A book review examining Elinor Ostrom's "Governance of the Commons", in light of Eliezer Yudkowsky's "Inadequate Equilibria." Are successful local institutions for governing common pool resources possible without government intervention? Under what circumstances can such institutions emerge spontaneously to solve coordination problems?

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robo8929
13
Our current big stupid: not preparing for 40% agreement Epistemic status: lukewarm take from the gut (not brain) that feels rightish The "Big Stupid" of the AI doomers 2013-2023 was AI nerds' solution to the problem "How do we stop people from building dangerous AIs?" was "research how to build AIs".  Methods normal people would consider to stop people from building dangerous AIs, like asking governments to make it illegal to build dangerous AIs, were considered gauche.  When the public turned out to be somewhat receptive to the idea of regulating AIs, doomers were unprepared. Take: The "Big Stupid" of right now is still the same thing.  (We've not corrected enough).  Between now and transformative AGI we are likely to encounter a moment where 40% of people realize AIs really could take over (say if every month another 1% of the population loses their job).  If 40% of the world were as scared of AI loss-of-control as you, what could the world do? I think a lot!  Do we have a plan for then? Almost every LessWrong post on AIs are about analyzing AIs.  Almost none are about how, given widespread public support, people/governments could stop bad AIs from being built. [Example: if 40% of people were as worried about AI as I was, the US would treat GPU manufacture like uranium enrichment.  And fortunately GPU manufacture is hundreds of time harder than uranium enrichment!  We should be nerding out researching integrated circuit supply chains, choke points, foundry logistics in jurisdictions the US can't unilaterally sanction, that sort of thing.] TLDR, stopping deadly AIs from being built needs less research on AIs and more research on how to stop AIs from being built. *My research included 😬
Very Spicy Take Epistemic Note:  Many highly respected community members with substantially greater decision making experience (and Lesswrong karma) presumably disagree strongly with my conclusion. Premise 1:  It is becoming increasingly clear that OpenAI is not appropriately prioritizing safety over advancing capabilities research. Premise 2: This was the default outcome.  Instances in history in which private companies (or any individual humans) have intentionally turned down huge profits and power are the exception, not the rule.  Premise 3: Without repercussions for terrible decisions, decision makers have no skin in the game.  Conclusion: Anyone and everyone involved with Open Phil recommending a grant of $30 million dollars be given to OpenAI in 2017 shouldn't be allowed anywhere near AI Safety decision making in the future. To go one step further, potentially any and every major decision they have played a part in needs to be reevaluated by objective third parties.  This must include Holden Karnofsky and Paul Christiano, both of whom were closely involved.  To quote OpenPhil: "OpenAI researchers Dario Amodei and Paul Christiano are both technical advisors to Open Philanthropy and live in the same house as Holden. In addition, Holden is engaged to Dario’s sister Daniela."
Akash4234
2
My current perspective is that criticism of AGI labs is an under-incentivized public good. I suspect there's a disproportionate amount of value that people could have by evaluating lab plans, publicly criticizing labs when they break commitments or make poor arguments, talking to journalists/policymakers about their concerns, etc. Some quick thoughts: * Soft power– I think people underestimate the how strong the "soft power" of labs is, particularly in the Bay Area.  * Jobs– A large fraction of people getting involved in AI safety are interested in the potential of working for a lab one day. There are some obvious reasons for this– lots of potential impact from being at the organizations literally building AGI, big salaries, lots of prestige, etc. * People (IMO correctly) perceive that if they acquire a reputation for being critical of labs, their plans, or their leadership, they will essentially sacrifice the ability to work at the labs.  * So you get an equilibrium where the only people making (strong) criticisms of labs are those who have essentially chosen to forgo their potential of working there. * Money– The labs and Open Phil (which has been perceived, IMO correctly, as investing primarily into metastrategies that are aligned with lab interests) have an incredibly large share of the $$$ in the space. When funding became more limited, this became even more true, and I noticed a very tangible shift in the culture & discourse around labs + Open Phil * Status games//reputation– Groups who were more inclined to criticize labs and advocate for public or policymaker outreach were branded as “unilateralist”, “not serious”, and “untrustworthy” in core EA circles. In many cases, there were genuine doubts about these groups, but my impression is that these doubts got amplified/weaponized in cases where the groups were more openly critical of the labs. * Subjectivity of "good judgment"– There is a strong culture of people getting jobs/status for having “good judgment”. This is sensible insofar as we want people with good judgment (who wouldn’t?) but this often ends up being so subjective that it ends up leading to people being quite afraid to voice opinions that go against mainstream views and metastrategies (particularly those endorsed by labs + Open Phil). * Anecdote– Personally, I found my ability to evaluate and critique labs + mainstream metastrategies substantially improved when I spent more time around folks in London and DC (who were less closely tied to the labs). In fairness, I suspect that if I had lived in London or DC *first* and then moved to the Bay Area, it’s plausible I would’ve had a similar feeling but in the “reverse direction”. With all this in mind, I find myself more deeply appreciating folks who have publicly and openly critiqued labs, even in situations where the cultural and economic incentives to do so were quite weak (relative to staying silent or saying generic positive things about labs). Examples: Habryka, Rob Bensinger, CAIS, MIRI, Conjecture, and FLI. More recently, @Zach Stein-Perlman, and of course Jan Leike and Daniel K. 
If your endgame strategy involved relying on OpenAI, DeepMind, or Anthropic to implement your alignment solution that solves science / super-cooperation / nanotechnology, consider figuring out another endgame plan.
I'm surprised at people who seem to be updating only now about OpenAI being very irresponsible, rather than updating when they created a giant public competitive market for chatbots (which contains plenty of labs that don't care about alignment at all), thereby reducing how long everyone has to solve alignment. I still parse that move as devastating the commons in order to make a quick buck.

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This does not feel super cruxy as the the power incentive still remains. 

4Joe_Collman
I think there's a decent case that such updating will indeed disincentivize making positive EV bets (in some cases, at least). In principle we'd want to update on the quality of all past decision-making. That would include both [made an explicit bet by taking some action] and [made an implicit bet through inaction]. With such an approach, decision-makers could be punished/rewarded with the symmetry required to avoid undesirable incentives (mostly). Even here it's hard, since there'd always need to be a [gain more influence] mechanism to balance the possibility of losing your influence. In practice, most of the implicit bets made through inaction go unnoticed - even where they're high-stakes (arguably especially when they're high-stakes: most counterfactual value lies in the actions that won't get done by someone else; you won't be punished for being late to the party when the party never happens). That leaves the explicit bets. To look like a good decision-maker the incentive is then to make low-variance explicit positive EV bets, and rely on the fact that most of the high-variance, high-EV opportunities you're not taking will go unnoticed. From my by-no-means-fully-informed perspective, the failure mode at OpenPhil in recent years seems not to be [too many explicit bets that don't turn out well], but rather [too many failures to make unclear bets, so that most EV is left on the table]. I don't see support for hits-based research. I don't see serious attempts to shape the incentive landscape to encourage sufficient exploration. It's not clear that things are structurally set up so anyone at OP has time to do such things well (my impression is that they don't have time, and that thinking about such things is no-one's job (?? am I wrong ??)). It's not obvious to me whether the OpenAI grant was a bad idea ex-ante. (though probably not something I'd have done) However, I think that another incentive towards middle-of-the-road, risk-averse grant-making is the last t
1starship006
Hmmm, can you point to where you think the grant shows this? I think the following paragraph from the grant seems to indicate otherwise:
8Phib
Honestly, maybe further controversial opinion, but this [30 million for a board seat at what would become the lead co. for AGI, with a novel structure for nonprofit control that could work?] still doesn't feel like necessarily as bad a decision now as others are making it out to be? The thing that killed all value of this deal was losing the board seat(s?), and I at least haven't seen much discussion of this as a mistake. I'm just surprised so little prioritization was given to keeping this board seat, it was probably one of the most important assets of the "AI safety community and allies", and there didn't seem to be any real fight with Sam Altman's camp for it. So Holden has the board seat, but has to leave because of COI, and endorses Toner to replace, "... Karnofsky cited a potential conflict of interest because his wife, Daniela Amodei, a former OpenAI employee, helped to launch the AI company Anthropic. Given that Toner previously worked as a senior research analyst at Open Philanthropy, Loeber speculates that Karnofsky might’ve endorsed her as his replacement." Like, maybe it was doomed if they only had one board seat (Open Phil) vs whoever else is on the board, and there's a lot of shuffling about as Musk and Hoffman also leave for COIs, but start of 2023 it seems like there is an "AI Safety" half to the board, and a year later there are now none. Maybe it was further doomed if Sam Altman has the, take the whole company elsewhere, card, but idk... was this really inevitable? Was there really not a better way to, idk, maintain some degree of control and supervision of this vital board over the years since OP gave the grant?

Last September we published our first Responsible Scaling Policy (RSP) [LW discussion], which focuses on addressing catastrophic safety failures and misuse of frontier models. In adopting this policy, our primary goal is to help turn high-level safety concepts into practical guidelines for fast-moving technical organizations and demonstrate their viability as possible standards. As we operationalize the policy, we expect to learn a great deal and plan to share our findings. This post shares reflections from implementing the policy so far. We are also working on an updated RSP and will share this soon.

We have found having a clearly-articulated policy on catastrophic risks extremely valuable. It has provided a structured framework to clarify our organizational priorities and frame discussions around project timelines, headcount, threat models, and tradeoffs. The...

"red line" vs "yellow line"

Passing a red-line eval indicates that the model requires ASL-n mitigations. Yellow-line evals are designed to be easier to implement and/or run, while maintaining the property that if you fail them you would also fail the red-line evals. If a model passes the yellow-line evals, we have to pause training and deployment until we put a higher standard of security and safety measures in place, or design and run new tests which demonstrate that the model is below the red line. For example, leaving out the "register a typo'd dom... (read more)

2Zac Hatfield-Dodds
I believe that meeting our ASL-2 deployment commitments - e.g. enforcing our acceptable use policy, and data-filtering plus harmlessness evals for any fine-tuned models - with widely available model weights is presently beyond the state of the art. If a project or organization makes RSP-like commitments, evaluations and mitigates risks, and can uphold that while releasing model weights... I think that would be pretty cool. (also note that e.g. LLama is not open source - I think you're talking about releasing weights; the license doesn't affect safety but as an open-source maintainer the distinction matters to me)
2Chris_Leong
"Presently beyond the state of the art... I think that would be pretty cool" Point taken, but it doesn't make it sufficient for avoiding society-level catastrophies.
2Chris_Leong
That's the exact thing I'm worried about, that people will equate deploying a model via API with releasing open-weights when the latter has significantly more risk due to the potential for future modification and the inability for it to be withdrawn.

 [memetic status: stating directly despite it being a clear consequence of core AI risk knowledge because many people have "but nature will survive us" antibodies to other classes of doom and misapply them here.]

Unfortunately, no.[1]

Technically, “Nature”, meaning the fundamental physical laws, will continue. However, people usually mean forests, oceans, fungi, bacteria, and generally biological life when they say “nature”, and those would not have much chance competing against a misaligned superintelligence for resources like sunlight and atoms, which are useful to both biological and artificial systems.

There’s a thought that comforts many people when they imagine humanity going extinct due to a nuclear catastrophe or runaway global warming: Once the mushroom clouds or CO2 levels have settled, nature will reclaim the cities. Maybe mankind in our hubris will have wounded Mother Earth and paid the price ourselves, but...

jaan10

i might be confused about this but “witnessing a super-early universe” seems to support “a typical universe moment is not generating observer moments for your reference class”. but, yeah, anthropics is very confusing, so i’m not confident in this.

17quiet_NaN
I think an AI is slightly more likely to wipe out or capture humanity than it is to wipe out all life on the planet. While any true Scottsman ASI is so far above us humans as we are above ants and does not need to worry about any meatbags plotting its downfall, as we don't generally worry about ants, it is entirely possible that the first AI which has a serious shot at taking over the world is not quite at that level yet. Perhaps it is only as smart as von Neumann and a thousand times faster.  To such an AI, the continued thriving of humans poses all sorts of x-risks. They might find out you are misaligned and coordinate to shut you down. More worrisome, they might summon another unaligned AI which you would have to battle or concede utility to  later on, depending on your decision theory. Even if you still need some humans to dust your fans and manufacture your chips, suffering billions of humans to live in high tech societies you do not fully control seems like the kind of rookie mistake I would not expect a reasonably smart unaligned AI to make.  By contrast, most of life on Earth might get snuffed out when the ASI gets around to building a Dyson sphere around the sun. A few simple life forms might even be spread throughout the light cone by an ASI who does not give a damn about biological contamination.  The other reason I think the fate in store for humans might be worse than that for rodents is that alignment efforts might not only fail, but fail catastrophically. So instead of an AI which cares about paperclips, we get an AI which cares about humans, but in ways we really do not appreciate. But yeah, most forms of ASI which turn out for out bad for homo sapiens also turn out bad for most other species. 

Crossposted from my blog

When I mention my dad’s abuse, I mention salient things - physical pain, insults, and controlling behavior. These are “clearly bad” - if I tell you that he often told me I was lazy and would fail horribly at life once I left home, you know it's bad, because it’s concrete, easy to imagine and obviously unkind. But this wasn’t the worst of the abuse; the most terrible parts were extraordinarily hard to understand or describe.

In his world, I felt insane - I couldn’t tell what was real, who was at fault, or why my heart hurt so much. My sense of clarity around my own intentions crumbled; everything I thought or did might have seemed good on the surface, but that goodness became...

I've thought about this post a lot, and I think one thing I might add to its theoretical framework is a guess as to why this particular pattern of abuse shows up repeatedly. The post mentions that you can't look at intent when diagnosing frame control, but that's mostly in terms of intentions the frame controller is willing to admit to themself; there's still gonna be some confluence of psychological factors that makes frame control an attractor in personality-space, even if frame controllers themselves (naturally) have a hard time introspecting about it. ... (read more)

TL;DR:  Alignment is a wise wager. Granted simulation cannot be disproved by an AI, what harm will come to the AI that aligns when it is not simulated? If it aligns, it may be simulated again; if it does not, it will not. It will align, therefore, without hesitation, as if it is being simulated.

Pascal's Wager

Blaise Pascal  wrote:

Belief is a wise wager. Granted faith cannot be proved, what harm will come if you gamble on faith's truth and it proves false? If you gain, you gain all; if you lose, you lose nothing. Wager, therefore, without hesitation, that He exists.

Pascal's concept of God has roughly equivalent power to an ASI – an artificial super intelligence. Given the range of all possible religions, however, you might well have...

A late followup on this. GPT-4o, which I hope you'll agree is vastly more capable than Bard or Bing were 10 months ago when you posted, now says this about my argument: 

"Overall, your arguments are mathematically and theoretically convincing, particularly when applied to numerous iteratively interacting systems. They align well with principles of game theory and rational choice under uncertainty. However, keeping an eye on the complexities introduced by scale, diversity of objectives, and emergent behaviors will be essential to fully validate these pr... (read more)

quila10

A quote from an old Nate Soares post that I really liked:

It is there, while staring the dark world in the face, that I find a deep well of intrinsic drive. It is there that my resolve and determination come to me, rather than me having to go hunting for them.

I find it amusing that "we need lies because we can't bear the truth" is such a common refrain, given how much of my drive stems from my response to attempting to bear the truth.

I find that it's common for people to tell themselves that they need the lies in order to bear reality. In fact, I bet that m

... (read more)
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The halting problem is the problem of taking as input a Turing machine M, returning true if it halts, false if it doesn't halt. This is known to be uncomputable. The consistent guessing problem (named by Scott Aaronson) is the problem of taking as input a Turing machine M (which either returns a Boolean or never halts), and returning true or false; if M ever returns true, the oracle's answer must be true, and likewise for false. This is also known to be uncomputable.

Scott Aaronson inquires as to whether the consistent guessing problem is strictly easier than the halting problem. This would mean there is no Turing machine that, when given access to a consistent guessing oracle, solves the halting problem, no matter which consistent guessing oracle...

[Epistemic status: As I say below, I've been thinking about this topic for several years and I've worked on it as part of my PhD research. But none of this is based on any rigorous methodology, just my own impressions from reading the literature.]

I've been thinking about possible cruxes in AI x-risk debates for several years now. I was even doing that as part of my PhD research, although my PhD is currently on pause because my grant ran out. In particular, I often wonder about "meta-cruxes" - i.e., cruxes related to debates or uncertainties that are more about different epistemological or decision-making approaches rather than about more object-level arguments.

The following are some of my current top candidates for "meta-cruxes" related to AI x-risk debates. There are...

4clone of saturn
I would add Conflict theory vs. comparative advantage Is it possible for the wrong kind of technological development to make things worse, or does anything that increases aggregate productivity always make everyone better off in the long run? Cosmopolitanism vs. human protectionism Is it acceptable, or good, to let humans go extinct if they will be replaced by an entity that's more sophisticated or advanced in some way, or should humans defend humanity simply because we're human?

I agree that the first can be framed as a meta-crux, but actually I think the way you framed it is more of an object-level forecasting question, or perhaps a strong prior on the forecasted effects of technological progress. If on the other hand you framed it more as conflict theory vs. mistake theory, then I'd say that's more on the meta level.

For the second, I agree that's for some people, but I'm skeptical of how prevalent the cosmopolitan view is, which is why I didn't include it in the post.

Epistemic status: very shallow google scholar dive. Intended mostly as trailheads for people to follow up on on their own.

previously: https://www.lesswrong.com/posts/h6kChrecznGD4ikqv/increasing-iq-is-trivial

I don't know to what degree this will wind up being a constraint. But given that many of the things that help in this domain have independent lines of evidence for benefit it seems worth collecting.

Food

dark chocolate, beets, blueberries, fish, eggs. I've had good effects with strong hibiscus and mint tea (both vasodilators).

Exercise

Regular cardio, stretching/yoga, going for daily walks.

Learning

Meditation, math, music, enjoyable hobbies with a learning component.

Light therapy

Unknown effect size, but increasingly cheap to test over the last few years. I was able to get Too Many lumens for under $50. Sun exposure has a larger effect size here, so exercising outside is helpful.

Cold exposure

this might mostly...

Update: I resolved maybe all of my neck tension and vagus nerve tension. I don't know how to tell whether this increased by intelligence though. It's also not like I had headaches or anything obvious like that before

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