some lessons from ml research:

• any shocking or surprising result in your own experiment is 80% likely to be a bug until proven otherwise. your first thought should always be to comb for bugs.
• only after you have ruled out bugs do you get to actually think about how to fit your theory to the data, and even then, there might still be a hidden bug.
• most papers are terrible and don't replicate.
• most techniques that sound intuitively plausible don't work.
• most techniques only look good if you don't pick a strong enough baseline.
• an actually good idea can take many tries before it works.
• once you have good research intuitions, the most productive state to be in is to literally not think about what will go into the paper and just do experiments that satisfy your curiosity and. convince yourself that the thing is true. once you have that, running the final sweeps is really easy
• most people have no intuition whatsoever about their hardware and so will write code that is horribly inefficient. even learning a little bit about hardware fundamentals so you don't do anything obviously dumb is super valuable
• in a long and complex enough project, you will almost certainly have a bug that invalidates weeks

i recently ran into to a vegan advocate tabling in a public space, and spoke briefly to them for the explicit purpose of better understanding what it feels like to be the target of advocacy on something i feel moderately sympathetic towards but not fully bought in on. (i find this kind of thing very valuable for noticing flaws in myself and improving; it's much harder to be perceptive of one's own actions otherwise). the part where i am genuinely quite plausibly persuadable of his position in theory is important; i think if i had talked to e.g flat earthers one might say my reaction is just because i'd already decided not to be persuaded. several interesting things i noticed (none of which should be surprising or novel, especially for someone less autistic than me, but as they say, intellectually knowing things is not the same as actual experience):

• this guy certainly knew more about e.g health impacts of veganism than i did, and i would not have been able to hold my own in an actual debate.
  • in particular, it's really easy for actually-good-in-practice heuristics to come out as logical fallacies, especially when arguing with someone much more familiar with the object level details th

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)

the modern world has many flaws, but I'm still deeply grateful for the modern era of unprecedented peace, prosperity, and freedom in the developed world. 99% of people reading these words have never had to worry about dying in a cholera epidemic, or malaria or smallpox or the plague, or childbirth, or in war, or from a famine, or due to a political purge. this is not true for other times in history, or other places in the world today.

(extremely unoriginal thought, but still important to acknowledge periodically because it's easy to take for granted. especially because it's much more common to complain about ways the world is broken than to acknowledge what has improved over time.)

I think it would be really bad for humanity to rush to build superintelligence before we solve the difficult problem of how to make it safe. But also I think it would be a horrible tragedy if humanity never ever built superintelligence. I hope we figure out how to thread this needle with wisdom.

I decided to conduct an experiment at neurips this year: I randomly surveyed people walking around in the conference hall to ask whether they had heard of AGI

I found that out of 38 respondents, only 24 could tell me what AGI stands for (63%)

we live in a bubble

(https://x.com/nabla_theta/status/1869144832595431553)

when i was new to research, i wouldn't feel motivated to run any experiment that wouldn't make it into the paper. surely it's much more efficient to only run the experiments that people want to see in the paper, right?

now that i'm more experienced, i mostly think of experiments as something i do to convince myself that a claim is correct. once i get to that point, actually getting the final figures for the paper is the easy part. the hard part is finding something unobvious but true. with this mental frame, it feels very reasonable to run 20 experiments for every experiment that makes it into the paper.

random thoughts on analytical and emotional intelligence 

one thing that I think the world needs more of is analyses into the nature of the mind by people who are both rigorous/analytically inclined, and also emotionally intelligent/integrated. much writing from the former fails to model large parts of the human mind, and much writing from the latter fails to create models of sufficient clarity and validity. 

I think this underlies a lot of my instinctive dislike of humanities work. people who are emotionally perceptive but not rigorous and analytical tend to notice interesting things about the human experience, but then come up with very poor models that set off all of my bullshit sensors that are attuned to rigorous arguments. but I think it should be possible to have humanities work that is not like this.

(for clarity, from here out I will say analytical and emotional to refer to the axes which are independent of each other, and ABNE (analytically but not emotionally intelligent) and EBNA for the converse)

(I also want to clarify that I don't think of analytical as being in opposition to intuition, at least in the context of this post. something something Terence Tao's pos... (read more)

random brainstorming ideas for things the ideal sane discourse encouraging social media platform would have:

• have an LM look at the comment you're writing and real time give feedback on things like "are you sure you want to say that? people will interpret that as an attack and become more defensive, so your point will not be heard". addendum: if it notices you're really fuming and flame warring, literally gray out the text box for 2 minutes with a message like "take a deep breath. go for a walk. yelling never changes minds"
• have some threaded chat component bolted on (I have takes on best threading system). big problem is posts are fundamentally too high effort to be a way to think; people want to talk over chat (see success of discord). dialogues were ok but still too high effort and nobody wants to read the transcript. one stupid idea is have an LM look at the transcript and gently nudge people to write things up if the convo is interesting and to have UI affordances to make it low friction (eg a single button that instantly creates a new post and automatically invites everyone from the convo to edit, and auto populates the headers)
• inspired by the court system, the most autisticall

a thing i've noticed rat/autistic people do (including myself): one very easy way to trick our own calibration sensors is to add a bunch of caveats or considerations that make it feel like we've modeled all the uncertainty (or at least, more than other people who haven't). so one thing i see a lot is that people are self-aware that they have limitations, but then over-update on how much this awareness makes them calibrated. one telltale hint that i'm doing this myself is if i catch myself saying something because i want to demo my rigor and prove that i've considered some caveat that one might think i forgot to consider

i've heard others make a similar critique about this as a communication style which can mislead non-rats who are not familiar with the style, but i'm making a different claim here that one can trick oneself.

it seems that one often believes being self aware of a certain limitation is enough to correct for it sufficiently to at least be calibrated about how limited one is. a concrete example: part of being socially incompetent is not just being bad at taking social actions, but being bad at detecting social feedback on those actions. of course, many people are not even... (read more)

it's quite plausible (40% if I had to make up a number, but I stress this is completely made up) that someday there will be an AI winter or other slowdown, and the general vibe will snap from "AGI in 3 years" to "AGI in 50 years". when this happens it will become deeply unfashionable to continue believing that AGI is probably happening soonish (10-15 years), in the same way that suggesting that there might be a winter/slowdown is unfashionable today. however, I believe in these timelines roughly because I expect the road to AGI to involve both fast periods and slow bumpy periods. so unless there is some super surprising new evidence, I will probably only update moderately on timelines if/when this winter happens

i find it funny that i know people in all 4 of the following quadrants:

• works on capabilities, and because international coordination seems hopeless, we need to race to build ASI first before the bad guys
• works on capabilities, and because international coordination seems possible, and all national leaders like to preserve the status quo, we need to build ASI before it gets banned
• works on safety, and because international coordination seems hopeless, we need to solve the technical problem before ASI kills everyone
• works on safety, and because international coordination seems possible, so we need to focus on regulation and policy before ASI kills everyone

people around these parts often take their salary and divide it by their working hours to figure out how much to value their time. but I think this actually doesn't make that much sense (at least for research work), and often leads to bad decision making.

time is extremely non fungible; some time is a lot more valuable than other time. further, the relation of amount of time worked to amount earned/value produced is extremely nonlinear (sharp diminishing returns). a lot of value is produced in short flashes of insight that you can't just get more of by spending more time trying to get insight (but rather require other inputs like life experience/good conversations/mentorship/happiness). resting or having fun can help improve your mental health, which is especially important for positive tail outcomes.

given that the assumptions of fungibility and linearity are extremely violated, I think it makes about as much sense as dividing salary by number of keystrokes or number of slack messages.

concretely, one might forgo doing something fun because it seems like the opportunity cost is very high, but actually diminishing returns means one more hour on the margin is much less valuable than the average implies, and having fun improves productivity in ways not accounted for when just considering the intrinsic value one places on fun.

every 4 years, the US has the opportunity to completely pivot its entire policy stance on a dime. this is more politically costly to do if you're a long-lasting autocratic leader, because it is embarrassing to contradict your previous policies. I wonder how much of a competitive advantage this is.

random half baked thoughts from a sleep deprived jet lagged mind: my guess is that the few largest principal components of variance of human intelligence are something like:

• a general factor that affects all cognitive abilities uniformly (this is a sum of a bazillion things. they could be something physiological like better cardiovascular function, or more efficient mitochondria or something; or maybe there's some pretty general learning/architecture hyperparameter akin to lr or aspect ratio that simply has better or worse configurations. each small change helps/hurts a little bit). having a better general factor makes you better at pattern recognition and prediction, which is the foundation of all intelligence. whether this is learning a policy or a world model, you need to be able to spot regularities in the world to exploit to have any hope of making good predictions.may
• a systematization factor (how much to be inclined towards using the machinery of pattern recognition towards finding and operating using explicit rules about the world, vs using that machinery implicitly and relying on intuition). this is the autist vs normie axis. importantly, it's not like normies are born with

execution is necessary for success, but direction is what sets apart merely impressive and truly great accomplishment. though being better at execution can make you better at direction, because it enables you to work on directions that others discard as impossible.

timelines takes

• i've become more skeptical of rsi over time. here's my current best guess at what happens as we automate ai research.
  • for the next several years, ai will provide a bigger and bigger efficiency multiplier to the workflow of a human ai researcher.
    • ai assistants will probably not uniformly make researchers faster across the board, but rather make certain kinds of things way faster and other kinds of things only a little bit faster.
    • in fact probably it will make some things 100x faster, a lot of things 2x faster, and then be literally useless for a lot of remaining things
    • amdahl's law tells us that we will mostly be bottlenecked on the things that don't get sped up a ton. like if the thing that got sped up 100x was only 10% of the original thing, then you don't get more than a 1/(1 - 10%) speedup.
      • i think the speedup is a bit more than amdahl's law implies. task X took up 10% of the time because there is diminishing returns to doing more X, and so you'd ideally do exactly the amount of X such that the marginal value of time spent on X is exactly in equilibrium with time spent on anything else. if you suddenly decrease the cost of X substantially, the equilibrium point shifts