Akash

Karma: 4,564

Akash 13 May 2024 10:59 UTC
5 points
0
in reply to: Neel Nanda’s comment on: MATS Winter 2023-24 Retrospective
Thanks, Neel! I responded in greater detail to Ryan’s comment but just wanted to note here that I appreciate yours as well & agree with a lot of it.
My main response to this is something like “Given that MATS selects the mentors and selects the fellows, MATS has a lot of influence over what the fellows are interested in. My guess is that MATS’ current mentor pool & selection process overweights interpretability and underweights governance + technical governance, relative to what I think would be ideal.”

Akash 13 May 2024 10:49 UTC
19 points
7
in reply to: Ryan Kidd’s comment on: MATS Winter 2023-24 Retrospective
Thanks for these explanations– I think they’re reasonable & insightful. A few thoughts:
Most of the scholars in this cohort were working on research agendas for which there are world-leading teams based at scaling labs
I suspect there’s probably some bidirectional causality here. People want to work at scaling labs because they’re interested in the research that scaling labs are doing, and people want to focus on the research the scaling labs are doing because they want to work at scaling labs.
There seems to be an increasing trend in the AI safety community towards the belief that most useful alignment research will occur at scaling labs
I think this is true among a subset of the AI safety community but I don’t think this characterizes the AI safety community as a whole. For example, another (even stronger IMO) trend in the AI safety community has been towards the belief that policy work & technical governance work is more important than many folks previously expected it to be (see EG Paul joining USAISI, MIRI shifting to technical governance, UKAISI being established, and not to mention the general surge in interest among policymakers).
One perspective on this could be “well, MATS is a technical research program, and we’re adding some governance mentors, so shrug.” Another perspective on this could be “well, it seems like perhaps MATS is shifting more slowly than one might’ve imagined, resulting in a culture/ecosystem/mentor cohort/selection process/fellow cohort that disproportionately wants to join scaling labs.”
RE shifting more slowly or having a disproportionate focus, note that the ERA fellowship has prioritized toward governance and technical governance– ²⁄₃ of their fellows will be focused on governance + technical governance projects. I’m not necessarily saying this is what would be best for MATS, but it at least points out that we should be seeing MATS’ focus on incubating “technical researchers that want to work at scaling labs” as something that’s part of its design.
I might be a bit “biased” in that I work in AI policy and my worldview generally suggests that AI policy (as well as technical governance) is extremely neglected. I personally think it’s harder to make the case that giving scaling labs better alignment talent is as neglected– it’s still quite important, but scaling labs are extremely popular & I think their ability to hire (and pay for) top technical talent is much stronger than that of governments.
Anecdotally, scholars seemed generally in favor of careers at an AISI or evals org, but would prefer to continue pursuing their current research agenda
Again, I think my primary response here is something like the research interests of the MATS cohort are a function of the program and its selection process– not an immutable characteristic of the world. The ERA example is a “strong” example of prioritizing people with other interests, but I imagine there are plenty of “weaker” things MATS could be doing to select/prioritize fellows who had an interest in governance & technical governance. (Or put differently, my guess is that there are ways in which the current selection process and mentor pool disproportionately attracts/favors those who are interested in the kinds of topics you mentioned).
If I could wave a magic wand, I would probably have MATS add many more governance & technical governance mentors and shift to something closer to ERA’s breakdown. This would admittedly be a rather big shift for MATS, and perhaps current employees/leaders/funders wouldn’t want to do it. I think it ought to be seriously considered, though, and if I were a MATS exec person or a MATS funder I would probably be pushing for this. Or at least asking some serious questions along the lines of “do we really feel like the most impactful thing a training program could be doing right now is serving as an upskilling program for the scaling labs?” (With all due respect to the importance of getting great people to the scaling labs, acknowledging the importance of technical research at scaling labs, agreeing with some of Neel’s points etc.)

Akash 12 May 2024 1:14 UTC
7 points
0
on: MATS Winter 2023-24 Retrospective
Thank you for explaining the shift from scholar support to research management— I found that quite interesting and I don’t think I would’ve intuitively assumed that the research management frame would be more helpful.

I do wonder if as the summer progresses, the role of the RM should shift from writing the reports for mentors to helping the fellows prepare their own reports for mentors. IMO, fellows getting into the habit of providing these updates & learning how to “manage up” when it comes to mentors seems important. I suspect something in the cluster of “being able to communicate well with mentors//manage your mentor+collaborator relationships” is one of the most important “soft skills” for research success. I suspect a transition from “tell your RM things that they include in their report” to “work with your RM to write your own report” would help instill this skill.

Akash 12 May 2024 1:02 UTC
19 points
5
on: MATS Winter 2023-24 Retrospective
Somewhat striking that the top 3 orgs on the career interest survey are Anthropic, DeepMind, and OpenAI.

I personally suspect that these are not the most impactful places for most MATS scholars to work (relative to say, UKAISI/USAISI, METR, starting new orgs/projects).

Regardless, curious if you have any thoughts on this & if it reflects anything about the culture/epistemics in MATS.

(And to be clear, I think the labs do have alignment teams that care about making progress & I suspect that there are some cases where joining a frontier lab alignment team is the most impactful thing for a scholar.)

Akash 9 May 2024 20:55 UTC
2 points
0
in reply to: Richard_Kennaway’s comment on: Introducing AI Lab Watch
Could consider “frontier AI watch”, “frontier AI company watch”, or “AGI watch.”

Most people in the world (including policymakers) have a much broader conception of AI. AI means machine learning, AI is the thing that 1000s of companies are using and 1000s of academics are developing, etc etc.

Akash 8 May 2024 21:29 UTC
5 points
2
in reply to: RobertM’s comment on: RobertM’s Shortform
I haven’t followed this in great detail, but I do remember hearing from many AI policy people (including people at the UKAISI) that such commitments had been made.
It’s plausible to me that this was an example of “miscommunication” rather than “explicit lying.” I hope someone who has followed this more closely provides details.
But note that I personally think that AGI labs have a responsibility to dispel widely-believed myths. It would shock me if OpenAI/Anthropic/Google DeepMind were not aware that people (including people in government) believed that they had made this commitment. If you know that a bunch of people think you committed to sending them your models, and your response is “well technically we never said that but let’s just leave it ambiguous and then if we defect later we can just say we never committed”, I still think it’s fair for people to be disappointed in the labs.
(I do think this form of disappointment should not be conflated with “you explicitly said X and went back on it”, though.)

Akash 5 May 2024 18:24 UTC
3 points
0
in reply to: Dan H’s comment on: Introducing AI Lab Watch
There should be points for how the organizations act wrt to legislation. In the SB 1047 bill that CAIS co-sponsored, we’ve noticed some AI companies to be much more antagonistic than others. I think is is probably a larger differentiator for an organization’s goodness or badness.
@Dan H are you able to say more about which companies were most/least antagonistic?

Akash 3 May 2024 15:57 UTC
LW: 5 AF: 2
2
AF
in reply to: Buck’s comment on: Buck’s Shortform
It is pretty plausible to me that AI control is quite easy
I think it depends on how you’re defining an “AI control success”. If success is defined as “we have an early transformative system that does not instantly kill us– we are able to get some value out of it”, then I agree that this seems relatively easy under the assumptions you articulated.
If success is defined as “we have an early transformative that does not instantly kill us and we have enough time, caution, and organizational adequacy to use that system in ways that get us out of an acute risk period”, then this seems much harder.
The classic race dynamic threat model seems relevant here: Suppose Lab A implements good control techniques on GPT-8, and then it’s trying very hard to get good alignment techniques out of GPT-8 to align a successor GPT-9. However, Lab B was only ~2 months behind, so Lab A feels like it needs to figure all of this out within 2 months. Lab B– either because it’s less cautious or because it feels like it needs to cut corners to catch up– either doesn’t want to implement the control techniques or it’s fine implementing the control techniques but it plans to be less cautious around when we’re ready to scale up to GPT-9.
I think it’s fine to say “the control agenda is valuable even if it doesn’t solve the whole problem, and yes other things will be needed to address race dynamics otherwise you will only be able to control GPT-8 for a small window of time before you are forced to scale up prematurely or hope that your competitor doesn’t cause a catastrophe.” But this has a different vibe than “AI control is quite easy”, even if that statement is technically correct.
(Also, please do point out if there’s some way in which the control agenda “solves” or circumvents this threat model– apologies if you or Ryan has written/spoken about it somewhere that I missed.)

Akash 2 May 2024 20:15 UTC
2 points
0
in reply to: Zach Stein-Perlman’s comment on: Questions for labs
Right now, I think one of the most credible ways for a lab to show its committment to safety is through its engagement with governments.

I didn’t mean to imply that a lab should automatically be considered “bad” if its public advocacy and its private advocacy differ.

However, when assessing how “responsible” various actors are, I think investigating questions relating to their public comms, engagement with government, policy proposals, lobbying efforts, etc would be valuable.

If Lab A had slightly better internal governance but lab B had better effects on “government governance”, I would say that lab B is more “responsible” on net.

Akash 2 May 2024 16:36 UTC
11 points
0
on: Questions for labs
@Zach Stein-Perlman, great work on this. I would be interested in you brainstorming some questions that have to do with the lab’s stances toward (government) AI policy interventions.
After a quick 5 min brainstorm, here are some examples of things that seem relevant:
- I remember hearing that OpenAI lobbied against the EU AI Act– what’s up with that?
- I heard a rumor that Congresspeople and their teams reached out to Sam/OpenAI after his testimony. They allegedly asked for OpenAI’s help to craft legislation around licensing, and then OpenAI refused. Is that true?
- Sam said we might need an IAEA for AI at some point– what did he mean by this? At what point would he see that as valuable?
- In general, what do labs think the US government should be doing? What proposals would they actively support or even help bring about? (Flagging ofc that there are concerns about actual and perceived regulatory capture, but there are also major advantages to having industry players support & contribute to meaningful regulation).
- Senator Cory Booker recently asked Jack Clark something along the lines of “what is your top policy priority right now//what would you do if you were a Senator.” Jack responded with something along the lines of “I would make sure the government can deploy AI successfully. We need a testing regime to better understand risks, but the main risk is that we don’t use AI enough, and we need to make sure we stay at the cutting edge.” What’s up with that?
- Why haven’t Dario and Jack made public statements about specific government interventions? Do they believe that there are some circumstances under which a moratorium would need to be implemented, labs would need to be nationalized (or internationalized), or something else would need to occur to curb race dynamics? (This could be asked to any of the lab CEOs/policy team leads– I don’t mean to be picking on Anthropic, though I think Sam/OpenAI have had more public statements here, and I think the other labs are scoring more poorly across the board//don’t fully buy into the risks in the first place.)
- Big tech is spending a lot of money on AI lobbying. How much is each lab spending (this is something you can estimate with publicly available data), and what are they actually lobbying for/against?
I imagine there’s a lot more in this general category of “labs and how they are interacting with governments and how they are contributing to broader AI policy efforts”, and I’d be excited to see AI Lab Watch (or just you) dive into this more.

Akash 2 May 2024 0:20 UTC
28 points
32
in reply to: Buck’s comment on: Introducing AI Lab Watch
I can imagine this growing into the default reference that people use when talking about whether labs are behaving responsibly.
I hope that this resource is used as a measure of relative responsibleness, and this doesn’t get mixed up with absolute responsibleness. My understanding is that the resource essentially says, “here’s some things that would be good– let’s see how the labs compare on each dimension.” The resource is not saying “if a lab gets a score above X% on each metric, then we are quite confident that the lab will not cause an existential catastrophe.”
Moreover, my understanding is that the resource is not taking a position on whether or not it is “responsible”– in some absolute sense– for a lab to be scaling toward AGI in our current world. I see the resource as saying “conditional on a lab scaling toward AGI, are they doing so in a way that is relatively more/less responsible compared to the others that are scaling toward AGI.”
This might be a pedantic point, but I think it’s an important one to emphasize– a lab can score in 1st place and still present a risk to humanity that reasonable people would still deem unacceptable & irresponsible (or put differently, a lab can score in 1st place and still produce a catastrophe).

Akash 1 May 2024 15:15 UTC
11 points
5
in reply to: tlevin’s comment on: tlevin’s Shortform
Agree with lots of this– a few misc thoughts [hastily written]:
1. I think the Overton Window frame ends up getting people to focus too much on the dimension “how radical is my ask”– in practice, things are usually much more complicated than this. In my opinion, a preferable frame is something like “who is my target audience and what might they find helpful.” If you’re talking to someone who makes it clear that they will not support X, it’s silly to keep on talking about X. But I think the “target audience first” approach ends up helping people reason in a more sophisticated way about what kinds of ideas are worth bringing up. As an example, in my experience so far, many policymakers are curious to learn more about intelligence explosion scenarios and misalignment scenarios (the more “radical” and “speculative” threat models).
2. I don’t think it’s clear that the more effective actors in DC tend to be those who look for small wins. Outside of the AIS community, there sure do seem to be a lot of successful organizations that take hard-line positions and (presumably) get a lot of their power/influence from the ideological purity that they possess & communicate. Whether or not these organizations end up having more or less influence than the more “centrist” groups is, in my view, not a settled question & probably varies a lot by domain. In AI safety in particular, I think my main claim is something like “pretty much no group– whether radical or centrist– has had tangible wins. When I look at the small set of tangible wins, it seems like the groups involved were across the spectrum of “reasonableness.”
3. The more I interact with policymakers, the more I’m updating toward something like “poisoning the well doesn’t come from having radical beliefs– poisoning the well comes from lamer things like being dumb or uninformed, wasting peoples’ time, not understanding how the political process works, not having tangible things you want someone to do, explaining ideas poorly, being rude or disrespectful, etc.” I’ve asked ~20-40 policymakers (outside of the AIS bubble) things like “what sorts of things annoy you about meetings” or “what tends to make meetings feel like a waste of your time”, and no one ever says “people come in with ideas that are too radical.” The closest thing I’ve heard is people saying that they dislike it when groups fail to understand why things aren’t able to happen (like, someone comes in thinking their idea is great, but then they fail to understand that their idea needs approval from committee A and appropriations person B and then they’re upset about why things are moving slowly). It seems to me like many policy folks (especially staffers and exec branch subject experts) are genuinely interested in learning more about the beliefs and worldviews that have been prematurely labeled as “radical” or “unreasonable” (or perhaps such labels were appropriate before chatGPT but no longer are).
4. A reminder that those who are opposed to regulation have strong incentives to make it seem like basically-any-regulation is radical/unreasonable. An extremely common tactic is for industry and its allies to make common-sense regulation seem radical/crazy/authoritarian & argue that actually the people proposing strong policies are just making everyone look bad & argue that actually we should all rally behind [insert thing that isn’t a real policy.] (I admit this argument is a bit general, and indeed I’ve made it before, so I won’t harp on it here. Also I don’t think this is what Trevor is doing– it is indeed possible to raise serious discussions about “poisoning the well” even if one believes that the cultural and economic incentives disproportionately elevate such points).
5. In the context of AI safety, it seems to me like the most high-influence Overton Window moves have been positive– and in fact I would go as far as to say strongly positive. Examples that come to mind include the CAIS statement, FLI pause letter, Hinton leaving Google, Bengio’s writings/speeches about rogue AI & loss of control, Ian Hogarth’s piece about the race to god-like AI, and even Yudkowsky’s TIME article.
6. I think some of our judgments here depend on underlying threat models and an underlying sense of optimism vs. pessimism. If one things that labs making voluntary agreements/promises and NIST contributing to the development of voluntary standards are quite excellent ways to reduce AI risk, then the groups that have helped make this happen deserve a lot of credit. If one thinks that much more is needed to meaningfully reduce xrisk, then the groups that are raising awareness about the nature of the problem, making high-quality arguments about threat models, and advocating for stronger policies deserve a lot of credit.
I agree that more research on this could be useful. But I think it would be most valuable to focus less on “is X in the Overton Window” and more on “is X written/explained well and does it seem to have clear implications for the target stakeholders?”

Akash 25 Apr 2024 0:58 UTC
12 points
8
in reply to: Richard_Ngo’s comment on: AI Regulation is Unsafe
I’m not sure who you’ve spoken to, but at least among the AI policy people who I talk to regularly (which admittedly is a subset of people who I think are doing the most thoughtful/serious work), I think nearly all of them have thought about ways in which regulation + regulatory capture could be net negative. At least to the point of being able to name the relatively “easy” ways (e.g., governments being worse at alignment than companies).
I continue to think people should be forming alliances with those who share similar policy objectives, rather than simply those who belong in the “I believe xrisk is a big deal” camp. I’ve seen many instances in which the “everyone who believes xrisk is a big deal belongs to the same camp” mentality has been used to dissuade people from communicating their beliefs, communicating with policymakers, brainstorming ideas that involve coordination with other groups in the world, disagreeing with the mainline views held by a few AIS leaders, etc.
The cultural pressures against policy advocacy have been so strong that it’s not surprising to see folks say things like “perhaps our groups are no longer natural allies” now that some of the xrisk-concerned people are beginning to say things like “perhaps the government should have more of a say in how AGI development goes than in status quo, where the government has played ~0 role and ~all decisions have been made by private companies.”
Perhaps there’s a multiverse out there in which the AGI community ended up attracting govt natsec folks instead of Bay Area libertarians, and the cultural pressures are flipped. Perhaps in that world, the default cultural incentives pushed people heavily brainstorming ways that markets and companies could contribute meaningfully to the AGI discourse, and the default position for the “AI risk is a big deal” camp was “well obviously the government should be able to decide what happens and it would be ridiculous to get companies involved– don’t be unilateralist by going and telling VCs about this stuff.”
I bring up this (admittedly kinda weird) hypothetical to point out just how skewed the status quo is. One might generally be wary of government overinvolvement in regulating emerging technologies yet still recognize that some degree of regulation is useful, and that position would likely still push them to be in the “we need more regulation than we currently have” camp.
As a final note, I’ll point out to readers less familiar with the AI policy world that serious people are proposing lots of regulation that is in between “status quo with virtually no regulation” and “full-on pause.” Some of my personal favorite examples include: emergency preparedness (akin to the OPPR), licensing (see Romney), reporting requirements, mandatory technical standards enforced via regulators, and public-private partnerships.

Akash 24 Apr 2024 20:14 UTC
3 points
0
on: Akash’s Shortform
I’m interested in writing out somewhat detailed intelligence explosion scenarios. The goal would be to investigate what kinds of tools the US government would have to detect and intervene in the early stages of an intelligence explosion.
If you know anyone who has thought about these kinds of questions, whether from the AI community or from the US government perspective, please feel free to reach out via LessWrong.

Akash 19 Apr 2024 0:46 UTC
8 points
−3
on: Express interest in an “FHI of the West”
To what extent would the organization be factoring in transformative AI timelines? It seems to me like the kinds of questions one would prioritize in a “normal period” look very different than the kinds of questions that one would prioritize if they place non-trivial probability on “AI may kill everyone in <10 years” or “AI may become better than humans on nearly all cognitive tasks in <10 years.”
I ask partly because I personally would be more excited of a version of this that wasn’t ignoring AGI timelines, but I think a version of this that’s not ignoring AGI timelines would probably be quite different from the intellectual spirit/tradition of FHI.
More generally, perhaps it would be good for you to describe some ways in which you expect this to be different than FHI. I think the calling it the FHI of the West, the explicit statement that it would have the intellectual tradition of FHI, and the announcement right when FHI dissolves might make it seem like “I want to copy FHI” as opposed to “OK obviously I don’t want to copy it entirely I just want to draw on some of its excellent intellectual/cultural components.” If your vision is the latter, I’d find it helpful to see a list of things that you expect to be similar/different.)

Akash 19 Apr 2024 0:25 UTC
64 points
36
in reply to: peterbarnett’s comment on: peterbarnett’s Shortform
I would strongly suggest considering hires who would be based in DC (or who would hop between DC and Berkeley). In my experience, being in DC (or being familiar with DC & having a network in DC) is extremely valuable for being able to shape policy discussions, know what kinds of research questions matter, know what kinds of things policymakers are paying attention to, etc.
I would go as far as to say something like “in 6 months, if MIRI’s technical governance team has not achieved very much, one of my top 3 reasons for why MIRI failed would be that they did not engage enough with DC people//US policy people. As a result, they focused too much on questions that Bay Area people are interested in and too little on questions that Congressional offices and executive branch agencies are interested in. And relatedly, they didn’t get enough feedback from DC people. And relatedly, even the good ideas they had didn’t get communicated frequently enough or fast enough to relevant policymakers. And relatedly… etc etc.”
I do understand this trades off against everyone being in the same place, which is a significant factor, but I think the cost is worth it.

Akash 19 Apr 2024 0:18 UTC
8 points
2
in reply to: Alexander Gietelink Oldenziel’s comment on: Akash’s Shortform
I do think evaporative cooling is a concern, especially if everyone (or a very significant amount) of people left. But I think on the margin more people should be leaving to work in govt.
I also suspect that a lot of systemic incentives will keep a greater-than-optimal proportion of safety-conscious people at labs as opposed to governments (labs pay more, labs are faster and have less bureaucracy, lab people are much more informed about AI, labs are more “cool/fun/fast-paced”, lots of govt jobs force you to move locations, etc.)
I also think it depends on the specific lab– EG in light of the recent OpenAI departures, I suspect there’s a stronger case for staying at OpenAI right now than for DeepMind or Anthropic.

Akash 18 Apr 2024 15:48 UTC
15 points
1
on: AI #60: Oh the Humanity
Daniel Kokotajlo has quit OpenAI
I think now is a good time for people at labs to seriously consider quitting & getting involved in government/policy efforts.
I don’t think everyone should leave labs (obviously). But I would probably hit a button that does something like “everyone at a lab governance team and many technical researchers spend at least 2 hours thinking/writing about alternative options they have & very seriously consider leaving.”
My impression is that lab governance is much less tractable (lab folks have already thought a lot more about AGI) and less promising (competitive pressures are dominating) than government-focused work.
I think governments still remain unsure about what to do, and there’s a lot of potential for folks like Daniel K to have a meaningful role in shaping policy, helping natsec folks understand specific threat models, and raising awareness about the specific kinds of things governments need to do in order to mitigate risks.
There may be specific opportunities at labs that are very high-impact, but I think if someone at a lab is “not really sure if what they’re doing is making a big difference”, I would probably hit a button that allocates them toward government work or government-focused comms work.

Akash 18 Apr 2024 15:44 UTC
43 points
17
on: Akash’s Shortform
I think now is a good time for people at labs to seriously consider quitting & getting involved in government/policy efforts.
I don’t think everyone should leave labs (obviously). But I would probably hit a button that does something like “everyone at a lab governance team and many technical researchers spend at least 2 hours thinking/writing about alternative options they have & very seriously consider leaving.”
My impression is that lab governance is much less tractable (lab folks have already thought a lot more about AGI) and less promising (competitive pressures are dominating) than government-focused work.
I think governments still remain unsure about what to do, and there’s a lot of potential for folks like Daniel K to have a meaningful role in shaping policy, helping natsec folks understand specific threat models, and raising awareness about the specific kinds of things governments need to do in order to mitigate risks.
There may be specific opportunities at labs that are very high-impact, but I think if someone at a lab is “not really sure if what they’re doing is making a big difference”, I would probably hit a button that allocates them toward government work or government-focused comms work.
Written on a Slack channel in response to discussions about some folks leaving OpenAI.

Akash’s Shortform

Akash18 Apr 2024 15:44 UTC

7 points

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