't care, because the important thing is whether you can point the AGI at very, very specifi... (read more)

For example, I claim that while AlphaGo could be said to be agent-y, it does not care about atoms. And I think that we could make it fantastically more superhuman at Go, and it would still not care about atoms. Atoms are just not in the domain of its utility function.

In particular, I don't think it has an incentive to break out into the real world to somehow get itself more compute, so that it can think more about its next move. It's just not modeling the real world at all. It's not even trying to rack up a bunch of wins over time. It's just playing the si

Here's my answer: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities?commentId=LowEED2iDkhco3a5d 

We have to actually figure out how to build aligned AGI, and the details are crucial. If you're modeling this as a random blog post aimed at persuading people to care about this cause area, a "voice of AI safety" type task, then sure, the details are less important and it's not so clear that Yet Another Marginal Blog Post Arguing For "Care About AI Stuff" matters much.

But humanity also has to do the task of actually figuring o... (read more)

On Twitter, Eric Rogstad wrote:

"the thing where it keeps being literally him doing this stuff is quite a bad sign"

I'm a bit confused by this part. Some thoughts on why it seems odd for him (or others) to express that sentiment...

1. I parse the original as, "a collection of EY's thoughts on why safe AI is hard". It's EY's thoughts, why would someone else (other than @robbensinger) write a collection of EY's thoughts?

(And if we generalize to asking why no-one else would write about why safe AI is hard, then what about Superintelligence, or the AI stuff in co

The conclusion we should take from the concept of mesa-optimisation isn't "oh no alignment is impossible", that's equivalent to "oh no learning is impossible".

The OP isn't claiming that alignment is impossible.

If we were actually inner aligned to the crude heuristics that evolution installed in us for bootstrapping the entire process, we would be totally disfunctional weirdoes. 

I don't understand the point you're making here.

this can (roughly) be read as a set of 42 statements that need to be true for us to in fact be doomed, and statistically speaking it seems unlikely that all of these statements are true.

I don't think these statements all need to be true in order for p(doom) to be high, and I also don't think they're independent. Indeed, they seem more disjunctive than conjunctive to me; there are many cases where any one of the claims being true increases risk substantially, even if many others are false.

+1

a mistake to leave you as the main "public advocate / person who writes stuff down" person for the cause.

It sort of sounds like you're treating him as the sole "person who writes stuff down", not just the "main" one. Noam Chomsky might have been the "main linguistics guy" in the late 20th century, but people didn't expect him to write more than a trivial fraction of the field's output, either in terms of high-level overviews or in-the-trenches research.

I think EY was pretty clear in the OP that this is not how things go on earths that survive. Even if there aren't many who can write high-level alignment overviews today, more people should make the attempt and try to build skill.