it's surprising just how much of cutting edge research (at least in ML) is dealing with really annoying and stupid bottlenecks. pesky details that seem like they shouldn't need attention. tools that in a good and just world would simply not break all the time.

i used to assume this was merely because i was inexperienced, and that surely eventually you learn to fix all the stupid problems, and then afterwards you can just spend all your time doing actual real research without constantly needing to context switch to fix stupid things. 

however, i've started to think that as long as you're pushing yourself to do novel, cutting edge research (as opposed to carving out a niche and churning out formulaic papers), you will always spend most of your time fixing random stupid things. as you get more experienced, you get bigger things done faster, but the amount of stupidity is conserved. as they say in running- it doesn't get easier, you just get faster.

as a beginner, you might spend a large part of your research time trying to install CUDA or fighting with python threading. as an experienced researcher, you might spend that time instead diving deep into some complicated distributed trai... (read more)

in research, if you settle into a particular niche you can churn out papers much faster, because you can develop a very streamlined process for that particular kind of paper. you have the advantage of already working baseline code, context on the field, and a knowledge of the easiest way to get enough results to have an acceptable paper.

while these efficiency benefits of staying in a certain niche are certainly real, I think a lot of people end up in this position because of academic incentives - if your career depends on publishing lots of papers, then a recipe to get lots of easy papers with low risk is great. it's also great for the careers of your students, because if you hand down your streamlined process, then they can get a phd faster and more reliably.

however, I claim that this also reduces scientific value, and especially the probability of a really big breakthrough. big scientific advances require people to do risky bets that might not work out, and often the work doesn't look quite like anything anyone has done before.

as you get closer to the frontier of things that have ever been done, the road gets tougher and tougher. you end up spending more time building basic infra... (read more)

reliability is surprisingly important. if I have a software tool that is 90% reliable, it's actually not that useful for automation, because I will spend way too much time manually fixing problems. this is especially a problem if I'm chaining multiple tools together in a script. I've been bit really hard by this because 90% feels pretty good if you run it a handful of times by hand, but then once you add it to your automated sweep or whatever it breaks and then you have to go in and manually fix things. and getting to 99% or 99.9% is really hard because things break in all sorts of weird ways.

I think this has lessons for AI - lack of reliability is one big reason I fail to get very much value out of AI tools. if my chatbot catastrophically hallucinates once every 10 queries, then I basically have to look up everything anyways to check. I think this is a major reason why cool demos often don't mean things that are practically useful - 90% reliable it's great for a demo (and also you can pick tasks that your AI is more reliable at, rather than tasks which are actually useful in practice). this is an informing factor for why my timelines are longer than some other people's

i've noticed a life hyperparameter that affects learning quite substantially. i'd summarize it as "willingness to gloss over things that you're confused about when learning something". as an example, suppose you're modifying some code and it seems to work but also you see a warning from an unrelated part of the code that you didn't expect. you could either try to understand exactly why it happened, or just sort of ignore it.

reasons to set it low:

• each time your world model is confused, that's an opportunity to get a little bit of signal to improve your world model. if you ignore these signals you increase the length of your feedback loop, and make it take longer to recover from incorrect models of the world.
• in some domains, it's very common for unexpected results to actually be a hint at a much bigger problem. for example, many bugs in ML experiments cause results that are only slightly weird, but if you tug on the thread of understanding why your results are slightly weird, this can cause lots of your experiments to unravel. and doing so earlier rather than later can save a huge amount of time
• understanding things at least one level of abstraction down often lets you do things more

random fun experiment: accuracy of GPT-4 on "Q: What is 1 + 1 + 1 + 1 + ...?\nA:"

any time someone creates a lot of value without capturing it, a bunch of other people will end up capturing the value instead. this could be end consumers, but it could also be various middlemen. it happens not infrequently that someone decides not to capture the value they produce in the hopes that the end consumers get the benefit, but in fact the middlemen capture the value instead

saying "sorry, just to make sure I understand what you're saying, do you mean [...]" more often has been very valuable

hypothesis: intellectual progress mostly happens when bubbles of non tribalism can exist. this is hard to safeguard because tribalism is a powerful strategy, and therefore insulating these bubbles is hard. perhaps it is possible for there to exist a monopoly on tribalism to make non tribal intellectual progress happen, in the same way a monopoly on violence makes it possible to make economically valuable trade without fear of violence

a great way to get someone to dig into a position really hard (whether or not that position is correct) is to consistently misunderstand that position

one kind of reasoning in humans is a kind of instant intuition; you see something and something immediately and effortlessly pops into your mind. examples include recalling vocabulary in a language you're fluent in, playing a musical instrument proficiently, or having a first guess at what might be going wrong when debugging.

another kind of reasoning is the chain of thought, or explicit reasoning: you lay out your reasoning steps as words in your head, interspersed perhaps with visuals, or abstract concepts that you would have a hard time putting in words. It feels like you're consciously picking each step of the reasoning. Working through a hard math problem, or explicitly designing a codebase by listing the constraints and trying to satisfy them, are examples of this.

so far these map onto what people call system 1 and 2, but I've intentionally avoided these labels because I think there's actually a third kind of reasoning that doesn't fit well into either of these buckets.

sometimes, I need to put the relevant info into my head, and then just let it percolate slowly without consciously thinking about it. at some later time, insights into the problem will suddenly and unpredictably... (read more)

Is it a very universal experience to find it easier to write up your views if it's in response to someone else's writeup? Seems like the kind of thing that could explain a lot about how research tends to happen if it were a pretty universal experience.

for people who are not very good at navigating social conventions, it is often easier to learn to be visibly weird than to learn to adapt to the social conventions.

this often works because there are some spaces where being visibly weird is tolerated, or even celebrated. in fact, from the perspective of an organization, it is good for your success if you are good at protecting weird people.

but from the perspective of an individual, leaning too hard into weirdness is possibly harmful. part of leaning into weirdness is intentional ignorance of normal conventions. this traps you in a local minimum where any progress on understanding normal conventions hurts your weirdness, but isn't enough to jump all the way to the basin of the normal mode of interaction.

(epistemic status: low confidence, just a hypothesis)

a common discussion pattern: person 1 claims X solves/is an angle of attack on problem P. person 2 is skeptical. there is also some subproblem Q (90% of the time not mentioned explicitly). person 1 is defending a claim like "X solves P conditional on Q already being solved (but Q is easy)", whereas person 2 thinks person 1 is defending "X solves P via solving Q", and person 2 also believes something like "subproblem Q is hard". the problem with this discussion pattern is it can lead to some very frustrating miscommunication:

• if the discussion recurses into whether Q is hard, person 1 can get frustrated because it feels like a diversion from the part they actually care about/have tried to find a solution for, which is how to find a solution to P given a solution to Q (again, usually Q is some implicit assumption that you might not even notice you have). it can feel like person 2 is nitpicking or coming up with fully general counterarguments for why X can never be solved.
• person 2 can get frustrated because it feels like the original proposed solution doesn't engage with the hard subproblem Q. person 2 believes that assuming Q were solved, then there would be many other proposals other than X that would also suffice to solve problem P, so that the core ideas of X actually aren't that important, and all the work is actually being done by assuming Q.

philosophy: while the claims "good things are good" and "bad things are bad" at first appear to be compatible with each other, actually we can construct a weird hypothetical involving exact clones that demonstrates that they are fundamentally inconsistent with each other

law: could there be ambiguity in "don't do things that are bad as determined by a reasonable person, unless the thing is actually good?" well, unfortunately, there is no way to know until it actually happens

economic recession and subsequent reduction in speculative research, including towards AGI, seems very plausible

AI (by which I mean, like, big neural networks and whatever) is not that economically useful right now. furthermore, current usage figures are likely an overestimate of true economic usefulness because a very large fraction of it is likely to be bubbly spending that will itself dry up if there is a recession (legacy companies putting LLMs into things to be cool, startups that are burning money without PMF, consumers with disposable income to spend on entertainment).

it will probably still be profitable to develop AI tech, but things will be much more tethered to consumer usefulness.

this probably doesn't set AGI back that much but I think people are heavily underrating this as a possibility. it also probably heavily impacts the amount of alignment work done at labs.

for something to be a good way of learning, the following criteria have to be met:

• tight feedback loops
• transfer of knowledge to your ultimate goal
• sufficiently interesting that it doesn't feel like a grind

trying to do the thing you care about directly hits 2 but can fail 1 and 3. many things that you can study hit 1 but fail 2 and 3. and of course, many fun games hit 3 (and sometimes 1) but fail to hit 2.

often the easiest way to gain status within some system is to achieve things outside that system

Corollary to Others are wrong != I am right (https://www.lesswrong.com/posts/4QemtxDFaGXyGSrGD/other-people-are-wrong-vs-i-am-right): It is far easier to convince me that I'm wrong than to convince me that you're right.

current understanding of optimization

• high curvature directions (hessian eigenvectors with high eigenvalue) want small lrs. low curvature directions want big lrs
• if the lr in a direction is too small, it takes forever to converge. if the lr is too big, it diverges by oscillating with increasing amplitude
• momentum helps because if your lr is too small, it makes you move a bit faster. if your lr is too big, it causes the oscillations to cancel out with themselves. this makes high curvature directions more ok with larger lrs and low curvature directions more ok

Some aspirational personal epistemic rules for keeping discussions as truth seeking as possible (not at all novel whatsoever, I'm sure there exist 5 posts on every single one of these points that are more eloquent)

• If I am arguing for a position, I must be open to the possibility that my interlocutor may turn out to be correct. (This does not mean that I should expect to be correct exactly 50% of the time, but it does mean that if I feel like I'm never wrong in discussions then that's a warning sign: I'm either being epistemically unhealthy or I'm talking

hypothesis: the kind of reasoning that causes ML people to say "we have made no progress towards AGI whatsoever" is closely analogous to the kind of reasoning that makes alignment people say "we have made no progress towards hard alignment whatsoever"

ML people see stuff like GPT4 and correctly notice that it's in fact kind of dumb and bad at generalization in the same ways that ML always has been. they make an incorrect extrapolation, which is that AGI must therefore be 100 years away, rather than 10 years away

high p(doom) alignment people see current mode... (read more)

for a sufficiently competent policy, the fact that BoN doesn't update the policy doesn't mean it leaks any fewer bits of info to the policy than normal RL

aiming directly for achieving some goal is not always the most effective way of achieving that goal.

it is often claimed that merely passively absorbing information is not sufficient for learning, but rather some amount of intentional learning is needed. I think this is true in general. however, one interesting benefit of passively absorbing information is that you notice some concepts/terms/areas come up more often than others. this is useful because there's simply too much stuff out there to learn, and some knowledge is a lot more useful than other knowledge. noticing which kinds of things come up often is therefore useful for prioritization. I often notice that my motivational system really likes to use this heuristic for deciding how motivated to be while learning something.

a claim I've been saying irl for a while but have never gotten around to writing up: current LLMs are benign not because of the language modelling objective, but because of the generalization properties of current NNs (or to be more precise, the lack thereof). with better generalization LLMs are dangerous too. we can also notice that RL policies are benign in the same ways, which should not be the case if the objective was the core reason. one thing that can go wrong with this assumption is thinking about LLMs that are both extremely good at generalizing ... (read more)

Schmidhubering the agentic LLM stuff pretty hard https://leogao.dev/2020/08/17/Building-AGI-Using-Language-Models/

in a highly competitive domain, it is often better and easier to be sui generis, rather than a top 10 percentile member of a large reference class

retargetability might be the distinguishing factor between controllers and optimizers

A thought pattern that I've noticed myself and others falling into sometimes: Sometimes I will make arguments about things from first principles that look something like "I don't see any way X can be true, it clearly follows from [premises] that X is definitely false", even though there are people who believe X is true. When this happens, it's almost always unproductive to continue to argue on first principles, but rather I should do one of: a) try to better understand the argument and find a more specific crux to disagree on or b) decide that this topic isn't worth investing more time in, register it as "not sure if X is true" in my mind, and move on.

there are policies which are successful because they describe a particular strategy to follow (non-mesaoptimizers), and policies that contain some strategy for discovering more strategies (mesaoptimizers). a way to view the relation this has to speed/complexity priors that doesn't depend on search in particular is that policies that work by discovering strategies tend to be simpler and more generic (they bake in very little domain knowledge/metis, and are applicable to a broader set of situations because they work by coming up with a strategy for the task ... (read more)