Akash's Shortform

Sorry for brevity, I'm busy right now.

1. Noticing good stuff labs do, not just criticizing them, is often helpful. I wish you thought of this work more as "evaluation" than "criticism."
2. It's often important for evaluation to be quite truth-tracking. Criticism isn't obviously good by default.

Edit:

3. I'm pretty sure OP likes good criticism of the labs; no comment on how OP is perceived. And I think I don't understand your "good judgment" point. Feedback I've gotten on AI Lab Watch from senior AI safety people has been overwhelmingly positive, and of course there's a selection effect in what I hear, but I'm quite sure most of them support such efforts.

4. Conjecture (not exclusively) has done things that frustrated me, including in dimensions like being "'unilateralist,' 'not serious,' and 'untrustworthy.'" I think most criticism of Conjecture-related advocacy is legitimate and not just because people are opposed to criticizing labs.

5. I do agree on "soft power" and some of "jobs." People often don't criticize the labs publicly because they're worried about negative effects on them, their org, or people associated with them.

I'd be worried about evaporative cooling. It seems that the net result of this would be that labs would be almost completely devoid of people earnest about safety.

I agree with you government pathways to impact are most plausible and until recently undervalued. I also agree with you there are weird competitive pressures at labs. 

I largely agree, but think given government hiring timelines, there's no dishonor in staying at a lab doing moderately risk-reducing work until you get a hiring offer with an actual start date.  This problem is less bad for the special hiring authorities being used for AI stuff oftentimes, but it's still not ideal.

My own impression is that this would be an improvement over the status quo. Main reasons:

• A lot of my P(doom) comes from race dynamics.
• Right now, if a leading lab ends up realizing that misalignment risks are super concerning, they can't do much to end the race. Their main strategy would be to go to the USG.
• If the USG runs the Manhattan Project (or there's some sort of soft nationalization in which the government ends up having a much stronger role), it's much easier for the USG to see that misalignment risks are concerning & to do something about it.
• A national project would be more able to slow down and pursue various kinds of international agreements (the national project has more access to POTUS, DoD, NSC, Congress, etc.)
• I expect the USG to be stricter on various security standards. It seems more likely to me that the USG would EG demand a lot of security requirements to prevent model weights or algorithmic insights from leaking to China. One of my major concerns is that people will want to pause at GPT-X but they won't feel able to because China stole access to GPT-Xminus1 (or maybe even a slightly weaker version of GPT-X).
• In general, I feel like USG natsec folks are less "move fast and break things" than folks in SF. While I do think some of the AGI companies have tried to be less "move fast and break things" than the average company, I think corporate race dynamics & the general cultural forces have been the dominant factors and undermined a lot of attempts at meaningful corporate governance.

(Caveat that even though I see this as a likely improvement over status quo, this doesn't mean I think this is the best thing to be advocating for.)

(Second caveat that I haven't thought about this particular question very much and I could definitely be wrong & see a lot of reasonable counterarguments.)

If the project was fueled by a desire to beat China, the structure of the Manhattan project seems unlikely to resemble the parts of the structure of the Manhattan project that seemed maybe advantageous here, like having a single government-controlled centralized R&D effort.

My guess is if something like this actually happens, it would involve a large number of industry subsidies, and would create strong institutional momentum that even when things got dangerous, to push the state of the art forward, and in as much as there is pushback, continue dangerous development in secret. 

In the case of nuclear weapons the U.S. really went very far under the advisement of Edward Teller, so I think the outside view here really doesn't look good: 

Worse than the current situation, because the counterfactual is that some later project happens which kicks off in a less race-y manner.

In other words, whatever the chance of its motivation shifting over time, it seems dominated by the chance that starting the equivalent project later would just have better motivations from the outset.

Some thoughts:

• The correct answer is clearly (c) - it depends on a bunch of factors.
• My current guess is that it would make things worse (given likely values for the bunch of other factors) - basically for Richard's reasons.
  • Given [new potential-to-shift-motivation information/understanding], I expect there's a much higher chance that this substantially changes the direction of a not-yet-formed project, than a project already in motion.
  • Specifically:
    • Who gets picked to run such a project? If it's primarily a [let's beat China!] project, are the key people cautious and highly adaptable when it comes to top-level goals? Do they appoint deputies who're cautious and highly adaptable?
      • Here I note that the kind of 'caution' we'd need is [people who push effectively for the system to operate with caution]. Most people who want caution are more cautious.
    • How is the project structured? Will the structure be optimized for adaptability? For red-teaming of top-level goals?
      • Suppose that a mid-to-high-level participant receives information making the current top-level goals questionable - is the setup likely to reward them for pushing for changes? (noting that these are the kind of changes that were not expected to be needed when the project launched)
    • Which external advisors do leaders of the project develop relationships with? What would trigger these to change?
    • ...
• I do think that it makes sense to aim for some centralized project - but only if it's the right kind.
  • I expect that almost all the directional influence is in [influence the initial conditions].
  • For this reason, I expect [push for some kind of centralized project, and hope it changes later] is a bad idea.
  • I think [devote great effort to influencing the likely initial direction of any such future project] seems a great idea (so long as you're sufficiently enlightened about desirable initial directions, of course :))
  • I'd note that [initial conditions] needn't only be internal to the project - in principle we could have reason to believe that various external mechanisms would be likely to shift the project's motivation sufficiently over time. (I don't know of any such reasons)
• I think the question becomes significantly harder once the primary motivation behind a project isn't [let's beat China!], but also isn't [your ideal project motivation (with your ideal initial conditions)].
• I note that my p(doom) doesn't change much if we eliminate racing but don't slow down until it's clear to most decision makers that it's necessary.
  • Likewise, I don't expect that [focus on avoiding the earliest disasters] is likely to be the best strategy. So e.g. getting into a good position on security seems great, all else equal - but I wouldn't sacrifice much in terms of [odds of getting to a sufficiently cautious overall strategy] to achieve better short-term security outcomes.

Depends on the direction/magnitude of the shift!

I'm currently feeling very uncertain about the relative costs and benefits of centralization in general. I used to be more into the idea of a national project that centralized domestic projects and thus reduced domestic racing dynamics (and arguably better aligned incentives), but now I'm nervous about the secrecy that would likely entail, and think it's less clear that a non-centralized situation inevitably leads to a decisive strategic advantage for the leading project. Which is to say, even under pretty optimistic assumptions about how much such a project invests in alignment, security, and benefit-sharing, I'm pretty uncertain that this would be good, and with more realistic assumptions I probably lean towards it being bad. But it super depends on the governance, the wider context, how a "Manhattan Project" would affect domestic companies and China's policymaking, etc.

(I think a great start would be not naming it after the Manhattan Project, though. It seems path dependent, and that's not a great first step.)

One factor is different incentives for decision-makers. The incentives (and the mindset) for tech companies is to move fast and break things. The incentives (and mindset) for government workers is usually vastly more conservative.

So if it is the government making decisions about when to test and deploy new systems, I think we're probably far better off WRT caution.

That must be weighed against the government typically being very bad at technical matters. So even an attempt to be cautious could be thwarted by lack of technical understanding of risks.

Of course, the Trump administration is attempting to instill a vastly different mindset, more like tech companies. So if it's that administration we're talking about, we're probably worse off on net with a combination of lack of knowledge and YOLO attitudes. Which is unfortunate - because this is likely to happen anyway.

As Habryka and others have noted, it also depends on whether it reduces race dynamics by aggregating efforts across companies, or mostly just throws funding fuel on the race fire.

I think this is a (c) leaning (b), especially given that we're doing it in public.  Remember, the Manhattan Project was a highly-classified effort and we know it by an innocuous name given to it to avoid attention.  

Saying publicly, "yo, China, we view this as an all-costs priority, hbu" is a great way to trigger a race with China...

But if it turned out that we knew from ironclad intel with perfect sourcing that China was already racing (I don't expect this to be the case), then I would lean back more towards (c).  

@davekasten [LW · GW] @Zvi [LW · GW] @habryka [LW · GW] @Rob Bensinger [LW · GW] @ryan_greenblatt [LW · GW] @Buck [LW · GW] @tlevin [LW · GW] @Richard_Ngo [LW · GW] @Daniel Kokotajlo [LW · GW] I suspect you might have interesting thoughts on this. (Feel free to ignore though.)

One thing I’d be bearish on is visibility into the latest methods being used for frontier AI methods, which would downstream reduce the relevance of alignment research except for the research within the manhattan-like project itself. This is already somewhat true of the big labs eg. methods used for o1 like models. However, there is still some visibility in the form of system cards and reports which hint at the methods. When the primary intention is racing ahead of China, I doubt there will be reports discussing methods used for frontier systems.

Something I'm worried about now is some RFK Jr/Dr. Oz equivalent being picked to lead on AI...

I think I agree with much-to-all of this.  One further amplification I'd make about the last point: the culture of DC policymaking is one where people are expected to be quick studies and it's OK to be new to a topic; talent is much more funged from topic to topic in response to changing priorities than you'd expect.  Your Lesswrong-informed outside view of how much you need to know on a topic to start commenting on policy ideas is probably wrong.

(Yes, I know, someone is about to say "but what if you are WRONG about the big idea given weird corner case X or second-order effects Y?"   Look, reversed stupidity is not wisdom, but also also sometimes you can just quickly identify stupid-across-almost-all-possible-worlds ideas and convince people just not to do them rather than having to advocate for an explicit good-idea alternative.)

I think how delicately you treat your personal Overton Window should also depend on your timelines. 

I am impressed regarding Helen Toner's China comment! 

For a while I have been tracking a hypothesis that nobody working in DC in AI Policy would openly and prominently speak against competition with China being a current priority, but this quote shows that hypothesis does not hold.

Now I will track whether any such person explicitly states that it doesn't matter who gets there first, civilization will most likely end regardless, and that competition shouldn't be a priority even if China were ahead of the US. I haven't seen a prominent instance of this happening yet.

One reason I feel interested in liability is because it opens up a way to do legal investigations. The legal system has a huge number of privileges that you get to use if you have reasonable suspicion someone has committed a crime or is being negligent. I think it's quite likely that if there was no direct liability, that even if Microsoft or OpenAI causes some huge catastrophe, that we would never get a proper postmortem or analysis of the facts, and would never reach high-confidence on the actual root-causes.

So while I agree that OpenAI and Microsoft want to of course already avoid being seen as responsible for a large catastrophe, having legal liability makes it much more likely there will be an actual investigation where e.g. the legal system gets to confiscate servers and messages to analyze what happens, which makes it then more likely that if OpenAI and Microsoft are responsible, they will be found out to be responsible.

I think liability-based interventions are substantially more popular with Republicans than other regulatory interventions - they're substantially more hands-off than, for instance, a regulatory agency.  They also feature prominently in the Josh Hawley proposal. I've also been told by a republican staffer that liability approaches are relatively popular amongst Rs. 

An important baseline point is that AI firms (if they're selling to consumers) are probably by default covered by product liability by default. If they're covered by product liability, then they'll be liable for damages if it can be shown that there was a not excessively costly alternative design that they could have implemented that would have avoided that harm. 

If AI firms aren't covered by product liability, they're liable according to standard tort law, which means they're liable if they're negligent under a reasonable person standard. 

Liability law also gives (some, limited) teeth to NIST standards. If a firm can show that it was following NIST safety standards, this gives it a strong argument that it wasn't being negligent. 

I share your scepticism of liability interventions as mechanisms for making important dents in the AI safety problem. Prior to the creation of the EPA, firms were still in principle liable for the harms their pollution caused, but the tort law system is generically a very messy way to get firms to reduce accident risks. It's expensive and time consuming to go through the court system, courts are reluctant to award punitive damages which means that externalities aren't internalised even theory (in expectation for firms,) and you need to find a plaintiff with standing to sue firms. 

I think there are still some potentially important use cases for liability for reducing AI risks:

• Making clear the legal responsibilities of private sector auditors (I'm quite confident that this is a good idea)
• Individual liability for individuals with safety responsibilities at firms (although this would be politically unpopular on the right I'd expect) 
• Creating safe harbours from liability if firms fulfil some set of safety obligations (similarly to the California bill) - ideally safety obligations that are updated over time and tied to best practice
• Requiring insurance to cover liability and using this to create better safety practices as firms to reduce insurance premiums and satisfy insurers' requirements for coverage
• Tieing liability to specific failures modes that we expect to correlate with catastrophic failure modes, perhaps tied to a punitive damages regime - for instance holding a firm liable, including for punitive damages if a model causes harm via say goal misgenerlisation or firms lacking industry standard risk management practices 

To be clear, I'm still sceptical of liability-based solutions and reasonably strongly favour regulatory proposals (where specific liability provisions will still play an important role.)

I'm not a lawyer and have no legal training. 

I think we should be talking more about potentially denying a frontier AI license to any company that causes a major disaster (within some future licensing regime), where a company’s record before the law passes will be taken into amount.

One alternative method to liability for the AI companies is strong liability for companies using AI systems. This does not directly address risks from frontier labs having dangerous AIs in-house, but helps with risks from AI system deployment in the real world. It indirectly affects labs, because they want to sell their AIs.

A lot of this is the default. For example, Air Canada recently lost a court case after claiming a chatbot promising a refund wasn't binding on them. However, there could be related opportunities. Companies using AI systems currently don't have particularly good ways to assess risks from AI deployment, and if models continue getting more capable while reliability continues lagging, they are likely to be willing to pay an increasing amount for ways to get information on concrete risks, guard against it, or derisk it (e.g. through insurance against their deployed AI systems causing harms). I can imagine a service that sells AI-using companies insurance against certain types of deployment risk, that could also double as a consultancy / incentive-provider for lower-risk deployments. I'd be interested to chat if anyone is thinking along similar lines.

There are analogies here in pollution.  Some countries force industry to post bonds for damage to the local environment.  This is a new innovation that may be working.

The reason the superfund exists in the US is because liability for pollution can be so severe that a company would simply cease to operate, and the mess would not be cleaned up.

In practice, when it comes to taking environmental risks, better to burn the train cars of vinyl chloride, creating a catastrophe too expensive for anyone to clean up or even comprehend than to allow a few gallons to leak, creating an expensive accident that you can actually afford.

This article makes some fine points but some misleading ones and its thesis is wrong, I think. Bottom line: Anthropic does lots of good things and is doing much better than being maximally selfish/ruthless. (And of course this is possible, contra the article — Anthropic is led by humans who have various beliefs which may entail that they should make tradeoffs in favor of safety. The space of AI companies is clearly not so perfectly competitive that anyone who makes tradeoffs in favor of safety becomes bankrupt and irrelevant.)

It’s pushing back on a landmark California bill to regulate AI.

Yep, Anthropic's policy advocacy seems bad.

It’s taking money from Google and Amazon in a way that’s drawing antitrust scrutiny. And it’s being accused of aggressively scraping data from websites without permission, harming their performance.

My impression is that these are not big issues. I'm open to hearing counterarguments. [Edit: the scraping is likely a substantial issue for many sites; see comment below. (It is not an x-safety issue, of course.)]

Here’s another tension at the heart of AI development: Companies need to hoover up reams and reams of high-quality text from books and websites in order to train their systems. But that text is created by human beings, and human beings generally do not like having their work used without their consent.

I agree this is not ideal-in-all-ways but I'm not aware of a better alternative.

Web publishers and content creators are angry. Matt Barrie, chief executive of Freelancer.com, a platform that connects freelancers with clients, said Anthropic is “the most aggressive scraper by far,” swarming the site even after being told to stop. “We had to block them because they don’t obey the rules of the internet. This is egregious scraping [that] makes the site slower for everyone operating on it and ultimately affects our revenue.”

This is surprising to me. I'm not familiar with the facts. Seems maybe bad.

Deals like these [investments from Amazon and Google] always come with risks. The tech giants want to see a quick return on their investments and maximize profit. To keep them happy, the AI companies may feel pressure to deploy an advanced AI model even if they’re not sure it’s safe.

Yes there's nonzero force to this phenomenon, but my impression is that Amazon and Google have almost no hard power over Anthropic and no guaranteed access to its models (unlike e.g. how OpenAI may have to share its models with Microsoft, even if OpenAI thinks the model is unsafe), and I'm not aware of a better alternative.

[Edit: mostly I just think this stuff is not-what-you-should-focus-on if evaluating Anthropic on safety — there are much bigger questions.]

There are some things Anthropic should actually do better. There are some ways it's kinda impure, like training on the internet and taking investments. Being kinda impure is unavoidable if you want to be a frontier AI company. Insofar as Anthropic is much better on safety than other frontier AI companies, I'm glad it exists.

[Edit: I'm slightly annoyed that the piece feels one-sided — it's not trying to figure out whether Anthropic makes tradeoffs for safety or how it compares to other frontier AI companies, instead it's collecting things that sound bad. Maybe this is fine since the article's role is to contribute facts to the discourse, not be the final word.]

I think there's a decent case that SB 1047 would improve Anthropic's business prospects, so I'm not sure this narrative makes sense. On one hand, SB 1047 might make it less profitable to run an AGI company, which is bad for Anthropic's business plan. But Anthropic is perhaps the best positioned of all AGI companies to comply with the requirements of SB 1047, and might benefit significantly from their competitors being hampered by the law. 

The good faith interpretation of Anthropic's argument would be that the new agency created by the bill might be very bad at issuing guidance that actually reduces x-risk, and you might prefer the decision-making of AI labs with a financial incentive to avoid catastrophes without additional pressure to follow the exact recommendations of the new agency. 

One question I have is whether Nancy Pelosi was asked and agreed to do this, or whether Nancy Pelosi identified this proactively as an opportunity to try to win back some tech folks to the Dem side.  Substantially changes our estimate of how much influence the labs have in this conversation. 

One plausible explanation is that industry still thinks it's likely to kill the bill, and they just didn't feel like they needed to play their cards sooner. 

But this still leaves me surprised– I would've expected that it's in industry's interest to kill the bill earlier in the process because:

1. It might be easier to kill earlier on because it hasn't gained much traction/support
2. If you want to appear like you're open to regulation (which seems to be the policy of major AI companies), you probably want to kill it in a relatively silent/invisible way. If you have to be very loud and public and you get to the point where there are a bunch of media articles about it, you lose some credibility/reputation/alliances (and indeed I do think industry has lost some of this "plausibility of good will" as a result of the SB1047 saga)

I don't think the risk ordering is obvious at all, especially not between #2 and #3, and especially not if you also took into account tractability concerns and risks separate from extinction (e.g. stable totalitarianism, s-risks). Even if you thought coordinating with China might be worth it, I think it should be at least somewhat obvious why the US government [/ and its allies] might be very uncomfortable building a coalition with, say, North Korea or Russia. Even between #1 and #2, the probable increase in risks of centralization might make it not worth it, at least in some worlds, depending on how optimistic one might be about e.g. alignment or offense-defense balance from misuse of models with dangerous capabilities.

I also don't think it's obvious alternative paradigms would necessarily be both safer and tractable enough, even on 10-year timelines, especially if you don't use AI automation (using the current paradigm, probably) to push those forward.

Competition is fractal. There are multiple hierarchies (countries/departments/agencies/etc, corporations/divisions/teams/etc), with individual humans acting on their own behalf. Often, individuals have influence and goals in multiple hierarchies.

Your 1/2/3 delineation is not the important part. It’s going to be all 3, with chaotic shifts as public perception, funding, and regulation shifts around.

Agree -- I think people need to be prepared for "try-or-die" scenarios.  

One unfun one I'll toss into the list: "Company A is 12 months from building Cthulhu, and governments truly do not care and there is extremely strong reason to believe that will not change in the next year.  All our policy efforts have failed, our existing technical methods are useless, and the end of the world has come.  Everyone report for duty at Company B, we're going to try to roll the hard six."

You are probably already familiar with this, but re option 3, the Multilateral AGI Consortium (MAGIC) proposal is I assume along the lines of what you are thinking.

My model is basically just "Newsom likely doesn't want to piss off Big Tech or Pelosi, and the incentive to not veto doesn't seem that high, and so seems highly likely to veto, and 50% veto seems super low". My fair is, like, 80% veto I think?

I'm not that compelled by the base rates argument, because I think the level of controversy over the bill is atypically high, so it's quite out of distribution. Eg I think Pelosi denouncing it is very unusual for a state Bill and a pretty big deal

But it seems like SB1047 hasn't been very controversial among CA politicians.

I think this isn't true. Concretely, I bet that if you looked at the distribution of Democratic No votes among bills that reached Newsom's desk, this one would be among the highest (7 No votes and a bunch of not-voting, which I think is just a polite way to vote No; source). I haven't checked and could be wrong!

My take is basically the same as Neel's, though my all-things-considered guess is that he's 60% or so to veto. My position on Manifold is in large part an emotional hedge. (Otherwise I would be placing much smaller bets in the same direction.)

@Peter Barnett [LW · GW] @Rob Bensinger [LW · GW] @habryka [LW · GW] @Zvi [LW · GW] @davekasten [LW · GW] @Peter Wildeford [LW · GW] you come to mind as people who might be interested. 

See also Wikipedia Page about the report (but IMO reading sections of the actual report is worth it.)

Wild speculation: they also have a sort of we're-watching-but-unsure provision about cyber operations capability in their most recent RSP update.  In it, they say in part that "it is also possible that by the time these capabilities are reached, there will be evidence that such a standard is not necessary (for example, because of the potential use of similar capabilities for defensive purposes)."  Perhaps they're thinking that automated vulnerability discovery is at least plausibly on-net-defensive-balance-favorable*, and so they aren't sure it should be regulated as closely, even if in still in some informal sense "dual use" ?

Again, WILD speculation here.  

*A claim that is clearly seen as plausible by, e.g., the DARPA AI Grand Challenge effort.

I'm obviously biased, but I would recommend my post on macrostrategy of evals: The case for more ambitious language model evals [LW · GW].

@Ryan Kidd [LW · GW] @Lee Sharkey [LW · GW] I suspect you'll have useful recommendations here.