august 2024 guaranteed safe ai newsletter

in case i forgot last month, here's a link to july

A wager you say

One proof of concept for the GSAI stack would be a well-understood mechanical engineering domain automated to the next level and certified to boot. How about locks? Needs a model of basic physics, terms in some logic for all the parts and how they compose, and some test harnesses that simulate an adversary. Can you design and manufacture a provably unpickable lock? 

Zac Hatfield-Dodds (of hypothesis/pytest and Anthropic, was offered and declined authorship on the GSAI position paper) challenged Ben Goldhaber to a bet after Ben coauthored a post with Steve Omohundro [LW(p) · GW(p)]. It seems to resolve in 2026 or 2027, the comment thread should get cleared up once Ben gets back from Burning Man. The arbiter is Raemon from LessWrong.

Zac says you can’t get a provably unpickable lock on this timeline. Zac gave (up to) 10:1 odds, so recall that the bet can be a positive expected value for Ben even if he thinks the event is most likely not going to happen.

For funsies, let’s map out one path of what has to happen for Zac to pay Ben $10k. This is not the canonical path, but it is a path:

1. Physics to the relevant granularity (question: can human lockpicks leverage sub-newtownian issues?) is conceptually placed into type theory or some calculus. I tried a riemann integral in coq once (way once), so it occurs to me that you need to decide if you want just the functional models (perhaps without computation / with proof irrelevance) in your proof stack or if you want the actual numerical analysis support in there as well.
2. Good tooling, library support, etc. around that conceptual work (call it mechlib) to provide mechanical engineering primitives
3. A lock designing toolkit, depending on mechlib, is developed
4. Someone (e.g. a large language model) is really good at programming in the lock designing toolkit. They come up with a spec L. 
5. You state the problem “forall t : trajectories through our physics simulation, if L(t) == open(L) then t == key(L)”
6. Then you get to write a nasty gazillion line Lean proof
7. Manufacture a lock (did I mention that the design toolkit has links to actual manufacturing stacks?)
8. Bring a bunch to DefCon 2027 and send another to the lockpicking lawyer
9. Everyone fails. Except Ben and the army of postdocs that $9,999 can buy.

Looks like after the magnificent research engineering in steps 1 and 2, the rest is just showing off and justifying those two steps. Of course, in a world where we have steps 1 and 2 we have a great deal of transformative applications of formal modeling and verification just in reach, and we’ll need a PoC like locks to practice and concretize the workflow. 

Cryptography applications tend to have a curse of requiring a lot of work after the security context, permission set, and other requirements are frozen in stone, which means that when the requirements change you have to start over and throw out a bunch of work (epistemic status: why do you think so many defi projects have more whitepapers than users?). The provably unpickable lock has 2 to 10 x that problem– get the granularity wrong in step one, most of your mechlib implementation won’t be salvageable. As the language model iterates on the spec L in step 5, the other language model has to iterate on the proof in step 6, because the new spec will break most of the proof.

Sorry I don’t know any mechanical engineering, Ben, otherwise I’d take some cracks at it. The idea of a logic such that its denotation is a bunch of mechanical engineering primitives seems interesting enough that my “if it was easy to do in less than a year someone would’ve, therefore there must be a moat” heuristic is tingling. Perhaps oddly, the quantum semantics folks (or with HoTT!) seem to have been productive, but I don’t know how much of that is translatable to mechanical engineering.

Reinforcement learning from proof assistant feedback, and yet more monte carlo tree search

DeepSeek’s paper 

The steps are pretraining, supervised finetuning, RLPAF (reinforcement learning from proof assistant feedback), and MCTS (monte carlo tree search). RLPAF is not very rich: it’s a zero reward for any bug at all and a one for a happy typechecker. Glad they got that far with just that. 

You can use the model at deepseek.com.

Harmonic ships their migration of miniF2F to Lean 4, gets 90% on it, is hiring

From their “one month in” newsletter. “Aristotle”, which has a mysterious methodology since I’ve only seen their marketing copy rather than an arxiv paper, gets 90% on miniF2F 4 when prompted with natural language proofs. It doesn’t look to me like the deepseek or LEGO papers do that? I could be wrong. It’s impressive just to autoformalize natural language proofs, I guess I’m still wrapping my head around how much harder it is (for an LLM) to implement coming up with the proof as well. 

Jobs: research engineer and software engineer

Atlas ships their big google doc alluded to in the last newsletter

Worth a read! The GSAI stack is large and varied, and this maps out the different sub-sub-disciplines. From the executive summary:

You could start whole organizations for every row in this table, and I wouldn’t be a part of any org that targets more than a few at once for fear of being unfocused. See the doc for more navigation (see what I did there? Navigating like with an atlas, perhaps? Get it?) of the field’s opportunities.^[1]

Efficient shield synthesis via state-space transformation

Shielding is an area of reactive systems and reinforcement learning that marks states as unsafe and synthesizes a kind of guarding layer between the agent and the environment that prevents unsafe actions from being executed in the environment. So in the rejection sampling flavored version, it literally intercepts the unsafe action and tells the agent “we’re not running that, try another action”. One of the limitations in this literature is computational cost, shields are, like environments, state machines plus some frills, and there may simply be too many states. This is the limitation that this paper focuses on. 

We consider the problem of synthesizing safety strategies for control systems, also known as shields. Since the state space is infinite, shields are typically computed over a finite-state abstraction, with the most common abstraction being a rectangular grid. However, for many systems, such a grid does not align well with the safety property or the system dynamics. That is why a coarse grid is rarely sufficient, but a fine grid is typically computationally infeasible to obtain. In this paper, we show that appropriate state-space transformations can still allow to use a coarse grid at almost no computational overhead. We demonstrate in three case studies that our transformation-based synthesis outperforms a standard synthesis by several orders of magnitude. In the first two case studies, we use domain knowledge to select a suitable transformation. In the third case study, we instead report on results in engineering a transformation without domain knowledge.

Besides cost, demanding a lot of domain knowledge is another limitation of shields, so this is an especially welcome development.

Funding opportunities

ARIA jumped right to technical area three (TA3), prototyping the gatekeeper. Deadline October 2nd. Seems geared toward cyber-physical systems folks. In the document:

Note that verified software systems is an area which is highly suitable for a simplified gatekeeper workflow, in which the world-model is implicit in the specification logic. However, in the context of ARIA’s mission to “change the perception of what’s possible or valuable,” we consider that this application pathway is already perceived to be possible and valuable by the AI community. As such, this programme focuses on building capabilities to construct guaranteed-safe AI systems in cyber-physical domains. That being said, if you are an organisation which specialises in verified software, we would love to hear from you outside of this solicitation about the cyber-physical challenges that are just at the edge of the possible for your current techniques.

This is really cool stuff, I hope they find brave and adventurous teams. I had thought gatekeeper prototypes would be in minecraft or mujoco (and asked a funder if they’d support me in doing that), so it’s wild to see them going for actual cyberphysical systems so quickly. 

Paper club

Add to your calendar. On September 19th we will read a paper about assume-guarantee contracts with learned components. I’m liable to have made a summary slide deck to kick us off, but if I don’t, we’ll quietly read together for the first 20-30 minutes then discuss. The google meet room in the gcal event by default.  

Andrew Dickson’s excellent post

See Limitations on Formal Verification for AI Safety [LW · GW] over on LessWrong. I have a lot of agreements, and my disagreements are more a matter of what deserves emphasis than the fundamentals. Overall, I think the Tegmark/Omohundro paper failed to convey a swisscheesey worldview, and sounded too much like “why not just capture alignment properties in ‘specs’ and prove the software ‘correct’?” (i.e. the vibe I was responding to in my very pithy post [LW · GW]). However, I think my main reason I’m not using Dickson’s post as a reason to just pivot all my worldview and resulting research is captured in one of Steve’s comments:

I'm focused on making sure our infrastructure is safe against AI attacks.

Like, a very strong version I almost endorse is “GSAI isn’t about AI at all, it’s about systems coded by extremely powerful developers (which happen to be AIs)”, and ensuring safety, security, and reliability capabilities scale at similar speeds with other kinds of capabilities.

It looks like one can satisfy Dickson just by assuring him that GSAI is a part of a swiss cheese stack, and that no one is messianically promoting One Weird Trick To Solve Alignment. Of course, I do hope that no one is messianically promoting One Weird Trick…

1. ^{^}

   One problem off the top of my head regarding the InterFramework section: Coq and Lean seems the most conceptually straightforward since they have the same underlying calculus, but even there just a little impredicativity or coinduction could lead to extreme headaches. Now you can have a model at some point in the future that steamrolls over these headaches, but then you have a social problem of the broader Lean community not wanting to upstream those changes– various forks diverging fundamentally seems problematic to me, would lead to a lot of duplicated work and missed opportunities for collaboration. I plan to prompt Opus 3.5 with “replicate flocq in lean4” as soon as I get access to the model, but how much more prompting effort will it be to ensure compliance with preexisting abstractions and design patterns, so that it can not only serve my purposes but be accepted by the community? At least there’s no coinduction in flocq, though some of the proofs may rely on set impredicativity for all I know (I haven’t looked at it in a while).

Good arguments - notes on Craft of Research chapter 7

Arguments take place in 5 parts.

1. Claim: What do you want me to believe?
2. Reasons: Why should I agree?
3. Evidence: How do you know? Can you back it up?
4. Acknowledgment and Response: But what about ... ?
5. Warrant: How does that follow?

This can be modeled as a conversation with readers, where the reader prompts the writer to taking the next step on the list.

Claim ought to be supported with reasons. Reasons ought to be based on evidence. Arguments are recursive: a part of an argument is an acknowledgment of an anticipated response, and another argument addresses that response. Finally, when the distance between a claim and a reason grows large, we draw connections with something called warrants.

The logic of warrants proceeds in generalities and instances. A general circumstance predictably leads to a general consequence, and if you have an instance of the circumstance you can infer an instance of the consequence.

Arguing in real life papers is complexified from the 5 steps, because

• Claims should be supported by two or more reasons
• A writer can anticipate and address numerous responses. As I mentioned, arguments are recursive, especially in the anticipated response stage, but also each reason and warrant can necessitate a subargument.

You might embrace a claim too early, perhaps even before you have done much research, because you "know" you can prove it. But falling back on that kind of certainty will just keep you from doing your best thinking.

Thinking about a top-level post on FOMO and research taste

• Fear of missing out defined as inability to execute on a project cuz there's a cooler project if you pivot
• but it also gestures at more of a strict negative, where you think your project sucks before you finish it, so you never execute
• was discussing this with a friend: "yeah I mean lesswrong is pretty egregious cuz it sorta promotes this idea of research taste as the ability to tear things down, which can be done armchair"
• I've developed strategies to beat this FOMO and gain more depth and detail with projects (too recent to see returns yet, but getting there) but I also suspect it was nutritious of me to develop discernment about what projects are valuable or not valuable for various threat models and theories of change (in such a way that being a phd student off of lesswrong wouldn't have been as good in crucial ways, tho way better in other ways).
  • but I think the point is you have to turn off this discernment sometimes, unless you want to specialize in telling people why their plans won't work, which I'm more dubious on the value of than I used to be

Idk maybe this shortform is most of the value of the top level post

A trans woman told me

I get to have all these talkative blowhard traits and no one will punish me for it cuz I'm a girl. This is one major reason detrans would make my life worse. Society is so cruel to men, it sucks so much for them

And another trans woman had told me almost the exact same thing a couple months ago.

My take is that roles have upsides and downsides, and that you'll do a bad job if you try to say one role is better or worse than another on net or say that a role is more downside than upside. Also, there are versions of "women talk too much" as a stereotype in many subcultures, but I don't have a good inside view about it.

thoughts on chapter 9 of Craft of Research

Getting the easy things right shows respect for your readers and is the best training for dealing with the hard things.

If they don't believe the evidence, they'll reject the reasons and, with them, your claim.

We saw previously [LW(p) · GW(p)] that claims ought to be supported with reasons, and reasons ought to be based on evidence. Now we will look closer at reasons and evidence.

Reasons must be in a clear, logical order. Atomically, readers need to buy each of your reasons, but compositionally they need to buy your logic. Storyboarding is a useful technique for arranging reasons into a logical order: physical arrangements of index cards, or some DAG-like syntax. Here, you can list evidence you have for each reason or, if you're speculating, list the kind of evidence you would need.

When storyboarding, you want to read out the top level reasons as a composite entity without looking at the details (evidence), because you want to make sure the high-level logic makes sense.

Readers will not accept a reason until they see it anchored in what they consider to be a bedrock of established fact. ... To count as evidence, a statement must report something that readers agree not to question, at least for the purposes of the argument. But if they do question it, what you think is hard factual evidence is for them only a reason, and you have not yet reached that bedrock of evidence on which your argument must rest.

I think there is a contract between you and the reader. You must agree to cite sources that are plausibly truthful, and your reader must agree to accept that these sources are reliable. A diligent and well-meaning reader can always second-guess whether, for instance, the beureau of subject matter statistics is collecting and reporting data correctly, but at a certain point this violates the social contract. If they're genuinely curious or concerned, it may fall on them to investigate the source, not on you. The bar you need to meet is that your sources are plausibly trustworthy. The book doesn't talk much about this contract, so there's little I can say about what "plausible" means.

Sometimes you have to be extra careful to distinguish reasons from evidence, a (<claim>, <reason>, <evidence>) tuple is subject to regress in the latter two components, (A, B, C) may need to be justified by (B, C, D) and so on. The example given of this regress is if I told you (american higher education must curb escalating tuition costs, because the price of college is becoming an impediment to the american dream, today a majority of students leave college with a crushing debt burden). In the context of this sentence, "a majority of students..." is evidence, but it would be reasonable to ask for more specifics. In principle, any time information is compressed it may be reasonable to ask for more specifics. A new tuple might look like (the price of college is becoming an impediment to the american dream, because today a majority of students leave college with a crushing debt burden, in 2013 nearly 70% of students borrowed money for college with loans averaging $30000...). The third component is still compressing information, but it's not in the contract between you and the reader for the reader to demand the raw spreadsheet, so this second tuple might be a reasonable stopping point of the regress.

If you can imagine readers plausibly asking, not once but many times, how do you know that? What facts make it true?, you have not yet reached what readers want - a bedrock of uncontested evidence.

Sometimes you have to be careful to distinguish evidence from reports of it. Again, because we are necessarily dealing with compressed information, we can't often point directly to evidence. Even a spreadsheet, rather than summary statistics of it, is a compression of the phenomena in base reality that it tracks.

data you take from a source have invariably been shaped by that source, not to misrepresent them, but to put them in a form that serves that source's ends. ... when you in turn report those data as your own evidence, you cannot avoid manipulating them once again, at least by putting them in a new context.

There is a criteria you want to screen your evidence with respect to.

• sufficient
• representative
• accurate
• precise
• authoritative

Being honest about the reliability and prospective accuracy of evidence is always a positive signal. Evidence can be either too precise or not precise enough. The women in one or two of Shakespeare's plays do not represent all his women, they are not representative. Figure out what sorts of authority signals are considered credible in your community, and seek to emulate them.

Sources - notes on Craft of Research chapters 5 and 6

Primary, secondary, and tertiary sources

Primary sources provide you with the "raw data" or evidence you will use to develop, test, and ultimately justify your hypothesis or claim. Secondary sources are books, articles, or reports that are based on primary sources and are intended for scholarly or professional audiences. Tertiary sources are books and articles that synthesize and report on secondary sources for general readers, such as textbooks, articles in encyclopedias, and articles in mass-circulation publications.

The distinction between primary and secondary sources comes from 19th century historians, and the idea of tertiary sources came later. The boundaries can be fuzzy, and are certainly dependent on the task at hand.

I want to reason about what these distinctions look like in the alignment community, and whether or not they're important.

The rest of chapter five is about how to use libraries and information technologies, and evaluating sources for relevance and reliability.

Chapter 6 starts off with the kind of thing you should be looking for while you read

Look for creative agreement

• Offer additional support. You can offer new evidence to support a source's claim.
• Confirm unsupported claims. You can prove something that a source only assumes or speculates about.
• Apply a claim more widely. You can extend a position.

Look for creative disagreement

• Contradictions of kind. A source says something is one kind of thing, but it's another.
• Part-whole contradictions. You can show that a source mistakes how the parts of something are related.
• Developmental or historical contradictions. You can show that a source mistakes the origin or development of a topic.
• External cause-effect contradictions. You can show that a source mistakes a causal relationship.
• Contradictions of perspective. Most contradictions don't change a conceptual framework, but when you contradict a "standard" view of things, you urge others to think in a new way.

The rest of chapter 6 is a few more notes about what you're looking for while reading (evidence, reasons), how to take notes, and how to stay organized while doing this.

The alignment community

I think I see the creative agreement modes and the creative disagreement modes floating around in posts. Would it be more helpful if writers decided on one or two of these modes before sitting down to write?

Moreover, what is a primary source in the alignment community? Surely if one is writing about inner alignment, a primary source is the Risks from Learned Optimization paper. But what are Risks' primary, secondary, tertiary sources? Does it matter?

Now look at Arbital. Arbital started off to be a tertiary source, but articles that seemed more like primary sources started appearing there. I remember distinctively thinking "what's up with that?" it struck me as awkward for Arbital to change it's identity like that, but I end up thinking about and citing the articles that seem more like primary sources.

There's also the problem of stuff in the memeplex not written down is the real "primary" source while the first person who happens to write it down looks like they're writing a primary source when in fact what they're doing is really more like writing a secondary or even tertiary source.

Yesterday I quit my job for direct work on epistemic public goods! Day one of direct work trial offer is April 4th, and it'll take 6 weeks after that to know if I'm a fulltime hire.

I'm turning down

• raise to 200k/yr usd
• building lots of skills and career capital that would give me immense job security in worlds where investment into one particular blockchain doesn't go entirely to zero
• having fun on the technical challenges

for

• confluence of my skillset and a theory of change that could pay huge dividends in the epistemic public goods space
• 0.35x paycut from my upcoming raise
• uncertainty of it being a trial offer.
• having fun on the technical challenges

Which I'm flagging in such detail to give you strength if you're ever reasoning about your risk tolerance and your goals, just remember, "look at what quinn did!"

did anyone draw up an estimate of how much the proportion of code written by LLMs will increase? or even what the proportion is today

How are people mistreated by bellcurves?

I think this is a crucial part of a lot of psychological maladaption and social dysfunction, very salient to EAs. If you're way more trait xyz than anyone you know for most of your life, your behavior and mindset will be massively effected, and depending on when in life / how much inertia you've accumulated by the time you end up in a different room where suddenly you're average on xyz, you might lose out on a ton of opportunities for growth.

In other words, the concept of "big fish small pond" is deeply insightful and probably underrated.

Some IQ-adjacent idea is sorta the most salient to me, since my brother recently reminded me "quinn is the smartest person I know", to which I was like, you should meet smarter people? Or I kinda did feel unusually smart before I was an EA, I can only reasonably claim to be average if you condition on EA or something similar. But this post is extremely important in terms of each of the Big 5, "grit"-adjacent things, etc.

For example, when you're way more trait xyz than anyone around you, you form habits around adjusting for people to underperform relative to you at trait xyz. Sometimes those habits run very deep in your behavior and wordview, and sometimes they can be super ill-tuned (or at least a bit suboptimal) to becoming average. Plus, you develop a lot of "I have to pave my own way" assumptions about growth and leadership. Related to growth, you may cultivate lower standards for yourself than you otherwise might have. Related to leadership, I expect many people in leader roles at small ponds would be more productive, impactful, and happy if they had access to averageness. Pond size means they don't get that luxury!

There's a tightly related topic about failure to abolish meatspace / how you might think the internet corrects for this but later realize how much it doesn't.

nonprosaic ai will not be on short timelines

I think a property of my theory of change is that academic and commercial speed is a bottleneck. I recently realized that my mass assignment for timelines synchronized with my mass assignment for the prosaic/nonprosaic axis. The basic idea is that let's say a radical new paper that blows up and supplants the entire optimization literature gets pushed to the arxiv tomorrow, signaling the start of some paradigm that we would call nonprosaic. The lag time for academics and industry to figure out what's going on, figure out how to build on that result, for developer ecosystems to form, would all compound to take us outside of what we would call "short timelines".

How flawed is this reasoning?

Cope isn't a very useful concept.

For every person who has a bad reason that they catch because you say "sounds like cope", there are 10x as many people who find their reason actually compelling. Saying "if that was my reason it would be a sign I was in denial of how hard I was coping" or "I don't think that reason is compelling" isn't really relevant to the person you're ostensibly talking to, who's trying to make the best decisions for the best reasons. Just say you don't understand why the reason is compelling.

Excellence and adequacy

I asked a friend whether I should TA for a codeschool called ${{codeschool}}.

You shouldn't hang around ${{codeschool}}. People at ${{codeschool}} are not pursuing excellence.

A hidden claim there that I would soak up the pursuit of non-excellence by proximity or osmosis isn't what's interesting (though I could see that turning out either way). What's interesting is the value of non-excellence, which I'll call adequacy.

${{codeschool}} in this case is effective and impactful at putting butts in seats at companies, and is thereby responsible for some negligible slice of economic growth. It's students and instructors are plentiful with the virtue of getting things done, do they really need the virtue of high-craftsmanship? The student who reads SICP and TAPL because they're pursuing mastery over the very nature of computation is strictly less valuable to the economy than the student who reads react tutorials because they're pursuing some cash.

Obviously, my friend who was telling me this was of the SICP/TAPL type. In software, this is problematic: lisp and type theory will increase your thinking about the nature of computation, but will it increase your thinking about the social problem of steering a team? From an employer's perspective, it is naive to prefer excellence over adequacy, it is much wiser to saddle the excellent person with the burden of proving that they won't get bored easily.

Clever kids in Ravenclaw, evil kids in Slytherin, wannabe heroes in Gryffindor, and everyone who does the actual work in Hufflepuff.

Hufflepuffs can go far, and the fuel is adequacy. Enough competence to get it done, any more is egotistical, a sunk cost.

But what if it's not about industry/markets, what if it's about the world's biggest problems? Don't we want people who are more competent than strictly necessary to be working on them? Maybe, maybe not.

Related: explore/exploit, become great/become useful [EA(p) · GW(p)]

For a long time I've operated in the excellence mindset: more energy for struggling with textbooks than for exploiting the skills I already have to ship projects and participate in the real world. Thinking it might be good to shift gears and flex my hufflepuff virtues more.

I used to think "community builder" was a personality trait I couldn't switch off, but once I moved to the bay I realized that I was just desperate for serendipity and knew how to take it from 0% to 1%. Since the bay is constantly humming at 70-90% serendipity, I simply lost the urge to contribute.

Benefactors are so over / beneficiaries are so back / etc.

Let FairBot be the player that sends an opponent to Cooperate (C) if it is provable that they cooperate with FairBot, and sends them to Defect (D) otherwise.

Let FairBot_k be the player that searches for proofs of length <= k that it's input cooperates with FairBot_k, and cooperates if it finds one, returning defect if all the proofs of length <= k are exhausted without one being valid.

Critch writes that "100%" of the time, mathematicians and computer scientists report believing that FairBot_k(FairBot_k) = D, owing to the basic vision of a stack overflow exceeding the value k (spoiler in the footnote^[1] for how it actually shakes out, in what is now a traditional result in open source game theory).

I am one of these people who believe that FairBot_k(FairBot_k) = D, because I don't understand Löb, nor do I understand parametric Löb. But I was talking about this on two separate occasions with friends Ben and Stephen, both of whom made the same remark, a remark which I have not seen discussed.

The solution set of an equation approach.

One shorter way of writing FairBot is this

$$FB:=a\mapsto a\left(FB\right)$$

because when $a$ lands in $\{C,D\}$, $\text{if}a\left(FB\right)=C\text{then}C\text{else}D$ collapses to $a\left(FB\right)$.

Here, I'm being sloppy about evaluation vs. provability. I'm taking what was originally "$a\left(FB\right)$ is provable" and replacing it with "$a$ is evaluable at $FB$", and assuming decidability so that I can reflect into bool for testing in an $\text{if}$. Then I'm actually performing the evaluation.

Stepping back, if you can write down

$$E:FB\left(a\right)=a\left(FB\right)$$

and you know that $a$ and $FB$ share a codomain (the moves of the game, in this case $\{C,D\}$), then the solution set of this equation $SS\left(E\right)=\text{cod}a=\text{cod}FB$. In other words, the equation is consistent at $a\left(FB\right)=C=FB\left(a\right)$ and consistent at $a\left(FB\right)=D=FB\left(a\right)$, and there may not be a principled way of choosing one particular item of $SS\left(E\right)$ in general. In other words, the proofs of the type $FB\left(a\right)=a\left(FB\right)$ are not unique.

What the heck is the type-driven story?

I'm guessing there's some solution to this problem in MIRI's old haskell repo, but I haven't been able to find it reading the code yet.

I can't think of a typey way to justify $A:=A\to \{C,D\}:Type$. It's simply nonsensical, or I'm missing something about a curry-howard correspondence with arithmoquining. In other words, agents like FairBot that take other agents as input and return moves are a lispy/pythonic warcrime, in terms of type-driven ideology.

Questions

• Am I confused because of a subtlety distinguishing evaluability and provability?
• Am I confused because of some nuance about how recursion really works?

I find myself, just as a random guy, deeply impressed at the operational competence of airports and hospitals. Any good books about that sort of thing?

Rats and EAs should help with the sanity levels in other communities

Consider politics. You should take your political preferences/aesthetics, go to the tribes that are based on them, and help them be more sane. In the politics example, everyone's favorite tribe has failure modes, and it is sort of the responsibility of the clearest-headed members of that tribe to make sure that those failure modes don't become the dominant force of that tribe.

Speaking for myself, having been deeply in an activist tribe before I was a rat/EA, I regret I wasn't there to help the value-aligned and clear-headed over the last few years while some of that tribe's worst pathologies made gains. Now it seems almost too late for them.

Actionably, I want you to

• Write for journals, forums, blogospheres, zines outside of rat and EA.
• Dump time into tribes that might not be the state of the art in sanity, find the most sane people there, and find ways to support them.

I speak not (well, not entirely) from my cognitive dissonance at having abandoned an aesthetic I still have feelings for. I think

• Tribes besides ours are what make up the overall sanity waterline
• It's ok to set aside humility and imposter syndrome and say "I can actionably be a resource of sanity for someone else", even tho you personally think you have a lot of work to do at getting less wrong yourself. I would say the opposite of the "affix your mask before helping others" comic strip: find synergies between mentoring others in the art and continuing to master the art yourself.
• We basically want every tribe to believe true things and think clearly about their values. Yes, I'm obviously concerned that this will lead to some of my fellow rats taking my advice, applying it to a political aesthetic I find barbaric, and helping that political aesthetic win-- I think this concern is basically fine because on net I expect more true beliefs and clear thinking about values to make the meaning of winning for each tribe converge on something that isn't zero-sum.

I should also mention that I expect an externality from this effort to be an increase in the intrarat / intraEA intellectual diversity.

Claims - thoughts on chapter eight of Craft of Research

Broadly, the two kinds of claims are conceptual and practical.

Conceptual claims ask readers not to ask, but to understand. The flavors of conceptual claim are as follows:

• Claims of fact or existence
• Claims of definition and classification
• Claims of cause and consequence
• Claims of evaluation or appraisal

There's essentially one flavor of practical claim

• Claims of action or policy.

If you read between the lines, you might notice that a kind of claim of fact or cause/consequence is that a policy works or doesn't work to bring about some end. In this case, we see that practical claims deal in ought or should. There is a difference, perhaps subtle perhaps not, between "X brings about Y" and "to get Y we ought to X".

Readers expect a claim to be specific and significant. You can evaluate your claim along these two axes.

To make a claim specific, you can use precise language and explicit logic. Usually, precision comes at the cost of a higher word count. To gain explicitness, use words like "although" and "because". Note some fields might differ in norms.

You can think of significance of a claim as the quantity it asks readers to change their mind, or I suppose even behavior.

While we can't quantify significance, we can roughly estimate it: if readers accept a claim, how many other beliefs must they change?

Avoid arrogance.

As paradoxical as it seems, you make your argument stronger and more credible by modestly acknowledging its limits.

Two ways of avoiding arrogance are acknowledging limiting conditions and using hedges to limit certainty.

Don't run aground: there are innumerable caveats that you could think of, so it's important to limit yourself only to the most relevant ones or the ones that readers would most plausibly think of. Limiting certainty with hedging is given by example of Watson and Crick, publishing what would become a high-impact result, "We wish to suggest ... in our opinion ... we believe ... Some ... appear"

without the hedges, Crick and Watson would be more concise but more aggressive.

In most fields, readers distrust flatfooted certainty

It is not obvious how to walk the line between hedging too little and hedging too much.

notes (from a very jr researcher) on alignment training pipeline

Training for alignment research is one part competence (at math, cs, philosophy) and another part having an inside view / gears-level model of the actual problem. Competence can be outsourced to universities and independent study, but inside view / gears-level model of the actual problem requires community support.

A background assumption I'm working with is that training as a longtermist is not always synchronized with legible-to-academia training. It might be the case that jr researchers ought to publication-maximize for a period of time even if it's at the expense of their training. This does not mean that training as a longtermist is always or even often orthogonal to legible-to-academia training, it can be highly synchronized, but it depends on the occasion.

It's common to query what relative ratio should be assigned to competence building (textbooks, exercises) vs. understanding the literature (reading papers and alignment forum), but perhaps there is a third category- honing your threat model and theory of change.

I spoke with a sr researcher recently who roughly said that a threat model with a theory of change is almost sufficient for an inside view / gears-level model. I'm working from the theory that honed threat models and your theory of change are important to calculate interventions. See Alice and Bob in Rohin's faq.

I've been trying [LW · GW] by doing exercises with a group of peers weekly to hone my inside view / gears-level model of the actual problem. But the sr researcher i spoke to said mentorship trees of 1:1 time, not exercises that jrs can just do independently or in groups, is the only way it can happen. This is troublesome to me, as the bottleneck becomes mentors' time. I'm not so much worried about the hopefully merit-based process of mentors figuring out who's worth their time, as I am about the overall throughput. It gets worse though- what if the process is credentialist?

Take a look at the Critch quote from the top of Rohin's faq:

I get a lot of emails from folks with strong math backgrounds (mostly, PhD students in math at top schools) who are looking to transition to working on AI alignment / AI x-risk.

Is he implicitly saying that he offloads some of the filtering work to admissions people at top schools? Presumably people from non-top schools are also emailing him, but he doesn't mention them.

I'd like to see a claim that admissions people at top schools are trustworthy. No one has argued this to my knowledge. I think sometimes the movement falls back on status games, unless there is some intrinsic benefit to "top schools" (besides building social power/capital) that everyone is aware of. (Indeed if someone's argument is that they identified a lever that requires a lot of social power/capital, then they can maybe put that top school on their resume to use, but if the lever is strictly high quality useful research (instead of say steering a federal government) this doesn't seem to apply).

I'm excited for language model interpretability to teach us about the difference between compilers and simulations of compilers. In the sense that chatgpt and I can both predict what a compiler of a suitably popular programming language will do on some input, what's going on there---- surely we're not reimplementing the compiler on our substrate, even in the limit of perfect prediction? Will be an opportunity for a programming language theorist in another year or two of interp progress

Proof cert memo

In the Safeguarded AI programme thesis^[1], proof certificates or certifying algorithms are relied upon in the theory of change. Let's discuss!

From the thesis:

Proof certificates are a quite broad concept, introduced by[33] : a certifying algorithm is defined as one that produces enough metadata about its answer that the answer's correctness can be checked by an algorithm which is so simple that it is easy to understand and to formally verify by hand.

The abstract of citation 33 (McConnell et al)^[2]

A certifying algorithm is an algorithm that produces, with each output, a certificate or witness (easy-to-verify proof) that the particular output has not been compromised by a bug. A user of a certifying algorithm inputs x, receives the output y and the certificate w, and then checks, either manually or by use of a program, that w proves that y is a correct output for input x. In this way, he/she can be sure of the correctness of the output without having to trust the algorithm.

In this memo, I do an overview by distinguishing a proof cert from a constructive proof, and dive shallowly into the paper. Then I ruminate on how this might apply in AI contexts.

What's the difference between a proof cert and a constructive proof?

If a cert is just a witness, you might think that proof certs are just proofs from constructive math as distinct from classical math. For example, under some assumptions and caveats (in the calculus/topology setting) a function can be "known" to have a fixed point without us methodically discovering them with any guarantees, while under other assumptions and caveats (namely the lattice theoretic setting) we know there's a fixed point because we have a guaranteed procedure for computing it. However, they go further: a certifying algorithm produces with each output a cert/witness that the particular output has, in the case of McConnell et al, "not been compromised by a bug". This isn't a conceptual leap from constructive math, in which a proof is literally a witness-building algorithm, it looks to me a bit like a memo saying "btw, don't throw out the witness" along with notes about writing cheap verifiers that do not simply reimplement the input program logic.

One way of looking at a certificate is that it's dependent on metadata. This may simply be something read off of an execution trace, or some state that logs important parts of the execution trace to construct a witness like in the bipartite test example. In the bipartite test example, all you need is for the algorithm that searches for a two-coloring or an odd cycle, and crucially will return either the two-coloring or the odd cycle as a decoration on it's boolean output (where "true" means "is bipartite"). Then, a cheap verifier (or checker) is none other than the pre-existing knowledge that two-colorable and bipartite are equivalent, or conversely that the existence of an odd cycle is equivalent to disproving bipartiteness.

Chapter 11 of ^[3] will in fact discuss certification and verification supporting eachother, which seems to relax the constraint that a cheap verifier doesn't simply reimplement the certifier logic.

The main difference between a standard proof as in an applied type theory (like lean or coq) and a proof cert is kinda cultural and not fundamental, in that proof certs prefer to emphasize single IO pairs and lean/coq proofs often like to emphasize entire input types. Just don't get confused on page 34 when it says "testing on all inputs"-- it means that a good certifying algorithm is a means to generate exhaustive unit tests, not that the witness predicate actually runs on all inputs at once. It seems like the picking out of only input of interest and not quantifying over the whole input type will be the critical consideration when we discuss making this strategy viable for learned components or neural networks.

Formally:

Skip this if you're not super compelled by what you know so far about proof certificates, and jump down to the section on learned programs.

Consider algorithms $\alpha :X\to Y$. A precondition $\varphi :X\to B$ and a postcondition $\psi :X\to Y\to B$ form an IO-spec or IO-behavior. An extension of the output set $Y^{\perp }=Y\cup \left\{\perp \right\}$ is needed to account for when the precondition is violated. We have a type $W$ of witness descriptions. Finally, we describe the witness predicate with $W:X\to Y^{\perp }\to W\to B$ that says IO pair $xy$ is witnessed by $w$. So when $\psi xy$ is true, $w$ must be a witness to the truth, else a witness to the falsehood. McConnell et al distinguish witness predicates from strong witness predicates, where the former can prove $\neg \varphi x\vee \psi xy$ but the latter knows which disjunct.

A checker for $W$ is an algorithm sending $xyw\mapsto Wxyw$. It's desiderata / nice to haves are correctness, runtime linear in input, and simplicity.

I'm not that confident this isn't covered in the document somewhere, but McConnell et al don't seem paranoid about false functions. If Alice (sketchy) claims that a $y$ came from $\alpha$ on $x$, but she lied to Bob and in fact ran ${\alpha }^{\prime }$, there's no requirement for witness predicates to have any means of finding out. (I have a loose intuition that the proper adversarial adaptation of proof certs would be possible on a restricted algorithm set, but impose a lot of costs). Notice that $W$ is with respect to a fixed $x$, $W^{\prime }:\forall (x:X),Y\left(X\right)\to W\left(X\right)\to B$ would be a different approach! We should highlight that a witness predicate only tells you about one input.

Potential for certificates about learning, learned artefacts, and learned artefacts' artefacts

The programme thesis continues:

We are very interested in certificates, because we would like to rely on black-box advanced AI systems to do the hard work of searching for proofs of our desired properties, yet without compromising confidence that the proofs are correct. In this programme, we are specifically interested in certificates of behavioural properties of cyber-physical systems (ranging from simple deterministic functions, to complex stochastic hybrid systems incorporating nuances like nondeterminism and partiality).

If AIs are developing critical software (like airplanes, cars, and weaponry) and assuring us that it's legit, introducing proof certs removes trust from the system. The sense in which I think Safeguarded AI primarily wants to use proof certs certainly doesn't emphasize certs of SGD or architectures, nor does it even emphasize inference-time certs (though I think it'd separately be excited about progress there). Instead, an artefact that is second order removed from the training loop seems to be the target of proof certs, like a software artefact written by a model but it's runtime is not the inference time of the model. I see proof certs in this context as primarily an auditing/interpretability tool. There's no reason a model should write code that is human readable, constraining it to write human readable code seems hard, letting the code be uninterpretable but forcing it to generate interpretable artefacts seems pretty good! The part I'm mostly not clear on is why proof certs would be better than anything else in applied type theory (where proofs are just lambdas, which are very decomposable- you can't read them all at once but they're easy to pick apart) or model checking (specs are logical formulae, even if reading the proof that an implementation obeys a spec is a little hairy).

It's important that we're not required to quantify over a whole type to get some really valuable assurances, and this proof cert framework is amenable to that. On the other hand, you can accomplish the same relaxation in lean or coq, and we can really only debate over ergonomics. I think ultimately, the merits of this approach vs other approaches are going to be decided entirely on the relative ergonomics of tools that barely exist yet.

Overall impression

McConnell et al is 90 pages, and I only spent a few hours with it. But I don't find it super impressive or compelling. Conceptually, a constructive proof gets me everything I want out of proof certificates. A tremendous value proposition would be to make these certs easier to obtain for real world programs than what coq or lean could provide, but I haven't seen those codebases yet. There's more literature I may look at around proof-carrying code (PCC)^[4] which is similar ideas, but I'm skeptical that I'll be terribly compelled since the insight "just decorate the code with the proof" isn't very subtle or difficult.

Moreover, this literature just seems kinda old I don't see obvious paths from it to current research.

any interest in a REMIX study group? in meatspace in berkeley, a few hours a week. https://github.com/redwoodresearch/remix_public/

Any tips for getting out of a "rise to the occasion mindset" and into a "sink to your training" mindset?

I'm usually optimizing for getting the most out of my A-game bursts. I want to start optimizing for my baseline habits, instead. I should cover the B-, C-, and Z-game; the A-game will cover itself.

Mathaphorically, "rising to the occasion" is taking a max of a max, whereas "sinking to the level of your habits" looks like a greatest lower bound.

Yall, this is a rant. It will be sloppy.

I'm really tired of high functioning super smart "autism" like ok we all have madeup diagnoses--- anyone with a IQ slightly above 90 knows that they can learn the slogans to manipulate gatekeepers to get performance enhancement, and they decide not to if they think theyre performing well enough already. That doesn't mean "ADHD" describes something in the world. Similarly, there's this drift of "autism" getting more and more popular. It's obnoxious because labels and identities are obnoxious, but i only find it repulsive because of the general trend of articulate and charismatic minorities setting agendas that effect the less talkative (and worse off!) fellow minorities https://open.substack.com/pub/jessesingal/p/why-disability-advocates-are-trying?utm_source=share&utm_medium=android&r=5hj2m (I only read up to free tier, but I've seen a bunch of this stuff).

Maybe neuroscientists or psychologists have good reasons for this, but "autism" is the most immensely deranged word in the history of categories--- what utility is a word that crosses absent minded professors and people who can't conceptually distinguish a week from a month insofar as you can wordlessly elicit conceptual understanding from them???? If you worked at the dictionary factory and you tried to slip that word in, you'd be fired immediately. So why do psychologists or neuroscientists get away with this???

I'm a computer guy. I'm bad at social queues sometimes. I feel at home in lots of sperg culture. But leave me out of your stolen valor campaign. We're fine, you guys--- we're smart enough to correct for most of the downsides.

https://www.thefp.com/p/the-autism-surge-lies-conspiracies

For the record, to mods: I waited till after petrov day to answer the poll because my first guess upon receiving a message on petrov day asking me to click something is that I'm being socially engineered. Clicking the next day felt pretty safe.

"EV is measure times value" is a sufficiently load-bearing part of my worldview that if measure and value were correlated or at least one was a function of the other I would be very distressed.

Like in a sense, is John [LW · GW] threatening to second-guess hundreds of years of consensus on is-ought?

messy, jotting down notes:

• I saw this thread https://twitter.com/alexschbrt/status/1666114027305725953 which my housemate had been warning me about for years.
• failure mode can be understood as trying to aristotle the problem, lack of experimentation
• thinking about the nanotech ASI threat model, where it solves nanotech overnight and deploys adversarial proteins in all the bloodstreams of all the lifeforms.
• These are sometimes justified by Drexler's inside view of boundary conditions and physical limits.
• But to dodge the aristotle problem, there would have to be an amount of bandwidth of what's passing between sensors and actuators (which may roughly correspond to the number of do applications in pearl)
• Can you use something like communication complexity https://en.wikipedia.org/wiki/Communication_complexity (between a system and an environment) to think about "lower bound on the number of sensor-actuator actions" mixed with sample complexity (statistical learning theory)
• Like ok if you're simulating all of physics you can aristotle nanotech, for a sufficient definition of "all" that you would run up against realizability problems and cost way more than you actually need to spend.

Like I'm thinking if there's a kind of complexity theory of pearl (number of do applications needed to acquire some kind of "loss"), then you could direct that at something like "nanotech projects" to fermstimate the way AIs might tradeoff between applying aristotlean effort (observation and induction with no experiment) and spending sensor-actuator interactions (with the world).

There's a scenario in the sequences if I recall correctly about which physics an AI infers from 3 frames of a video of an apple falling, and something about how security mindset suggests you shouldn't expect your information-theoretic calculation that einsteinian physics is impossible to believe from the three frames to actually apply to the AI. Which is a super dumbed down way of opening up this sort of problem space.

Methods, famously, includes the line "I am a descendant of the line of Bacon", tracing empiricism to either Roger (13th century) or Francis (16th century) (unclear which).

Though a cursory wikiing shows an 11th century figure providing precedents for empiricism! Alhazen or Ibn al-Haytham worked mostly optics apparently but had some meta-level writings about the scientific method itself. I found this shockingly excellent quote

The duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and ... attack it from every side. He should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.

Should we do more to celebrate Alhazen as an early rationalist?

New discord server dedicated to multi-multi delegation research

DM me for invite if you're at all interested in multipolar scenarios, cooperative AI, ARCHES, social applications & governance, computational social choice, heterogeneous takeoff, etc.

(side note I'm also working on figuring out what unipolar worlds and/or homogeneous takeoff worlds imply for MMD research).

Questions and Problems - thoughts on chapter 4 of Craft of Doing Research

Last time [LW(p) · GW(p)] we discussed the difference between information and a question or a problem, and I suggested that the novelty-satisfied mode of information presentation isn't as good as addressing actual questions or problems. In chapter 3 which I have not typed up thoughts about, A three step procedure is introduced

1. Topic: "I am studying ..."
2. Question: "... because I want to find out what/why/how ..."
3. Significance: "... to help my reader understand ..." As we elaborate on the different kinds of problems, we will vary this framework and launch exercises from it.

Some questions raise problems, others do not. A question raises a problem if not answering it keeps us from knowing something more important than its answer.

The basic feedback loop introduced in this chapter relates practical with conceptual problems and relates research questions with research answers.

Practical problem -> motivates -> research question -> defines -> conceptual/research problem -> leads to -> research answer -> helps to solve -> practical problem (loop)

What should we do vs. what do we know - practical vs conceptual problems

Opposite eachother in the loop are practical problems and conceptual problems. Practical problems are simply those which imply uncertainty over decisions or actions, while conceptual problems are those which only imply uncertainty over understanding. Concretely, your bike chain breaking is a practical problem because you don't know where to get it fixed, implying that the research task of finding bike shops will reduce your uncertainty about how to fix the bike chain.

Conditions and consequences

The structure of a problem is that it has a condition (or situation) and the (undesirable) consequences of that condition. The consequences-costs model of problems holds both for practical problems and conceptual problems, but comes in slightly different flavors. In the practical problem case, the condition and costs are immediate and observed. However, a chain of "so what?" must be walked.

Readers judge the significance of your problem not by the cost you pay but by the cost they pay if you don't solve it... To make your problem their problem, you must frame it from their point of view, so that they see its cost to them.

One person's cost may be another person's condition, so when stating the cost you ought to imagine a socratic "so what?" voice, forcing you to articulate more immediate costs until the socratic voice has to really reach in order to say that it's not a real cost.

The conceptual problem case is where intangibles play in. The condition in that case is always the simple lack of knowledge or understanding of something. The cost in that case is simple ignorance.

Modus tollens

A helpful exercise is if you find yourself saying "we want to understand x so that we can y", try flipping to "we can't y if we don't understand x". This sort of shifts the burden on the reader to provide ways in which we can y without understanding x. You can do this iteratively: come up with _z_s which you can't do without y, and so on.

Pure vs. applied research

Research is pure when the significance stage of the topic-question-significance frame refers only to knowing, not to doing. Research is applied when the significance step refers to doing. Notice that the question step, even in applied research, refers to knowing or understanding.

Connecting research to practical consequences

You might find that the significance stage is stretching a bit to relate the conceptual understanding gained from the question stage. Sometimes you can modify and add a fourth step to the topic-question-significance frame and make it into topic-conceptual question-conceptual significance-possible practical application. Splitting significance into two helps you draw reasonable, plausible applications. A claimed application is a stretch when it is not plausible. Note: the authors suggest that there is a class of conceptual papers in which you want to save practical implications entirely for the conclusion, that for a certain kind of paper practical applications do not belong in the introduction.

AI safety

One characterisitic of AI safety that makes it difficult both to do and interface with is the chains of "so what" are often very long. The path from deconfusion research to everyone dying or not dying feels like a stretch if not done carefully, and has a lot of steps when done carefully. As I mentioned in my last post, it's easy to get sucked into the "novel information for it's own sake" regime at least as a reader. More practical oriented approaches are perhaps those that seek new regimes for how to even train models, and the "so what?" is answered "so we have dramatically less OODR-failures" or something. The condition-costs framework seems really beneficial for articulating alignment agendas and directions.

Misc

• "Researchers often begin a project without a clear idea of what the problem even is."
• Look for problems as you read. When you see contradictions, inconsistencies, incomplete explanations tentatively assume that readers would or should feel the same.
• Ask not "Can I solve it?" but "will my readers think it ought to be solved?"
• "Try to formulate a question you think is worth answering, so that down the road, you'll know how to find a problem others think is worth solving."

what's the best essay on asking for advice?

Going over etiquette and the social contract, perhaps if it's software specific it talks about minimal reproducers, whatever else the author thinks is involved.

Guaranteed Safe AI paper club meets again this thursday

Event for the paper club: https://calendar.app.google/2a11YNXUFwzHbT3TA

blurb about the paper in last month's newsletter:

... If you’re wondering why you just read all that, here’s the juice: often in GSAI position papers there’ll be some reference to expectations that capture “harm” or “safety”. Preexpectations and postexpectations with respect to particular pairs of programs could be a great way to cash this out, cuz we could look at programs as interventions and simulate RCTs (labeling one program control and one treatment) in our world modeling stack. When it comes to harm and safety, Prop and bool are definitely not rich enough.

Yoshua Bengio is giving a talk online tomorrow https://lu.ma/4ylbvs75

GSAI paper club is tomorrow (gcal ticket), summary (by me) and discussion of this paper

i'm getting back into composing and arranging. send me rat poems to set to music!

Does anyone use vim / mouse-minimal browser? I like Tridactyl better than the other one I tried, but it's not great when there's a vim mode in a browser window everything starts to step on eachother (like in jupyter, colab, leetcode, codesignal)

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I'm halfway through how to measure anything: cybersecurity, which doesn't have a lot of specifics to cybersecurity and mostly reviews the first book. I never finished the first one, and it was about four years ago that I read the parts that I did.

I think for top of the funnel EA recruiting it remains the best and most underrated book. Basically anyone worried about any kind of problem will do better if they read it, and most people in memetically adaptive / commonsensical activist or philanthropic mindsets probably aren't measuring enough.

However, the material is incredibly basic for someone who's been hanging out with EAs or on LessWrong for even a little bit. You've already absorbed so much of it from the water supply.

preorders as the barest vocabulary for emergence

We can say "a monotonic map, $\Phi \in mono\left(Q^P\right)$ is a phenomenon of $P$ as observed by $Q$", then, emergence is simply the impreservation of joins.

Given preorders $(P,{\le }_P)$ and $(Q,{\le }_Q)$, we say a map in $mono\left(Q^P\right)$ "preserves" joins (which, recall, are least upper bounds) iff $\forall ab\in P,\Phi a{\vee }_Q\Phi b=\Phi \left(a{\vee }_{P}b\right)$ where by "$x=y$" we mean $x\le y\wedge y\le x$.

Suppose $\Phi$ is a measurement taken from a particle. We would like for our measurement system to be robust against emergence, which is literally operationalized by measuring one particle, measuring another, then doing some operation on the two results and getting the exact same thing as you would have gotten if you smashed the particles together somehow before taking the (now, single) measurement. But we don't always get what we want.

Indeed, for arbitrary preorders and monotone arrows, you can prove $\Phi a{\vee }_Q\Phi b{\le }_Q\Phi \left(a{\vee }_{P}b\right)$, which we interpret as saying "smashing things together before measuring gives you more information than measuring two things then somehow combining them".

In the sequences community, emergence is a post-it note that says "you're confused or uncertain, come back here to finish working later" (Eliezer, 2008 or whatever). In the applied category theory community, emergence is also a failure of understanding but the antidote, namely reductions to composition, is prescribed.

This is all in chapter 1 of seven sketches on compositionality by fong and spivak, citing a thesis by someone called adam.

Jotted down some notes about the law of mad science on the EA Forum. Looks like some pretty interesting open problems in the global priorities, xrisk strategy space. https://forum.effectivealtruism.org/posts/r5GbSZ7dcb6nbuWch/quinn-s-shortform?commentId=DqSh6ifdXpwHgXnCG [EA(p) · GW(p)]

Ambition, romance, kids

Two premises of mine are that I'm more ambitious than nearly everyone I meet in meatspace and normal distributions. This implies that in any relationship, I should expect to be the more ambitious one.

I do aspire to be a nagging voice increasing the ambitions of all my friends. I literally break the ice with acquaintances by asking "how's your master plan going?" because I try to create vibes like we're having coffee in the hallway of a supervillain conference, and I like to also ask "what harder project is your current project a warmup for?".

I'm mostly sure I want kids. I told a gf recently (who does not want kids) that if it seemed like someone would be a good coparent, but they made me less ambitious, I would accept the bargain. But what's implicit premise here?

The premise is of course that in relationships, you drift toward the average of yourself and the other person. Is this plausibly true?

I think there's a folk wisdom about friendships, which generalizes to romance, that you're a weighted average of your influences, so you should exercise caution in picking your influences.

Also--- autonomy to leave a deadend job and go to EA Hotel was an important part of my ability to cultivate ambition. What price should I put on giving up that autonomy?

However, according to Owain's comment here [LW(p) · GW(p)], there's not a super good reason to expect children to decrease ambition. But it's complicated-- that dataset doesn't express parenting quality.

One comment you could make is "move to the bay and you'll no longer be the most ambitious person you run into in meatspace". I'm empirically not someone who needs to be surrounded by like minds in order to thrive, but plausibly like minds could still amplify me. (Separately, I think it's important for everyone who can afford to not live in the bay to avoid living in the bay, because brain drain and complete absence of cool projects in non-bay cities seem really bad! But I understand that some people simply can't be ambitious if they're not getting social rewards for it)

I guess I wonder how best to cultivate ass-kicking, through the kind of automatic cultivation and habituation that comes built in to relationships.

I think 15-20% decrease in ambition is a reasonable price to pay for being a parent. I don't know if that price is really exacted.

Positive and negative longtermism

I'm not aware of a literature or a dialogue on what I think is a very crucial divide in longtermism.

In this shortform, I'm going to take a polarity approach. I'm going to bring each pole to it's extreme, probably each beyond positions that are actually held, because I think median longtermism or the longtermism described in the Precipice is a kind of average of the two.

Negative longtermism is saying "let's not let some bad stuff happen", namely extinction. It wants to preserve. If nothing gets better for the poor or the animals or the astronauts, but we dodge extinction and revolution-erasing subextinction events, that's a win for negative longtermism.

In positive longtermism, such a scenario is considered a loss. From an opportunity cost perspective, the failure to erase suffering or bring to agency and prosperity to 1e1000 comets and planets hurts literally as bad as extinction.

Negative longtermism is a vision of what shouldn't happen. Positive longtermism is a vision of what should happen.

My model of Ord says we should lean at least 75% toward positive longtermism, but I don't think he's an extremist. I'm uncertain if my model of Ord would even subscribe to the formation of this positive and negative axis.

What does this axis mean? I wrote a little about this earlier this year [LW · GW]. I think figuring out what projects you're working on and who you're teaming up with strongly depends on how you feel about negative vs. positive longtermism. The two dispositions toward myopic coalitions are "do" and "don't". I won't attempt to claim which disposition is more rational or desirable, but explore each branch

When Alice wants future X and Bob wants future Y, but if they don't defeat the adversary Adam they will be stuck with future 0 (containing great disvalue), Alice and Bob may set aside their differences and choose form a myopic coalition to defeat Adam or not.

• Form myopic coalitions. A trivial case where you would expect Alice and Bob to tend toward this disposition is if X and Y are similar. However, if X and Y are very different, Alice and Bob must each believe that defeating Adam completely hinges on their teamwork in order to tend toward this disposition, unless they're in a high trust situation where they each can credibly signal that they won't try to get a head start on the X vs. Y battle until 0 is completely ruled out.
• Don't form myopic coalitions. A low trust environment where Alice and Bob each fully expect the other to try to get a head start on X vs. Y during the fight against 0 would tend toward the disposition of not forming myopic coalitions. This could lead to great disvalue if a project against Adam can only work via a team of Alice and Bob.

An example of such a low-trust environment is, if you'll excuse political compass jargon, reading bottom-lefts online debating internally the merits of working with top-lefts on projects against capitalism. The argument for coalition is that capitalism is a formiddable foe and they could use as much teamwork as possible; the argument against coalition is historical backstabbing and pogroms when top-lefts take power and betray the bottom-lefts.

For a silly example, consider an insurrection against broccoli. The ice cream faction can coalition with the pizzatarians if they do some sort of value trade that builds trust, like the ice cream faction eating some pizza and the pizzatarians eating some ice cream. Indeed, the viciousness of the fight after broccoli is abolished may have nothing to do with the solidarity between the two groups under broccoli's rule. It may or may not be the case that the ice cream faction and the pizzatarians can come to an agreement about best to increase value in a post-broccoli world. Civil war may follow revolution, or not.

Now, while I don't support long reflection [EA · GW] (TLDR I think a collapse of diversity sufficient to permit a long reflection would be a tremendous failure), I think elements of positive longtermism are crucial for things to improve for the poor or the animals or the astronauts. I think positive longtermism could outperform negative longtermism when it comes to finding synergies between the extinction prevention community and the suffering-focused ethics community. However, I would be very upset if I turned around in a couple years and positive longtermists were, like, the premiere face of longtermism. The reason for this is once you admit positive goals, you have to deal with everybody's political aesthetics, like a philosophy professor's preference for a long reflection or an engineer's preference for moar spaaaace or a conservative's preference for retvrn to pastorality or a liberal's preference for intercultural averaging. A negative goal like "don't kill literally everyone" greatly lacks this problem. Yes, I would change my mind about this if 20% of global defense expenditure was targeted at defending against extinction-level or revolution-erasing events, then the neglectedness calculus would lead us to focus the by comparison smaller EA community on positive longtermism.

The takeaway from this shortform should be that quinn thinks negative longtermism is better for forming projects and teams.

The audience models of research - thoughts on Craft of Doing Research chapter 2

Writers can't avoid creating some role for themselves and their readers, planned or not

Before considering the role you're creating for your reader, consider the role you're creating for yourself. Your broad options are the following

1. I've found some new and interesting information - I have information for you
2. I've found a solution to an important practical problem - I can help you fix a problem
3. I've found an answer to an important question - I can help you understand something better

The authors recommend assuming one of these three. There is of course a wider gap between information and the neighborhood of problems and questions than there is between problems and questions! Later on in chapter four the authors provide a graph illustrating problems and questions: Practical problem -> motivates -> Research question -> defines -> Conceptual/research problem. Information, when provided mostly for novelty, however, is not in this cycle. Information can be leveled at problems or questions, plays a role in providing solutions or answers, but can also be for "its own sake".

I'm reminded of a paper/post I started but never finished, on providing a poset-like structure to capabilities. I thought it would be useful if you could give a precise ordering on a set of agents, to assign supervising/overseeing responsibilities. Looking back, providing this poset would just be a cool piece of information, effectively: I wasn't motivated by a question or problem so much as "look at what we can do". Yes, I can post-hoc think of a question or a problem that the research would address, but that was not my prevailing seed of a reason for starting the project. Is the role of the researcher primarily a writing thing, though, applying mostly to the final draft? Perhaps it's appropriate for early stages of the research to involve multi-role drifting, even if it's better for the reader experience if you settle on one role in the end.

Additionally, it occurs to me that maybe "I have information for you" mode just a cheaper version of the question/problem modes. Sometimes I think of something that might lead to cool new information (either a theory or an experiment), and I'm engaged moreso by the potential for novelty than I am by the potential for applications.

I think I'd like to become more problem-driven. To derive possibilities for research from problems, and make sure I'm not just seeking novelty. At the end of the day, I don't think these roles are "equal" I think the problem-driven role is the best one, the one we should aspire to.

[When you adopt one of these three roles, you must] cast your readers in a complementary role by offering them a social contract: _I'll play my part if you play yours ... if you cast them in a role they won't accept, you're likely to lose them entirely... You must report your research in a way that motivates your readers to play the role you have imagined for them.

The three reader roles complementing the three writer roles are

1. Entertain me
2. Help me solve my practical problem
3. Help me understand something better

It's basically stated that your choice of writer role implies a particular reader role, 1 mapping to 1, 2 mapping to 2, and 3 mapping to 3.

Role 1 speaks to an important difficulty in the x-risk, EA, alignment community; which is how not to get drawn into the phenomenal sensation of insight when something isn't going to help you on a problem. At my local EA meetup I sometimes worry that the impact of our speaker events is low, because the audience may not meaningfully update even though they're intellectually engaged. Put another way, intellectual engagement can be goodhartable, the sensation of insight can distract you from your resolve to shatter your bottlenecks and save the world if it becomes an end itself. Should researchers who want to be careful about this avoid the first role entirely? Should the alignment literature look upon the first reader role as a failure mode? We talk about a lot of cool stuff, it can be easy to be drawn in by the cool factor like some of the non-EA rationalists I've met at meetups.

I'm not saying reader role number two absolutely must dominate, because it can diverge from deconfusion [? · GW] which is better captured by reader role number three.

Division of labor between reader and writer, writer roles do not always imply exactly one reader role

Isn't it the case that deconfusion/writer role three research can be disseminated to practical (as opposed to theoretical) -minded people, and then those people turn question-answer into problem-solution? You can write in the question-answer regime, but there may be that (rare) reader who interprets it in the problem-solution regime! This seems to be an extremely good thing that we should find a way to encourage. In general reading the drifts across multiple roles seems like the most engaged kind of reading.

He had become so caught up in building sentences that he had almost forgotten the barbaric days when thinking was like a splash of color landing on a page.

Edward St Aubyn

a $B$-valued quantifier is any function $(A\to B)\to B$, so when $B$ is bool quantifiers are the functions that take predicates as input and return bool as output (same for prop). the standard max and min functions on arrays count as real-valued quantifiers for some index set $A$.

I thought I had seen $\forall$ as the max of the Prop-valued quantifiers, and exists as the min somewhere, which has a nice mindfeel since forall has this "big" feeling (if you determined for $P:A\to Prop$ that $\forall P$ (of which $\forall x:A,Px$ is just syntax sugar since the variable name $x$ is irrelevant) by exhaustive checking, it would cost $O\left(|A|\right)$ whereas $\exists P$ would cost $O\left(1\right)$ unless the derivation of the witness was dependent on size of domain somehow).

Incidentally however, in differentiable logic it seems forall is the "minimal expectation" and existential is the "maximal expectation". Page 10 of the LDL paper, where a $E_{min}\left(g\right(X\left)\right)$ is the limit as gamma goes to zero of ${\int }_{x\in B^{\gamma }\left(ming\right)}p\left(x\right)g\left(x\right)dx$, or the integral with respect to a $\gamma$-ball about the min of $g$ rather than about the entire domain of $g$. os in this sense, the interpretation of a universally quantified prop is a minimal expectation, dual where existentially quantified prop is a maximal expectation.

I didn't like the way this felt aesthetically, since as I said, forall feels "big" which mood-affiliates toward a max. But that's notes from barely-remembered category theory I saw once. Anyway, I asked a language model and it said that forall is minimal because it imposes the strictest of "most conservative" requirement. so "max" in the sense of "exists is interpreted to maximal expectation" refers to maximal freedom.

I suppose this is fine.

claude and chatgpt are pretty good at ingesting textbooks and papers and making org-drill cards.

here's my system prompt https://chat.openai.com/g/g-rgeaNP1lO-org-drill-card-creator though i usually tune it a little further per session.

Here are takes on the idea from the anki ecosystem

• https://ankiweb.net/shared/info/1915225457
• https://ankigpt.help/

I tried a little ankigpt and it was fine, i haven't tried the direct plugin from ankiweb. I'm opting for org-drill here cuz I really like plaintext.

consider how our nonconstructive existence proof of nash equilibria creates an algorithmic search problem, which we then study with computational complexity. For example, 2-player 0-sum games are P but for three or more players general sum games are NP-hard. I wonder if every nonconstructive existence proof is like this? In the sense of inducing a computational complexity exercise to find what class it's in, before coming up with greedy heuristics to accomplish an approximate example in practice.

I like thinking about "what it feels like to write computer programs if you're a transformer".

Does anyone have a sense of how to benchmark or falsify Nostalgebraist's post on the subject?

Quick version of conversations I keep having, might be worth a top level effortpost.

A prediction market platform giving granular permission systems would open up many use cases for many people

whistleblower protections at large firms, dating, project management and internal company politics--- all userbases with underserved opinions about transparency. Manifold could pivot to this but have a lot of other stuff they could do instead.

Think about slack admins are confused about how to prevent some usergroups from @channel and discord admins aren't.

what are your obnoxious price systems for tutoring?

There's a somewhat niche CS subtopic that a friend wants to learn, I'm really well positioned to teach her. More discussion on the manifold bounty:

10^93 is a fun and underrated number https://en.wikipedia.org/wiki/Transcomputational_problem

Jargon is not due to status scarcity, but it sometimes makes unearned requests for attention

When you see a new intricate discipline, and you're reticent to invest in navigating it, asking to be convinced that your attention has been earned is fine, but I don't recall seeing a valid or interesting complaint about jargon that deviates from this.

Some elaboration here [LW(p) · GW(p)]

There's a remarkable TNG episode about enfeeblement and paul-based threatmodels, if I recall correctly.

There's a post-scarcity planet with some sort of Engine of Prosperity in the townsquare, and it doesn't require maintenance for enough generations that engineering itself is a lost oral tradition. Then it starts showing signs of wear and tear...

If paul was writing this story, they would die. I think in the actual episode, there's a disagreeable autistic teenager who expresses curiosity about the Engine mechanisms, and the grownups basically shame him, like "shut up and focus on painting and dancing". I think the Enterprise crew bails them out by fixing the Engine, and leaving the kid with a lesson about recultivating engineering as a discipline and a sort of intergenerational cultural heritage and responsibility.

I probably saw it over 10 years ago, I haven't looked it up yet. Man, this is a massive boon to the science-communication elements of threatmodeling, given that the state of public discussion seems to be little middle ground between unemployment and literally everyone literally dying. We can just point people to this episode! Any thoughts?

We need a cool one-word snappy thing to say for "just what do you think you know and how do you think you know it" or like "I'm requesting more background about this belief you've stated, if you have time".

I want something that has the same mouthfeel as "roll to disbelieve" for this.

Is there an EV monad? I'm inclined to think there is not, because EV(EV(X)) is a way simpler structure than a "flatmap" analogue.

Would there be a way of estimating how many people within the amazon organization are fanatical about same day delivery ratio against how many are "just working a job"? Does anyone have a guess? My guess is that an organization of that size with a lot of cash only needs about 50 true fanatics, the rest can be "mere employees". What do yall think?

We need a name for the following heuristic, I think, I think of it as one of those "tribal knowledge" things that gets passed on like an oral tradition without being citeable in the sense of being a part of a literature. If you come up with a name I'll certainly credit you in a top level post!

I heard it from Abram Demski at AISU'21.

Suppose you're either going to end up in world A or world B, and you're uncertain about which one it's going to be. Suppose you can pull lever $L_A$ which will be 100 valuable if you end up in world A, or you can pull lever $L_B$ which will be 100 valuable if you end up in world B. The heuristic is that if you pull $L_A$ but end up in world B, you do not want to have created disvalue, in other words, your intervention conditional on the belief that you'll end up in world A should not screw you over in timelines where you end up in world B.

This can be fully mathematized by saying "if most of your probability mass is on ending up in world A, then obviously you'd pick a lever L such that $V\left(L|A\right)$ is very high, just also make sure that $V\left(L|B\right)>=0$ or creates an acceptably small amount of disvalue.", where $V\left(L|A\right)$ is read "the value of pulling lever L if you end up in world A"

critiques and complaints

I think one of the most crucial meta skills i've developed is honing my sense of who's criticizing me vs. who's complaining.

A criticism is actionable, implicitly often it's from someone who wants you to win. A complaint is when you can't figure out how you'd actionably fix something or improve based on what you're being told.

This simple binary story is problematic. It can empower you to ignore criticism you don't like by providing a set of excuses, if you're not careful. Sometimes it's operationally impossible to parse out a criticism that runs so deep that it unsettles your premises from a complaint! I think people who are building things can be excused for ignoring advice if the only actionable way of accepting that advice is to completely overhaul their approach, for reasons of focus and other logistical concerns. If it's that rare time in a project when you are going back to the drawing board and starting over, that's definitely time to mine complaints for useful insight.

Related: the legend of the amazon customer in the 90s who was insatiably filling out customer feedback forms, to the point where 2000s or 2010s amazon named a boardroom after him. The idea was that this guy helped them improve a lot-- surely it would have been easy to dismiss him as a complainer, but they didn't, they found actionable advice within the complaints. I think your ability to take something that isn't intended to help you, isn't actionable on it's face, and mining it for actionable insight can be very important. But for filtering, for attention, for sanity, dismissing something quickly because it doesn't seem like it can help you or the project improve can be valid as well.

hmu for a haskell job in decentralized finance. Super fun zero knowledge proof stuff, great earning to give opportunity.

Are shelling points the occam's razor of mechanism design?

In game theory, a focal point (or Schelling point) is a solution that people tend to choose by default in the absence of communication. (wikipedia)

Intuitively I think simplicity is a good explanation for a solution being converged upon.

Does anyone have any crisp examples that violate the schelling point - occam's razor correspondence?

Disvalue via interpersonal expected value and probability

My deontologist friend just told me that treating people like investments is no way to live. The benefits of living by that take are that your commitments are more binding, you actually do factor out uncertainty, because when you treat people like investments you always think "well someday I'll no longer be creating value for this person and they'll drop me from their life". It's hard to make long term plans, living like that.

I've kept friends around out of loyalty to what we shared 5-10 years ago while questioning an expected value theory or probability theory based value prop. So I'm not, like, super guilty of this or anything. But overall I do take expected value theory and probability theory into interpersonal matters, and I don't object when others do the same for me. Though it's hard sometimes, I think it's basically fine if someone drops me because I'm not adding value for them. An edge case in the opposite direction is that you're obligated to build deep friendships with every acquaintance, which is also a little silly. But a sweet spot, like a marriage or other way of teaming up (like for a project) might meaningfully call for a suspension of expected value theory and probability theory.

I may refine this into a formal bounty at some point.

I'm curious if censorship would actually work in the context of blocking deployment of superpowerful AI systems. Sometimes people will mention "matrix multiplication" as a sort of goofy edge case, which isn't very plausible, but that doesn't mean there couldn't be actual political pressure to censor it. A more plausible example would be attention. Say the government threatens soft power against arxiv if they don't pull attention is all you need, or threatens soft power against harvard if their linguistics department doesn't pull the pytorch-annotated attention is all you need. By this point, it goes without saying that black hat hackers writing down the equations would face serious consequences if they got caught. Now instead of attention, imagine some more galaxy-brained paper or insight that gets published in 2028 and is an actual missing ingredient to advanced AI (assuming you're not one of the people who think attention is all you need already is that paper).

While it's certainly a research project to look at pros and cons of this approach to safety from AI, I think before that we need someone to profile efficacy of technological censorship through history to come at an estimate of how well this would work, i.e., how well it would actually slow or stop the propagation of this information, how well it would slow or stop the deployment of systems based on that information.

My guess at who the ideal person to execute on this bounty would be some patent law nerd, tho I'm sure a variety of types of nerd could do a great job.

any literature on estimates of social impact of businesses divided by their valuations?

the idea that dollars are a proxy for social impact is neat, but leaves a lot of room for goodhart and I think it's plausible that they diverge entirely in cases. It would be useful to know, if possible to know, what's going on here.

Why have I heard about Tyson investing into lab grown, but I haven't heard about big oil investing in renewable?

Tyson's basic insight here is not to identify as "an animal agriculture company". Instead, they identify as "a feeding people company". (Which happens to align with doing the right thing, conveniently!)

It seems like big oil is making a tremendous mistake here. Do you think oil execs go around saying "we're an oil company"? When they could instead be going around saying "we're a powering stuff" company. Being a powering stuff company means you have fuel source indifference!

I mean if you look at all the money they had to spend on disinformation and lobbying, isn't it insultingly obvious to say "just invest that money into renewable research and markets instead"?

Is there dialogue on this? Also, have any members of "big oil" in fact done what I'm suggesting, and I just didn't hear about it?

Gonna cc to ea forum shortform

I've had a background assumption in my interpretation of and beliefs about reward functions for as long as I can remember (i.e. since first reading the sequences), that I suddenly realized I don't believe is written down. Over the last two years I've gained experience writing coq sufficient to inspire a convenient way of framing it.

Computational vs axiomatic reward functions

Computational vs axiomatic in proof engineering

A proof engineer calls a proposition computational if it's proof can be broken down into parts.

For example, a + (b + c) = (a + b) + c is computational because you can think of it's proof as the application of the associativity lemma then the application of something called a "refl", the fundamental termination of a proof involving equality. Passing around the associativity lemma is in a sense passing around it's proof, which assuming a is inductive (take nat; zero and successor) is an application of nat's induction principle, unpacking the recursive definition of +, etc.

In other words, if my adversary asks "why is a + (b + c) = (a + b) + c I can show them; I only have to make sure they agree to the fundamental definitions of nat and + : nat -> nat -> nat, the rest I can compel them to believe.

On the flip side, consider function extensionality, or f = g <-> forall x, f x = g x, not provable because we do not know that the domain of f (which equals the domain of g) is countable, to name but one scenario. Because they can't prove it, theories "admit function extensionality as an axiom" from time to time.

In other words, if I invoke function extensionality in a proof, and my adversary has agreed to the basic type and function definitions, they remain entitled to reject my proof because if they ask why I believe function extensionality the best I can do is say "because I declared it on line 7".

We do not call reasoning involving axioms computational. Instead, the discourse has sort of become poisoned by the axiom; it's verificational properties have become weaker. (Intuitively, I can declare on line 7 anything I want; the risk of proving something that is actually false increases a great deal with each axiom I declare).

Apocryphally, a lecturer recalled a meeting perhaps of the univalent foundations group at IAS, when homotopy type theory (HoTT) was brand new (HoTT is based on something called univalence, which is about reasoning on type equalities in arbitrary "universes" ("kinds" for the haskell programmer)). In HoTT 1.0, univalence relied on an axiom (done carefully of course, to minimize the damage of the poison) and Per Martin-Lof is said to have remarked "it's not really type theory if there's an axiom". HoTT 2.0 called cubical type theory repairs this, which is why cubical tt is sometimes called computational tt.

AIXI-like and AIXI-unlike AGIs

If the space of AGIs can be carved into AIXI-like and AIXI-unlike with respect to goals, clearly AIXI-like architectures have goals imposed on them axiomatically by the programmer. The complement of course is where the reward function is computational; decomposable.

See the NARS literature of Wang et. al. for something at least adjacent to AIXI-unlike-- reasoning about NARS emphasizes that reward functions can be computational to an extent, but "bottom out" at atoms eventually. Still, NARS goals are computational to a far greater degree than AIXI-likes.

Conjecture: humans are AIXI-unlike AGIs

This should be trivial: humans can decompose their reward functions in ways richer than "because god said so".

Relation to mutability???

If the space of AGIs can be carved into AIXI-like and human-like with respect to goals, does the computationality question help me reason about modifying my own reward function? Intuitively, AIXI's axiomatic goal corresponds to immutability. However, I don't think there's a for-free implication that AIXI-unlikes get self-modification for-free. More work needed.

Position of this post in my overall reasoning

In general, my basic understanding that the AGI space can be divided into what I've called AIXI-like and AIXI-unlike with respect to how reward functions are reasoned about, and that computationality (anaxiomaticity vs axiomaticity?) is the crucial axis to view, is deeply embedded in my assumptions. Maybe writing it down will make eventually changing my mind about this easier: I'm uncertain just how conventional my belief/understanding is here.

capabilities-prone research.

I come to you with a dollar I want to spend on AI. You can allocate p pennies to go to capabilities and 100-p pennies to go to alignment, but only if you know of a project that realizes that allocation. For example, we might think that GAN research sets p = 98 (providing 2 cents to alignment) while interpretability research sets p = 10 (providing 90 cents to alignment).

Is this remotely useful? This is a really rough model (you might think it's more of a venn diagram and that this model doesn't provide a way of reasoning about the double counting problem).

a task: rate research areas, even whole agendas, with such value p. Many people may disagree about my example assignments to GANs and interpretability, or think both of those are too broad.

What are some alternatives to the splitting a dollar intuition?

To say something is capabilities-prone is less to say a dollar has been cleanly split, and more to say that there are some dynamics that sort of tend toward or get pushed toward different directions. Perhaps I want some sort of fluid metaphor instead.

Question your argument as your readers will - thoughts on chapter 10 of Craft of Research

Three predictable disagreements are

• There are causes in addition to the one you claim
• What about these counterexamples?
• I don't define X as you do, to me X means...

There are roughly two kinds of queries readers will have about your argument

1. intrinsic soundness - "challenging the clarity of a claim, relevance of reasons, or quality of evidence"
2. extrinsic soundness - "different ways of framing the problem, evidence you've overlooked, or what others have written on the topic." The idea is to anticipate, acknowledge, and respond to both kinds of questions. This is the path to making an argument that readers will trust and accept.

Voicing too many hypothetical objections up front can paralyze you. Instead, what you should do before anything else is focus on what you want to say. Give that some structure, some meat, some life. Then, an important exercise is to imagine readers' responses to it.

I think cleaving these into two highly separated steps is an interesting idea, doing this with intention may be a valuable exercise next time I'm writing something.

View your argument through the eyes of someone who has a stake in a different outcome, someone who wants you to be wrong.

The authors provide some questions about your problem from a possible reader:

1. Why do you think there's a problem at all?
2. Have you properly defined the problem?
3. Is your solution practical or conceptual [LW(p) · GW(p)]?
4. Have you stated your claim too strongly?
5. Why is your practical/conceptual solution better than others?

Then, they provide some questions about your support from a possible reader.

1. "I want to see a different kind of evidence" i.e. hard numbers over anecdotes / real people over cold numbers
2. "It isn't accurate"
3. "It isn't precise enough"
4. "It isn't current"
5. "It isn't representative"
6. "It isn't authoritative"
7. "You need more evidence"

It builds credibility to play defense: to recognize your own argument's limitations. It builds even more credibility to play offense: to explore alternatives to your argument and bring them into your reasoning. If you can, you might develop those alternatives in your own imagination, but more likely you'd like to find alternatives in your sources [LW(p) · GW(p)].

Often your readers will be likeyour sources' authors; sometimes they may even include them.

What is the perfect amount of objections to acknowledge? Acknowledging too many can distract readers from the core of your argument, while acknowledging too few is a signal of laziness or even disrespect. You need to narrow your list of alternatives or objections by subjecting them to the following priorities

• plausible charges of weaknesses that you can rebut
• alternative lines of argument important in your field
• alternative conclusions that readers want to be true
• alternative evidence that readers know
• important counterexamples that youu have to address. What if your argument is flawed? The best thing to do is candidly acknowledge the issue and respond that...
• the rest of your argument more than balances the flaw
• while the flaw is serious, more research will show a way around it
• while the flaw makes it impossible to accept your claim fully, your argument offers important insight into the question and suggests what a better answer would need.

It is wise to build up good faith by acknowledging questions you can't answer. Concessions are often interpreted as positive signals by the reader.

It is important for your responses to acknowledgments to be subordinate to your main point, or else the reader will miss the forest for the trees.

Remember to make an intentional decision about how much credence to give to an objection or alternative. Weaker ones imply weaker credences, imply less effort in your acknowledgment and response.

there's a gap in my inside view of the problem, part of me thinks that capabilities progress such as out-of-distribution robustness or the 4 tenets described in open problems in cooperative ai is necessary for AI to be transformative, i.e. a prereq of TAI, and another part of me that thinks AI will be xrisky and unstable if it progresses along other aspects but not along the axis of those capabilities.

There's a geometry here of transformative / not transformative cross product with dangerous not dangerous.

To have an inside view I must be able to adequately navigate between the quadrants with respect to outcomes, interventions, etc.

testing latex in spoiler tag

missed opportunities to build a predictive track record and trump

I was reminiscing about my prediction market failures, the clearest "almost won a lot of mana dollars" (if manifold markets had existed back then) was this executive order. The campaign speeches made it fairly obvious, and I'm still salty about a few idiots telling me "stop being hysterical" when I accused him of being what he's writing on the tin that he is pre inauguration even though I overall reminisce that being a time when my epistemics were way worse than they are now.

However, there does seem like there needs to be a word for "lack of shock but failed to predict concretely". We were threatmodeling a ton of crazy stuff back then! So what if you can econo-splain "well if you didn't predict concretely then you were, by definition, shocked", the more useful and accurate thing sounds more like "we were worried about various classes of populist atrocities, some of which would look hysterical in hindsight, those which would look hysterical in hindsight crowded out the ability to write detailed executive orders just to win the mana dollars / bayes points / etc.". Early onsets of a populist swing are so anxiety-inducing and chaotic, I forgive myself for making an at least token attempt at security mindset by thinking about how bad it could get, but I shouldn't do so too quickly-- a post manifold markets populist would give me a great opportunity to take things seriously, put a little of that anxiety to use.

So of course, what is the institutional role of metaculus or manifold in the leadup to january 6 2021, or things in that reference class? Again, "didn't write down a detailed description of what would happen, but isn't shocked when it does". It cost 0 IQ points to observe in the months leading up to the election that the administration would be a sore loser in worlds where they lost. So why is it so subtle to leverage this observation to gain actual mana dollars or metaculus ranking? This seems like an open problem to me.