Air purifiers are highly suboptimal and could be >2.5x better.

Some things I learned while researching air purifiers for my house, to reduce COVID risk during jam nights.

• An air purifier is simply a fan blowing through a filter, delivering a certain CFM (airflow in cubic feet per minute). The higher the filter resistance and lower the filter area, the more pressure your fan needs to be designed for, and the more noise it produces.
• HEPA filters are inferior to MERV 13-14 filters except for a few applications like cleanrooms. The technical advantage of HEPA filters is filtering out 99.97% of particles of any size, but this doesn't matter when MERV 13-14 filters can filter 77-88% of infectious aerosol particles at much higher airflow. The correct metric is CADR (clean air delivery rate), equal to airflow * efficiency. [1, 2]
• Commercial air purifiers use HEPA filters for marketing reasons and to sell proprietary filters. But an even larger flaw is that they have very small filter areas for no apparent reason. Therefore they are forced to use very high pressure fans, dramatically increasing noise.
• Originally people devised the Corsi-Rosenthal Box to maximize CADR. They're cheap but rather loud and ugly, but later designs have fixed this.
• (85% confidence) Wirecutter recommendations (Coway AP-1512HH) have been beat by ~2.5x in CADR/$, CADR/noise, CADR/floor area, and CADR/watt at a given noise level, just by having higher filter area; the better purifiers are about 2.5x better at their jobs. [3]
  • At noise levels acceptable for a living room (~40 dB, Wirecutter's top pick on medium), CleanAirKits and Nukit sell purifier kits that use PC fans to push air through commercial MERV filters, getting 2.5x CADR at the same noise level, footprint, and energy usage [4]. These are basically handmade but still achieve cost parity with used Coways, 2.5x CADR/$ against new Coways, and use cheaper filters.
  • At higher noise levels (Wirecutter's top pick on high), there are kits and DIY options meant for garages and workshops that beat Wirecutter in cost too.
• However, there exist even better designs that no one is making.
  • jefftk devised a ceiling fan air purifier [LW · GW] which is extremely quiet.
  • Someone on Twitter made a wall-mounted prototype with PC fans that blocks fan noise, reducing noise by another few dB and reducing the space requirement to near zero. If this were mass-produced flat-pack furniture (and had a few more fans), it would likely deliver ~300 CFM CADR (2.7x Wirecutter's top pick on medium, enough to meet ASHRAE 241 standards for infection control for 6 people in a residential common area or 9 in an office), be really cheap, and generally be unobtrusive enough in noise, space, and aesthetics to be run 24/7.
  • A seller on Taobao makes PC fan kits for much less than cleanairkits (reddit discussion). One model is sold on Amazon for a big markup, but it's not the best model, takes 4-7 weeks to ship, is often out of stock, and don't ship to CA where I live. If their taller (higher area) model shipped to CA I would get it over the cleanairkits one.
  • V-bank filters should have ~3x higher filter area for a given footprint, further increasing CADR by maybe 1.7x.
• If I'm right, the fact that these are not mass-produced is a major civilizational failing.

[1] For large rooms, another concern is getting air to circulate properly.

[2] One might worry that the 20% of particles that pass through MERV filters will be more likely to pass through again, which would put a ceiling on the achievable purification. But in practice, you can get to the air quality of a low-grade cleanroom with enough MERV 13 filtration, even if the filters are a few months old. Also, MERV filters get a slight efficiency boost from the slower airflow of a PC fan CR box.

[3] Most commercially available air purifiers have even worse CADR/$ or noise than the Wirecutter picks.

[4] The Wirecutter top pick was tested at 110 CFM on medium; the CleanAirKits Luggable XL was tested at 323 CFM at around the same noise level (not sure of exact values as measurements differ, but the Luggable is likely quieter) and footprint with slightly higher power usage.

The cost of goods has the same units as the cost of shipping: $/kg. Referencing between them lets you understand how the economy works, e.g. why construction material sourcing and drink bottling has to be local, but oil tankers exist.

• An iPhone costs $4,600/kg, about the same as SpaceX charges to launch it to orbit. [1]
• Beef, copper, and off-season strawberries are $11/kg, about the same as a 75kg person taking a three-hour, 250km Uber ride costing $3/km.
• Oranges and aluminum are $2-4/kg, about the same as flying them to Antarctica. [2]
• Rice and crude oil are ~$0.60/kg, about the same as $0.72 for shipping it 5000km across the US via truck. [3,4] Palm oil, soybean oil, and steel are around this price range, with wheat being cheaper. [3]
• Coal and iron ore are $0.10/kg, significantly more than the cost of shipping it around the entire world via smallish (Handysize) bulk carriers. Large bulk carriers are another 4x more efficient [6].
• Water is very cheap, with tap water $0.002/kg in NYC. But shipping via tanker is also very cheap, so you can ship it maybe 1000 km before equaling its cost.

It's really impressive that for the price of a winter strawberry, we can ship a strawberry-sized lump of coal around the world 100-400 times.

[1] iPhone is $4600/kg, large launches sell for $3500/kg, and rideshares for small satellites $6000/kg. Geostationary orbit is more expensive, so it's okay for GPS satellites to cost more than an iPhone per kg, but Starlink wants to be cheaper.

[2] https://fred.stlouisfed.org/series/APU0000711415. Can't find numbers but Antarctica flights cost $1.05/kg in 1996.

[3] https://www.bts.gov/content/average-freight-revenue-ton-mile

[4] https://markets.businessinsider.com/commodities

[5] https://www.statista.com/statistics/1232861/tap-water-prices-in-selected-us-cities/

[6] https://www.researchgate.net/figure/Total-unit-shipping-costs-for-dry-bulk-carrier-ships-per-tkm-EUR-tkm-in-2019_tbl3_351748799

People with p(doom) > 50%: would any concrete empirical achievements on current or near-future models bring your p(doom) under 25%?

Answers could be anything from "the steering vector for corrigibility generalizes surprisingly far" to "we completely reverse-engineer [LW(p) · GW(p)] GPT4 and build a trillion-parameter GOFAI without any deep learning".

Eight beliefs I have about technical alignment research

Written up quickly; I might publish this as a frontpage post with a bit more effort.

1. Conceptual work on concepts like “agency”, “optimization”, “terminal values”, “abstractions”, “boundaries” is mostly intractable at the moment.
   • Success via “value alignment” alone— a system that understands human values, incorporates these into some terminal goal, and mostly maximizes for this goal, seems hard unless we’re in a very easy world because this involves several fucked concepts.
2. Whole brain emulation probably won’t happen in time because the brain is complicated and biology moves slower than CS, being bottlenecked by lab work.
3. Most progress will be made using simple techniques [LW · GW] and create artifacts publishable in top journals (or would be if reviewers understood alignment as well as e.g. Richard Ngo).
4. The core story for success (>50%) goes something like:
   • Corrigibility can in practice be achieved by instilling various cognitive properties [LW · GW] into an AI system, which are difficult but not impossible to maintain as your system gets pivotally capable.
   • These cognitive properties will be a mix of things from normal ML fields (safe RL), things that rhyme with normal ML fields (unlearning, faithfulness), and things that are currently conceptually fucked but may become tractable (low impact, no ontological drift).
   • A combination of oversight and these cognitive properties is sufficient to get useful cognitive work out of an AGI.
   • Good oversight complements corrigibility properties, because corrigibility both increases the power of your most capable trusted overseer and prevents your untrusted models from escaping.
5. Most end-to-end “alignment plans” are bad for three reasons: because research will be incremental and we need to adapt to future discoveries, because we need to achieve several things for AI to go well (no alignment magic bullet), and because to solve the hardest worlds that are possible, you have to engage with MIRI threat models which very few people can do well [1].
   • e.g. I expect Superalignment’s impact to mostly depend on their ability to adapt to knowledge about AI systems that we gain in the next 3 years, and continue working on relevant subproblems.
6. The usefulness of basic science is limited unless you can eventually demonstrate some application. We should feel worse about a basic science program the longer it goes without application, and try to predict how broad the application of potential basic science programs will be.
   • Glitch tokens work [LW · GW] probably won’t go anywhere. But steering vectors [LW · GW] are good because there are more powerful techniques [LW · GW] in that space.
   • The usefulness of sparse coding depends on whether we get applications like sparse circuit discovery, or intervening on features in order to usefully steer model behavior. Likewise with circuits-style mechinterp, singular learning theory, etc.
7. There are convergent instrumental pressures towards catastrophic behavior given certain assumptions about how cognition works, but the assumptions are rather strong and it’s not clear if the argument goes through.
   • The arguments I currently think are strongest are Alex Turner’s power-seeking theorem [LW · GW] and an informal argument about goals.
8. Thoughts on various research principles picked up from Nate Soares
   • You should have a concrete task in mind when you’re imagining an AGI or alignment plan: agree. I usually imagine something like “Apollo program from scratch”.
   • Non-adversarial principle (A safe AGI design should not become unsafe if any part of it becomes infinitely good at its job): unsure, definitely agree with weaker versions
     • Garrabrant calls this robustness to relative scale [LW(p) · GW(p)]
   • To make any alignment progress we must first understand cognition through either theory or interpretability: disagree
   • You haven’t engaged with the real problem until your alignment plan handles metacognition, self-modification, etc.: weakly disagree; wish we had some formalism for “weak metacognition” to test our designs against [2]

[1], [2]: I expect some but not all of the MIRI threat models to come into play. Like, when we put safeguards into agents, they'll rip out or circumvent some but not others, and it's super tricky to predict which. My research with Vivek [LW · GW] often got stuck by worrying too much about reflection, others get stuck by worrying too little.

Agency/consequentialism is not a single property.

It bothers me that people still ask the simplistic question "will AGI be agentic and consequentialist by default, or will it be a collection of shallow heuristics?". A consequentialist utility maximizer is just a mind with a bunch of properties that tend to make it capable, incorrigible, and dangerous. These properties can exist independently, and the first AGI probably won't have all of them, so we should be precise about what we mean by "agency". Off the top of my head, here are just some of the qualities included in agency:

• Consequentialist goals that seem to be about the real world rather than a model/domain
• Complete preferences between any pair of worldstates
• Tends to cause impacts disproportionate to the size of the goal (no low impact preference)
• Resists shutdown
• Inclined to gain power (especially for instrumental reasons)
• Goals are unpredictable or unstable (like instrumental goals that come from humans' biological drives)
• Goals usually change due to internal feedback, and it's difficult for humans to change them
• Willing to take all actions it can conceive of to achieve a goal, including those that are unlikely on some prior

See Yudkowsky's list of corrigibility properties [LW · GW] for inverses of some of these.

It is entirely possible to conceive of an agent at any capability level--including far more intelligent and economically valuable than humans-- that has some but not all properties; e.g. an agent whose goals are about the real world, has incomplete preferences, high impact, does not resist shutdown but does tend to gain power, etc.

Other takes I have:

• As AIs become capable of more difficult and open-ended tasks, there will be pressure of unknown and varying strength towards each of these agency/incorrigibility properties.
• Therefore, the first AGIs capable of being autonomous CEOs will have some but not all of these properties.
• It is also not inevitable that agents will self-modify into having all agency properties.
• [edited to add] All this may be true even if future AIs run consequentialist algorithms that naturally result in all these properties, because some properties are more important than others, and because we will deliberately try to achieve some properties, like shutdownability.
• The fact that LLMs are largely corrigible is a reason for optimism about AI risk compared to 4 years ago, but you need to list individual properties to clearly say why. "LLMs are not agentic (yet)" is an extremely vague statement.
• Multifaceted corrigibility evals are possible but no one is doing them. DeepMind's recent evals paper was just on capability. Anthropic's RSP doesn't mention them. I think this is just because capability evals are slightly easier to construct?
• Corrigibility evals are valuable. It should be explicit in labs' policies that an AI with low impact is relatively desirable, that we should deliberately engineer AIs to have low impact, and that high-impact AIs should raise an alarm just like models that are capable of hacking or autonomous replication.
• Sometimes it is necessary to talk about "agency" or "scheming" as a simplifying assumption for certain types of research, like Redwood's control agenda [LW · GW].

[1] Will add citations whenever I find people saying this

I think the framing "alignment research is preparadigmatic" might be heavily misunderstood. The term "preparadigmatic" of course comes from Thomas Kuhn's The Structure of Scientific Revolutions. My reading of this says that a paradigm is basically an approach to solving problems which has been proven to work, and that the correct goal of preparadigmatic research should be to do research generally recognized as impressive.

For example, Kuhn says in chapter 2 that "Paradigms gain their status because they are more successful than their competitors in solving a few problems that the group of practitioners has come to recognize as acute." That is, lots of researchers have different ontologies/approaches, and paradigms are the approaches that solve problems that everyone, including people with different approaches, agrees to be important. This suggests that to the extent alignment is still preparadigmatic, we should try to solve problems recognized as important by, say, people in each of the five clusters of alignment researchers [LW(p) · GW(p)] (e.g. Nate Soares, Dan Hendrycks, Paul Christiano, Jan Leike, David Bau). 

I think this gets twisted in some popular writings on LessWrong. John Wentworth writes [LW · GW] that a researcher in a preparadigmatic field should spend lots of time explaining their approaches:

Because the field does not already have a set of shared frames [LW · GW] - i.e. a paradigm - you will need to spend a lot of effort explaining your frames, tools, agenda, and strategy. For the field, such discussion is a necessary step to spreading ideas and eventually creating a paradigm.

I think this is misguided. A paradigm is not established by ideas diffusing between researchers with different frames until they all agree on some weighted average of the frames. A paradigm is established by research generally recognized as impressive, which proves the correctness of (some aspect of) someone's frames. So rather than trying to communicate one's frame to everyone, one should communicate with other researchers to get an accurate sense of what problems they think are important, and then work on those problems using one's own frames. (edit: of course, before this is possible one should develop one's frames to solve some problems)

Possible post on suspicious multidimensional pessimism:

I think MIRI people (specifically Soares and Yudkowsky but probably others too) are more pessimistic than the alignment community average on several different dimensions, both technical and non-technical: morality, civilizational response, takeoff speeds, probability of easy alignment schemes working, and our ability to usefully expand the field of alignment. Some of this is implied by technical models, and MIRI is not more pessimistic in every possible dimension, but it's still awfully suspicious.

I strongly suspect that one of the following is true:

• the MIRI "optimism dial" is set too low
• everyone else's "optimism dial" is set too high. (Yudkowsky has said this multiple times in different contexts)
• There are common generators that I don't know about that are not just an "optimism dial", beyond MIRI's models

I'm only going to actually write this up if there is demand; the full post will have citations which are kind of annoying to find.

You should update by +-1% on AI doom surprisingly frequently

This is just a fact about how stochastic processes work. If your p(doom) is Brownian motion in 1% steps starting at 50% and stopping once it reaches 0 or 1, then there will be about 50^2=2500 steps of size 1%. This is a lot! If we get all the evidence for whether humanity survives or not uniformly over the next 10 years, then you should make a 1% update 4-5 times per week. In practice there won't be as many due to heavy-tailedness in the distribution concentrating the updates in fewer events, and the fact you don't start at 50%. But I do believe that evidence is coming in every week such that ideal market prices should move by 1% on maybe half of weeks, and it is not crazy for your probabilities to shift by 1% during many weeks if you think about it often enough. [Edit: I'm not claiming that you should try to make more 1% updates, just that if you're calibrated and think about AI enough, your forecast graph will tend to have lots of >=1% week-to-week changes.]

Maybe this is too tired a point, but AI safety really needs exercises-- tasks that are interesting, self-contained (not depending on 50 hours of readings), take about 2 hours, have clean solutions, and give people the feel of alignment research.

I found some of the SERI MATS application questions better than Richard Ngo's exercises [AF · GW] for this purpose, but there still seems to be significant room for improvement. There is currently nothing smaller than ELK (which takes closer to 50 hours to develop a proposal for and properly think about it) that I can point technically minded people to and feel confident that they'll both be engaged and learn something.

I'm worried that "pause all AI development" is like the "defund the police" of the alignment community. I'm not convinced it's net bad because I haven't been following governance-- my current guess is neutral-- but I do see these similarities:

• It's incredibly difficult and incentive-incompatible with existing groups in power
• There are less costly, more effective steps to reduce the underlying problem, like making the field of alignment 10x larger or passing regulation to require evals
• There are some obvious negative effects; potential overhangs or greater incentives to defect in the AI case, and increased crime, including against disadvantaged groups, in the police case
• There's far more discussion than action (I'm not counting the fact that GPT5 isn't being trained yet; that's for other reasons)
• It's memetically fit, and much discussion is driven by two factors that don't advantage good policies over bad policies, and might even do the reverse. This is the toxoplasma of rage.
  • disagreement with the policy
  • (speculatively) intragroup signaling; showing your dedication to even an inefficient policy proposal proves you're part of the ingroup. I'm not 100% this was a large factor in "defund the police" and this seems even less true with the FLI letter, but still worth mentioning.

This seems like a potentially unpopular take, so I'll list some cruxes. I'd change my mind and endorse the letter if some of the following are true.

• The claims above are mistaken/false somehow.
• Top labs actually start taking beneficial actions towards the letter's aims
• It's caused people to start thinking more carefully about AI risk
• A 6 month pause now is especially important by setting anti-racing norms, demonstrating how far AI alignment is lagging behind capabilities, or something
• A 6 month pause now is worth close to 6 months of alignment research at crunch time (my guess is that research at crunch time is worth 1.5x-3x more depending on whether MIRI is right about everything)
• The most important quality to push towards in public discourse is how much we care about safety at all, so I should endorse this proposal even though it's flawed

Say I need to publish an anonymous essay. If it's long enough, people could plausibly deduce my authorship based on the writing style; this is called stylometry. The only stylometry-defeating tool I can find is Anonymouth; it hasn't been updated in 7 years and it's unclear if it can defeat modern AI. Is there something better?

Tech tree for worst-case/HRAD alignment

Here's a diagram of what it would take to solve alignment in the hardest worlds, where something like MIRI's HRAD agenda is needed. I made this months ago with Thomas Larsen and never got around to posting it (mostly because under my worldview it's pretty unlikely that we can, or need to, do this), and it probably won't become a longform at this point. I have not thought about this enough to be highly confident in anything.
 

• This flowchart is under the hypothesis that LLMs have some underlying, mysterious algorithms and data structures that confer intelligence, and that we can in theory apply these to agents constructed by hand, though this would be extremely tedious. Therefore, there are basically three phases: understanding what a HRAD agent would do in theory, reverse-engineering language models, and combining these two directions. The final agent will be a mix of hardcoded things and ML, depending on what is feasible to hardcode and how well we can train ML systems whose robustness and conformation to a spec we are highly confident in.
• Theory of abstractions: Also called multi-level models. A mathematical framework for a world-model that contains nodes at different levels of abstraction, such that one can represent concepts like “diamond” and “atom” while respecting consistency between different levels, and be robust to ontology shifts
• WM inference = inference on a world-model for an embedded agent, may run in like double exponential time so long as it's computable

I was going to write an April Fool's Day post in the style of "On the Impossibility of Supersized Machines", perhaps titled "On the Impossibility of Operating Supersized Machines", to poke fun at bad arguments that alignment is difficult. I didn't do this partly because I thought it would get downvotes. Maybe this reflects poorly on LW?

The independent-steps model of cognitive power

A toy model of intelligence implies that there's an intelligence threshold above which minds don't get stuck when they try to solve arbitrarily long/difficult problems, and below which they do get stuck. I might not write this up otherwise due to limited relevance, so here it is as a shortform, without the proofs, limitations, and discussion.

The model

A task of difficulty n is composed of $n$ independent and serial subtasks. For each subtask, a mind of cognitive power $Q$ knows $Q$ different “approaches” to choose from. The time taken by each approach is at least 1 but drawn from a power law, $P(X>x)=x^{-\alpha }$ for $x>1$, and the mind always chooses the fastest approach it knows. So the time taken on a subtask is the minimum of $Q$ samples from the power law, and the overall time for a task is the total for the n subtasks.

Main question: For a mind of strength $Q$,

• what is the average rate at which it completes tasks of difficulty n?
• will it be infeasible for it to complete sufficiently large tasks?

Results

• There is a critical threshold $Q_{crit}$ of intelligence below which the distribution of time to complete a subtask has infinite mean. This threshold depends on $\alpha$.
  • This implies that for an n-step task, the median of average time-per-subtask grows without bound as n increases. So (for minds below the critical threshold) the median time to complete a whole task grows superlinearly with n.
• Above the critical threshold, minds can solve any task in expected linear time.
• Some distance above the critical threshold, minds are running fairly close to the optimal speed, and further increases in Q cause small efficiency gains.
• I think this doesn't depend on the function being a power law; it would be true for many different heavy-tailed distributions, but the math wouldn't be as nice.

I'm looking for AI safety projects with people with some amount of experience. I have 3/4 of a CS degree from Caltech, one year at MIRI, and have finished the WMLB and ARENA bootcamps. I'm most excited about making activation engineering more rigorous, but willing to do anything that builds research and engineering skill.

If you've published 2 papers in top ML conferences or have a PhD in something CS related, and are interested in working with me, send me a DM.

I had a long-ish conversation with John Wentworth and want to make it known that I could probably write up any of the following distillations if I invested lots of time into them (about a day (edit: 3 days seems more likely) of my time and an hour of John's). Reply if you're really interested in one of them.

• What is the type signature of a utility function?
• Utility functions must be defined with respect to an external world-model
• Infinite money-pumps are not required for incoherence, and not likely in practice. The actual incoherent behavior is that an agent could get to states A_1 or A_2, identical except that A_1 has more money, and chooses A_2. Implications.
• Why VNM is not really a coherence theorem. Other coherence theorems relating to EU maximization simultaneously derive Bayesian probabilities and utilities. VNM requires an external frequentist notion of probabilities.

The LessWrong Review's short review period is a fatal flaw.

I would spend WAY more effort on the LW review if the review period were much longer. It has happened about 10 times in the last year that I was really inspired to write a review for some post, but it wasn’t review season. This happens when I have just thought about a post a lot for work or some other reason, and the review quality is much higher because I can directly observe how the post has shaped my thoughts. Now I’m busy with MATS and just don’t have a lot of time, and don’t even remember what posts I wanted to review.

I could have just saved my work somewhere and paste it in when review season rolls around, but there really should not be that much friction in the process. The 2022 review period should be at least 6 months, including the entire second half of 2023, and posts from the first half of 2022 should maybe even be reviewable in the first half of 2023.

Below is a list of powerful optimizers ranked on properties, as part of a brainstorm on whether there's a simple core of consequentialism that excludes corrigibility. I think that AlphaZero is a moderately strong argument that there is a simple core of consequentialism which includes inner search.

Properties

• Simple: takes less than 10 KB of code. If something is already made of agents (markets and the US government) I marked it as N/A.
• Coherent: approximately maximizing a utility function most of the time. There are other definitions:
  • Not being money-pumped
  • Nate Soares's notion in the MIRI dialogues: having all your actions point towards a single goal
  • John Wentworth's setup of Optimization at a Distance [? · GW]
• Adversarially coherent: something like "appears coherent to weaker optimizers" or "robust to perturbations by weaker optimizers". This implies that it's incorrigible.
  • Sufficiently optimized agents appear coherent - Arbital
  • will achieve high utility even when "disrupted" by an optimizer somewhat less powerful
• Search+WM: operates by explicitly ranking plans within a world-model. Evolution is a search process, but doesn't have a world-model. The contact with the territory it gets comes from directly interacting with the world, and this is maybe why it's so slow

Notes:

• Humans are not adversarially coherent: prospect theory and other cognitive biases can be exploited, indoctrination, etc.
• AIXI-tl is not adversarially coherent because it is an embedded agent and can be switched off etc.
• AlphaZero: when playing chess, you can use another strategy and it still wins
• Markets are inexploitable, but they don't do search in a world-model other than the search done by individual market participants
• The US government is not adversarially coherent in most circumstances, even if its subparts are coherent; lobbying can affect the US government's policies, and it is meant to be corrigible by the voting population.
• Evolution is not coherent: species often evolve to extinction; foxes and rabbits, etc.

Has anyone made an alignment tech tree where they sketch out many current research directions, what concrete achievements could result from them, and what combinations of these are necessary to solve various alignment subproblems? Evan Hubinger made this [LW · GW], but that's just for interpretability and therefore excludes various engineering achievements and basic science in other areas, like control, value learning, agent foundations, Stuart Armstrong's work, etc.

Suppose that humans invent nanobots that can only eat feldspars (41% of the earth's continental crust). The nanobots:

• are not generally intelligent
• can't do anything to biological systems
• use solar power to replicate, and can transmit this power through layers of nanobot dust
• do not mutate
• turn all rocks they eat into nanobot dust small enough to float on the wind and disperse widely

Does this cause human extinction? If so, by what mechanism?

Antifreeze proteins prevent water inside organisms from freezing, allowing them to survive at temperatures below 0 °C. They do this by actually binding to tiny ice crystals and preventing them from growing further, basically keeping the water in a supercooled state. I think this is fascinating.

Is it possible for there to be nanomachine enzymes (not made of proteins, because they would denature) that bind to tiny gas bubbles in solution and prevent water from boiling above 100 °C?

Is there a well-defined impact measure to use that's in between counterfactual value and Shapley value, to use when others' actions are partially correlated with yours?

I started a dialogue with @Alex_Altair [LW · GW] a few months ago about the tractability of certain agent foundations problems, especially the agent-like structure problem [LW · GW]. I saw it as insufficiently well-defined to make progress on anytime soon. I thought the lack of similar results in easy settings, the fuzziness of the "agent"/"robustly optimizes" concept, and the difficulty of proving things about a program's internals given its behavior all pointed against working on this. But it turned out that we maybe didn't disagree on tractability much, it's just that Alex had somewhat different research taste, plus thought fundamental problems in agent foundations must be figured out to make it to a good future, and therefore working on fairly intractable problems can still be necessary. This seemed pretty out of scope and so I likely won't publish.

Now that this post [LW · GW] is out, I feel like I should at least make this known. I don't regret attempting the dialogue, I just wish we had something more interesting to disagree about.

I'm planning to write a post called "Heavy-tailed error implies hackable proxy". The idea is that when you care about $V$ and are optimizing for a proxy $U=V+X$, Goodhart's Law sometimes implies that optimizing hard enough for  [LW · GW]$U$ causes  [LW · GW]$V$ to stop increasing. [LW · GW]

A large part of the post would be proofs about what the distributions of $X$ and $V$ must be for ${lim}_{t\to \infty }E[V|V+X>t]=0$, where X and V are independent random variables with mean zero. It's clear that

• X must be heavy-tailed (or long-tailed or something)
• X must have heavier tails than V

The proof seems messy though; Drake Thomas and I have spent ~5 person-days on it and we're not quite done. Before I spend another few days proving this, is it a standard result in statistics? I looked through a textbook and none of the results were exactly what I wanted.

Note that a couple of people have already looked at it for ~5 minutes and found it non-obvious, but I suspect it might be a known result anyway on priors.

The most efficient form of practice is generally to address one's weaknesses. Why, then, don't chess/Go players train by playing against engines optimized for this? I can imagine three types of engines:

1. Trained to play more human-like sound moves (soundness as measured by stronger engines like Stockfish, AlphaZero).
2. Trained to play less human-like sound moves.
3. Trained to win against (real or simulated) humans while making unsound moves.

The first tool would simply be an opponent when humans are inconvenient or not available. The second and third tools would highlight weaknesses in one's game more efficiently than playing against humans or computers. I'm confused about why I can't find any attempts at engines of type 1 that apply modern deep learning techniques, or any attempts whatsoever at engines of type 2 or 3.

I looked at Tetlock's Existential Risk Persuasion Tournament results, and noticed some oddities. The headline result is of course "median superforecaster gave a 0.38% risk of extinction due to AI by 2100, while the median AI domain expert gave a 3.9% risk of extinction." But all the forecasters seem to have huge disagreements from my worldview on a few questions:

• They divided forecasters into "AI-Concerned" and "AI-Skeptic" clusters. The latter gave 0.0001% for AI catastrophic risk before 2030, and even lower than this (shows as 0%) for AI extinction risk. This is incredibly low, and don't think you can have probabilities this low without a really good reference class.
• Both the AI-Concerned and AI-skeptic clusters gave low probabilities for space colony before 2030, 0.01% and "0%" medians respectively.
• Both groups gave numbers I would disagree with for the estimated year of extinction: year 3500 for AI-concerned, and 28000 for AI-skeptic. Page 339 suggests that none of the 585 survey participants gave a number above 5 million years, whereas it seems plausible to me and probably many EA/LW people on the "finite time of perils" thesis that humanity survives for 10^12 years or more, likely giving an expected value well over 10^10. The justification given for the low forecasts even among people who believed the "time of perils" arguments seems to be that conditional on surviving for millions of years, humanity will probably become digital, but even a 1% chance of the biological human population remaining above the "extinction" threshold of 5,000 still gives an expected value in the billions.

I am not a forecaster and would probably be soundly beaten in any real forecasting tournament, but perhaps there is a bias against outlandish-seeming forecasts, strongest in this last question, that also affects the headline results.

Question for @AnnaSalamon [LW · GW] and maybe others. What's the folk ethics analysis [LW(p) · GW(p)] behind the sinking of the SF Hydro, which killed 14 civilians but destroyed heavy water to be used in the Nazi nuclear weapons program? Eliezer used this as a classic example of ethical injunctions [LW · GW] once.

People say it's important to demonstrate alignment problems like goal misgeneralization. But now, OpenAI, Deepmind, and Anthropic have all had leaders sign the CAIS statement on extinction risk and are doing substantial alignment research. The gap between the 90th percentile alignment concerned people at labs and the MIRI worldview is now more about security mindset. Security mindset is present in cybersecurity because it is useful in the everyday, practical environment researchers work in. So perhaps a large part of the future hinges on whether security mindset becomes useful for solving problems in applied ML.

• Will it become clear that the presence of one bug in an AI system implies that there are probably five more?
• Will architectures that we understand with fewer moving parts be demonstrated to have better robustness than black-box systems or systems that work for complicated reasons?
• Will it become tractable to develop these kinds of simple transparent systems so that security mindset can catch on?

The author of "Where Is My Flying Car" says that the Feynman Program (teching up to nanotechnology by machining miniaturized parts, which are assembled into the tools for micro-scale machining, which are assembled into tools for yet smaller machining, etc) might be technically feasible and the only reason we don't have it is that no one's tried it yet. But this seems a bit crazy for the following reasons:

• The author doesn't seem like a domain expert
• AFAIK this particular method of nanotechnology was just an idea Feynman had in the famous speech and not a serious proposal (he introduces it with "let me suggest one weird possibility")
• Investment in the Feynman Program, if it's feasible, would pay off quickly, since there are large economic benefits to the incremental microtechnologies (getting halfway there at a reasonable price would essentially produce any manufactured good that doesn't require nanoscale processes, which seems like most of them, while also increasing quality).
• In particular, semiconductor manufacturing started at the micro-scale, and many companies converged on lithography for manufacturing rather than using miniature robots. Then we invented MEMS, which is machinery created using lithography-like processes, and chip factories never switched from lithography to MEMS. We never even progressed from MEMS to fully general microtechnology.

Does anyone with domain expertise have further thoughts?

Is it possible to make an hourglass that measures different amounts of time in one direction than the other? Say, 25 minutes right-side up, and 5 minutes upside down, for pomodoros. Moving parts are okay (flaps that close by gravity or something) but it should not take additional effort to flip.

↑ comment by mingyuan · 2020-12-13T00:54:01.057Z · LW(p) · GW(p)

I don't see why this wouldn't be possible? It seems pretty straightforward to me; the only hard part would be the thing that seems hard about making any hourglass, which is getting it to take the right amount of time, but that's a problem hourglass manufacturers have already solved. It's just a valve that doesn't close all the way: 

Unless you meant, "how can I make such an hourglass myself, out of things I have at home?" in which case, idk bro.

Replies from: mr-hire

↑ comment by Matt Goldenberg (mr-hire) · 2020-12-13T01:07:18.915Z · LW(p) · GW(p)

One question I have about both your solution and mine is how easy it is to vary the time drastically by changing the size of the hole.  My intuition says that too large holes behave much differently than smaller holes and if you want a drastic 5x difference in speed you might get into this "too large and the sand sort of just rushes through" behavior.

Given that social science research often doesn't replicate [LW · GW], is there a good way to search a social science finding or paper and see if it's valid?

Ideally, one would be able to type in e.g. "growth mindset" or a link to Dweck's original research, and see:

• a statement of the idea e.g. 'When "students believe their basic abilities, their intelligence, their talents, are just fixed traits", they underperform students who "understand that their talents and abilities can be developed through effort, good teaching and persistence." Carol Dweck initially studied this in 2012, measuring 5th graders on IQ tests.'
• an opinion from someone reputable
• any attempted replications, or meta-analyses that mention it
• the Replication Markets predicted replication probability, if no replications have been attempted.

An idea for removing knowledge from models

Suppose we have a model with parameters $x$, and we want to destroy a capability-- doing well on loss function $f$-- so completely that fine-tuning can't recover it. Fine-tuning would use gradients $\nabla f\left(x\right)$, so what if we fine-tune the model and do gradient descent on the norm of the gradients $\parallel \nabla f\left(x\right)\parallel$ during fine-tuning, or its directional derivative $\nabla f\left(x\right)\cdot v$ where $v=\text{copy}\left(\nabla f\right(x),\text{requires\_grad = False})$? Then maybe if we add the accumulated parameter vector, the new copy of the model won't have useful gradients to fine-tune on.

This is a simple enough idea that it's probably somewhere in the literature, but I don't know where to search; maybe it's been done in an adversarial training context?

We might want to keep our AI from learning a certain fact about the world, like particular cognitive biases humans have that could be used for manipulation. But a sufficiently intelligent agent might discover this fact despite our best efforts. Is it possible to find out when it does this through monitoring, and trigger some circuit breaker?

Evals can measure the agent's propensity for catastrophic behavior, and mechanistic anomaly detection hopes to do better by looking at the agent's internals without assuming interpretability, but if we can measure the agent's beliefs, we can catch the problem earlier. Maybe there can be more specific evals we give to the agent, which are puzzles that can only be solved if the agent knows some particular fact. Or maybe the agent is factorable into a world-model and planner, and we can extract whether it knows the fact from the world-model.

Have the situational awareness people already thought about this? Does anything change when we're actively trying to erase a belief?

Somewhat related to this post [LW · GW] and this post [? · GW]:

Coherence implies mutual information between actions. That is, to be coherent, your actions can't be independent. This is true under several different definitions of coherence, and can be seen in the following circumstances:

• When trading between resources (uncertainty over utility function). If you trade 3 apples for 2 bananas, this is information that you won't trade 3 bananas for 2 apples, if there's some prior distribution over your utility function.
• When taking multiple actions from the same utility function (uncertainty over utility function). Your actions will all have to act like a phased array [? · GW] pushing the variables you care about in some direction.
• When taking multiple actions based on the same observation (uncertainty over observation / world-state). Suppose that you're trying to juggle, and your vision is either reversed or not reversed. The actions of your left arm and right arm will have mutual information, because they both depend on whether your vision has been reversed in related ways.

This would be a full post, but I don't think it's important enough to write up.

Many people think that AI alignment is intractable (<50% chance of success) and also believe that a universe optimized towards elephant CEV, or the CEV of aliens that had a similar evolutionary environment to humans, would be at least 50% as good as a universe optimized towards human CEV. Doesn't this mean we should be spending significant effort (say, at least 1% of the effort spent on alignment) finding tractable plans to create a successor species in case alignment fails?

Are there ring species where the first and last populations actually can interbreed? What evolutionary process could feasibly create one?

2.5 million jobs were created in May 2020, according to the jobs report. Metaculus was something like [99.5% or 99.7% confident](https://www.metaculus.com/questions/4184/what-will-the-may-2020-us-nonfarm-payrolls-figure-be/) that the number would be smaller, with the median at -11.0 and 99th percentile at -2.8. This seems like an obvious sign Metaculus is miscalibrated, but we have to consider both tails, making this merely a 1 in 100 or 1 in 150 event, which doesn't seem too bad.

I don't know how to say this without sounding elitist, but my guess is that people who prolifically write LW comments and whose karma:(comments+posts) ratio is less than around 1.5:1 or maybe even 2:1 should be more selective in what they say. Around this range, it would be unwarranted for mods to rate-limit you, but perhaps the bottom 30% of content you produce is net negative considering the opportunity cost to readers.

Of course, one should not Goodhart for karma and Eliezer is not especially virtuous by having a 16:1 ratio, but 1.5:1 is a quite low ratio. You get 1.5 karma if 0.75 high-karma users or 1.5 low-karma users weakly upvote your comment. Considering that it takes barely a second to upvote and comments get tens of views, this is not a high bar for the average comment.

Caveats:

• If your absolute number of comments + posts is low, the risk of making new users participate less outweighs the risk of comments clogging up the frontpage.
• In some contexts votes might underestimate quality, like if your comment is on some esoteric topic that few people engage with.

Hangnails are Largely Optional

Hangnails are annoying and painful, and most people deal with them poorly. [1] Instead, use a drop of superglue to glue it to your nail plate. It's $10 for 12 small tubes on Amazon. Superglue is also useful for cuts and minor repairs, so I already carry it around everywhere.

Hangnails manifest as either separated nail fragments or dry peeling skin on the paronychium (area around the nail). In my experience superglue works for nail separation, and a paper (available free on Scihub) claims it also works for peeling skin on the paronychium.

Is this safe?

Cyanoacrylate glue is regularly used in medicine to close wounds, and now frequently replaces stitches. Medical superglue has slightly different types of cyanoacrylate, but doctors I know say it's basically the same thing.

I think medical superglue exists to prevent rare reactions and for large wounds where the exothermic reaction from a large quantity might burn you, and the safety difference for hangnails is minimal [2]. But to be extra safe you could just use 3M medical grade superglue or Dermabond.

[1]: Typical responses to hangnails include:

• Pulling them out, which can lead to further bleeding or infection.
• Trimming them with nail clippers, which often leaves a jagged edge.
• Wrapping the affected finger in a bandage, requiring daily changes.

[2]: There have been studies showing cytotoxicity in rabbits when injecting it in their eyes, or performing internal (bone or cartilage) grafts. A 2013 review says that although some studies have found internal toxicity, "[f]or wound closure and various other procedures, there have been a considerable number of studies finding histologic equivalence between ECA [commercial superglue] and more widely accepted modalities of repair."

Current posts in the pipeline:

• Dialogue on whether agent foundations is valuable, with Alex Altair. I might not finish this.
• Why junior AI safety researchers should go to ML conferences
• Summary of ~50 interesting or safety-relevant papers from ICML and NeurIPS this year 
• More research log entries
• A list of my mistakes from the last two years. For example, spending too much money
• Several flowcharts / tech trees for various alignment agendas.

I'm reading a series of blogposts called Extropia's Children about the Extropians mailing list and its effects on the rationalist and EA communities. It seems quite good although a bit negative at times.

Eliezer Yudkowsky wrote in 2016:

At an early singularity summit, Jürgen Schmidhuber, who did some of the pioneering work on self-modifying agents that preserve their own utility functions with his Gödel machine, also solved the friendly AI problem. Yes, he came up with the one true utility function that is all you need to program into AGIs!

(For God’s sake, don’t try doing this yourselves. Everyone does it. They all come up with different utility functions. It’s always horrible.)

His one true utility function was “increasing the compression of environmental data.” Because science increases the compression of environmental data: if you understand science better, you can better compress what you see in the environment. Art, according to him, also involves compressing the environment better. I went up in Q&A and said, “Yes, science does let you compress the environment better, but you know what really maxes out your utility function? Building something that encrypts streams of 1s and 0s using a cryptographic key, and then reveals the cryptographic key to you.”

At first it seemed to me that EY refutes the entire idea that "increasing the compression of environmental data" is intrinsically valuable. This surprised me because my intuition says it is intrinsically valuable, though less so than other things I value.

But EY's larger point was just that it's highly nontrivial for people to imagine the global maximum of a function. In this specific case, building a machine that encrypts random data seems like a failure of embedded agency [LW · GW] rather than a flaw in the idea behind the utility function. What's going on here?

What was the equation for research progress referenced in Ars Longa, Vita Brevis?

“Then we will talk this over, though rightfully it should be an equation. The first term is the speed at which a student can absorb already-discovered architectural knowledge. The second term is the speed at which a master can discover new knowledge. The third term represents the degree to which one must already be on the frontier of knowledge to make new discoveries; at zero, everyone discovers equally regardless of what they already know; at one, one must have mastered every previously-discovered fact before one can discover anything new. The fourth term represents potential for specialization; at one, it is impossible to understand any part without understanding the whole; at zero, it can be subdivided freely. The fifth…”

Showerthought: what's the simplest way to tell that the human body is less than 50% efficient at converting chemical energy to mechanical work via running? I think it's that running uphill makes you warmer than running downhill at the same speed.

When running up a hill at mechanical power p and efficiency f, you have to exert p/f total power and so p(1/f - 1) is dissipated as heat. When running down the hill you convert p to heat. p(1/f - 1) > p implies that f > 0.5.

Maybe this story is wrong somehow. I'm pretty sure your body has no way of recovering your potential energy on the way down; I'd expect most of the waste heat to go in your joints and muscles but maybe some of it goes into your shoes.

Are there approximate versions of the selection theorems [LW · GW]? I haven't seen anyone talk about them, but they might be easy to prove.

Approximate version of Kelly criteron: any agent that follows a strategy different by at least epsilon from Kelly betting will almost surely lose money compared to a Kelly-betting agent at a rate f(epsilon)

Approximate version of VNM: Any agent that satisfies some weakened version of the VNM axioms will have high likelihood under Boltzmann rationality (or some other metric of approximate utility maximization). The closest thing I've seen is logical inductors.

Approximate version of good regulator theorem: any approximately optimal regulator is equivalent to something that approximately models variables in its environment

Probably there are others.

Ideally, the assumptions and/or conclusions would describe how agents like humans, companies, animals, and ML systems actually work.

Is there somewhere I can find a graph of the number of AI alignment researchers vs AI capabilities researchers over time, from say 2005 to the present day?

Is there software that would let me automatically switch between microphones on my computer when I put on my headset?

I imagine this might work as a piece of software that integrates all microphones connected to my computer into a single input device, then transmits the audio stream from the best-quality source.

A partial solution would be something that automatically switches to the headset microphone when I switch to the headset speakers.