Thomas Kwa's MIRI research experience

Meta: I don’t want this comment to be taken as “I disagree with everything you (Thomas) said.” I do think the question of what to do when you have an opaque, potentially intractable problem is not obvious, and I don’t want to come across as saying that I have the definitive answer, or anything like that. It’s tricky to know what to do, here, and I certainly think it makes sense to focus on more concrete problems if deconfusion work didn’t seem that useful to you. 

That said, at a high-level I feel pretty strongly about investing in early-stage deconfusion work, and I disagree with many of the object-level claims you made suggesting otherwise. For instance: 

The neuroscientists I've talked to say that a new scanning technology that could measure individual neurons would revolutionize neuroscience, much more than a theoretical breakthrough. But in interpretability we already have this, and we're just missing the software.

It seems to me like the history of neuroscience should inspire the opposite conclusion: a hundred years of increasingly much data collection at finer and finer resolution, and yet, we still have a field that even many neuroscientists agree barely understands anything. I did undergrad and grad school in neuroscience and can at the very least say that this was also my conclusion. The main problem, in my opinion, is that theory usually tells us which facts to collect. Without it—without even a proto-theory or a rough guess, as with “model-free data collection” approaches—you are basically just taking shots in the dark and hoping that if you collect a truly massive amount of data, and somehow search over it for regularities, that theory will emerge. This seems pretty hopeless to me, and entirely backwards from how science has historically progressed. 

It seems similarly pretty hopeless to me to expect a “revolution” out of tabulating features of the brain at low-enough resolution. Like, I certainly buy that it gets us some cool insights, much like every other imaging advance has gotten us some cool insights. But I don’t think the history of neuroscience really predicts a “revolution,” here. Aside from the computational costs of “understanding” an object in such a way, I just don’t really buy that you’re guaranteed to find all the relevant regularities. You can never collect *all* the data, you have to make choices and tradeoffs when you measure the world, and without a theory to tell you which features are meaningfully irrelevant and can be ignored, it’s hard to know that you’re ultimately looking at the right thing. 

I ran into this problem, for instance, when I was researching cortical uniformity. Academia has amassed a truly gargantuan collection of papers on the structural properties of the human neocortex. What on Earth do any of these papers say about how algorithmically uniform the brain is? As far as I can tell, pretty much close to zero, because we have no idea how the structural properties of the cortex relate to the functional ones, and so who’s to say that “neuron subtype A is more dense in the frontal cortex relative to the visual cortex” is a meaningful finding or not? I worry that other “shot in the dark” data collection methods will suffer similar setbacks.

Eliezer has written about how Einstein cleverly used very limited data to discover relativity. But we could have discovered relativity easily if we observed not only the precession of Mercury, but also the drifting of GPS clocks, gravitational lensing of distant galaxies, gravitational waves, etc.

It’s of course difficult to say how science might have progressed counterfactually, but I find it pretty hard to believe that relativity would have been “discovered easily” were we to have had a bunch of data staring us in the face. In general, I think it’s very easy to underestimate how difficult it is to come up with new concepts. I felt this way when I was reading about Darwin and how it took him over a year to go from realizing that “artificial selection is the means by which breeders introduce changes,” to realizing that “natural selection is the means by which changes are introduced in the wild.” But then I spent a long time in his shoes, so to speak, operating from within the concepts he had available to him at the time, and I became more humbled. For instance, among other things, it seems like a leap to go from “a human uses their intellect to actively select” to “nature ends up acting like a selector, in the sense that its conditions favor some traits for survival over others.” These feel like quite different “types” of things, in some ways. 

In general, I suspect it’s easy to take the concepts we already have, look over past data, and assume it would have been obvious. But I think the history of science again speaks to the contrary: scientific breakthroughs are rare, and I don’t think it’s usually the case that they’re rare because of a lack of data, but because they require looking at that data differently. Perhaps data on gravitational lensing may have roused scientists to notice that there were anomalies, and may have eventually led to general relativity. But the actual process of taking the anomalies and turning that into a theory is, I think, really hard. Theories don’t just pop out wholesale when you have enough data, they take serious work.

I heard David Bau say something interesting at the ICML safety workshop: in the 1940s and 1950s lots of people were trying to unlock the basic mysteries of life from first principles. How was hereditary information transmitted? Von Neumann designed a universal constructor in a cellular automaton, and even managed to reason that hereditary information was transmitted digitally for error correction, but didn't get further. But it was Crick, Franklin, and Watson who used crystallography data to discover the structure of DNA, unraveling far more mysteries. Since then basically all advances in biochemistry have been empirical. Biochemistry is a case study where philosophy and theory failed to solve the problems but empirical work succeeded, and maybe interpretability and intelligence are similar.

This story misses some pretty important pieces. For instance, Schrödinger predicted basic features about DNA—that it was an aperiodic crystal—using first principles in his book What if Life? published in 1944. The basic reasoning is that in order to stably encode genetic information, the molecule should itself be stable, i.e., a crystal. But to encode a variety of information, rather than the same thing repeated indefinitely, it needs to be aperiodic. An aperiodic crystal is a molecule that can use a few primitives to encode near infinite possibilities, in a stable way. His book was very influential, and Francis and Crick both credited Schrödinger with the theoretical ideas that guided their search. I also suspect their search went much faster than it would have otherwise; many biologists at the time thought that the hereditary molecule was a protein, of which there are tens of millions in a typical cell. 

But, more importantly, I would certainly not say that biochemistry is an area where empirical work has succeeded to nearly the extent that we might hope it to. Like, we still can’t cure cancer, or aging, or any of the myriad medical problems people have to endure; we still can’t even define “life” in a reasonable way, or answer basic questions like “why do arms come out basically the same size?” The discovery of DNA was certainly huge, and helpful, but I would say that we’re still quite far from a major success story with biology.

My guess is that it is precisely because we lack theory that we are unable to answer these basic questions, and to advance medicine as much as we want. Certainly the “tabulate indefinitely” approach will continue pushing the needle on biological research, but I doubt it is going to get us anywhere near the gains that, e.g., “the hereditary molecule is an aperiodic crystal” did.

And while it’s certainly possible that biology, intelligence, agency and so on are just not amenable to the cleave-reality-at-its-joints type of clarity one gets from scientific inquiry, I’m pretty skeptical that this the world we in fact live in, for a few reasons. 

For one, it seems to me that practically no one is trying to find theories in biology. It is common for biologists (even bright-eyed, young PhDs at elite universities) to say things like (and in some cases this exact sentence): “there are no general theories in biology because biology is just chemistry which is just physics.” These are people at the beginning of their careers, throwing in the towel before they’ve even started! Needless to say, this take is clearly not true in all generality, because it would anti-predict natural selection. It would also, I think, anti-predict Newtonian mechanics (“there are no general theories of motion because motion is just the motion of chemicals which is just the motion of particles which is just physics”). 

Secondly, I think that practically all scientific disciplines look messy, ad-hoc, and empirical before we get theories that tie it together, and that this does not on its own suggest biology is a theoretically bankrupt field. E.g., we had steam engines before we knew about thermodynamics, but they were kind of ad-hoc, messy contraptions, because we didn’t really understand what variables were causing the “work.” Likewise, naturalism before Darwin was largely compendiums upon compendiums of people being like “I saw this [animal/fossil/plant/rock] here, doing this!” Science before theory often looks like this, I think. 

Third: I’m just like, look guys, I don’t really know what to tell you, but when I look at the world and I see intelligences doing stuff, I sense deep principles. It’s a hunch, to be sure, and kind of hard to justify, but it feels very obvious to me. And if there are deep principles to be had, then I sure as hell want to find them. Because it’s embarrassing that at this point we don’t even know what intelligence is, nor agency, nor abstractions: how to measure any of it, predict when it will increase or not. These are the gears that are going to move our world, for better or for worse, and I at least want my hands on the steering wheel when they do.

I think that sometimes people don’t really know what to envision with theoretical work on alignment, or “agent foundations”-style work. My own vision is quite simple: I want to do great science, as great science has historically been done, and to figure out what in god’s name any of these phenomena are. I want to be able to measure that which threatens our existence, such that we may learn to control it. And even though I am of course not certain this approach is workable, it feels very important to me to try. I think there is a strong case for there being a shot, here, and I want us to take it. 

↑ comment by jacobjacob · 2023-10-05T06:08:01.178Z · LW(p) · GW(p)

I did undergrad and grad school in neuroscience and can at the very least say that this was also my conclusion.

I remember the introductory lecture for the Cognitive Neuroscience course I took at Oxford. I won't mention the professor's name, because he's got his own lab and is all senior and stuff, and might not want his blunt view to be public -- but his take was "this field is 95% nonsense. I'll try to talk about the 5% that isn't". Here's a lecture slide:

Replies from: Simon Skade

↑ comment by Towards_Keeperhood (Simon Skade) · 2023-10-05T20:44:13.223Z · LW(p) · GW(p)

Lol possibly someone should try to make this professor work for Steven Byrnes / on his agenda.

↑ comment by Thomas Kwa (thomas-kwa) · 2023-10-05T22:18:43.988Z · LW(p) · GW(p)

Thanks, I really like this comment. Here are some points about metascience I agree and disagree with, and how this fits into my framework for thinking about deconfusion vs data collection in AI alignment.

• I tentatively think you're right about relativity, though I also feel out of my depth here. [1]
• David Bau must have mentioned the Schrödinger book but I forgot about it, thanks for the correction. The fact that ideas like this told Watson and Crick where to look definitely seems important.
• Overall, I agree that a good theoretical understanding guides further experiment and discovery early on in a field.
• However, I don't think curing cancer or defining life are bottlenecked on deconfusion. [2]
  • For curing cancer, we know the basic mechanisms behind cancer and understand that they're varied and complex. We have categorized dozens of oncogenes of about 7 different types, and equally many ways that organisms defend against cancer. It seems unlikely that the the cure for cancer will depend on some unified theory of cancer, and much more plausible that it'll be due to investments in experiment and engineering. It was mostly engineering that gave us mRNA vaccines, and a mix of all three that allowed CRISPR.
  • For defining life, we already have edge cases like viruses and endosymbiotic organisms, and understand pretty well which things can maintain homeostasis, reproduce, etc. in what circumstances. It also seems unlikely that someone will draw a much sharper boundary around life, especially without lots more useful data.

My model of the basic process of science currently looks like this:

Note that I distinguish deconfusion (what you can invest in) from theory (the output). At any time, there are various returns to investing in data collection tech, experiments, and deconfusion, and returns diminish with the amount invested. I claim that in both physics and biology, we went through an early phase where the bottleneck was theory and returns to deconfusion were high, and currently the fields are relatively mature, such that the bottlenecks have shifted to experiment and engineering, but with theory still playing a role.

In AI, we're in a weird situation:

• We feel pretty confused about basic concepts like agency, suggesting that we're early and deconfusion is valuable.
• Machine learning is a huge field. If there are diminishing returns to deconfusion, this means experiment and data collection are more valuable than deconfusion.
• Machine learning is already doing impressive things primarily on the back of engineering, without much reliance on the type of theory that deconfusion generates alone (deep, simple relationships between things).
  • But even if engineering alone is enough to build AGI, we need theory for alignment.
• In biology, we know cancer is complex and unlikely to be understood by a deep simple theory, but in AI, we don't know whether intelligence is complex.

I'm not sure what to make of all this, and this comment is too long already, but hopefully I've laid out a frame that we can roughly agree on.

[1] When writing the dialogue I thought the hard part of special relativity was discovering the Lorentz transformations (which GPS clock drift observations would make obvious), but Lorentz did this between 1892-1904 and it took until 1905 for Einstein to discover the theory of special relativity. I missed the point about theory guiding experiment earlier, and without relativity we would not have built gravitational wave detectors. I'm not sure whether this also applies to gravitational lensing or redshift.

[2] I also disagree with the idea that "practically no one is trying to find theories in biology". Theoretical biology seems like a decently large field-- probably much larger than it was in 1950-- and biologists use mathematical models all the time.

I wrote this dialogue to stimulate object-level discussion on things like how infohazard policies slow down research, how new researchers can stop flailing around so much, whether deconfusion is a bottleneck to alignment, or the sharp left turn. I’m a bit sad that most of the comments are about exactly how much emotional damage Nate tends to cause people and whether this is normal/acceptable: it seems like a worthwhile conversation to happen somewhere, but now that many people have weighed in with experiences from other contexts, including non-research colleagues, a romantic partner, etc., I think all the drama distracts from the discussions I wanted to have. The LW team will be moving many comments to an escape-valve post [LW · GW]; after this I’ll have a pretty low bar for downvoting comments I think are off-topic here.

The model was something like: Nate and Eliezer have a mindset that's good for both capabilities and alignment, and so if we talk to other alignment researchers about our work, the mindset will diffuse into the alignment community, and thence to OpenAI, where it would speed up capabilities. I think we didn't have enough evidence to believe this, and should have shared more.

What evidence were you working off of? This is an extraordinary thing to believe.

Flagging inconvenient acronym clash between SLT used for Sharp Left Turn and Singular Learning Theory (have seem both)!

I’m also much less pessimistic about the possibility of communicating with people than Nate/MIRI seem to be.

FWIW this is also my take (I have done comms work for MIRI in the past, and am currently doing it part-time) but I'm not sure I have any more communication successes than Nate/MIRI have had, so it's not clear to me this is skill talking on my part instead of inexperience.

Thanks for sharing! I was wondering what happened with that project & found this helpful (and would have found it even more helpful if I didn't already know and talk with some of you).

I'd love to see y'all write more, if you feel like it. E.g. here's a prompt:

You said:

Solving the full problem despite reflection / metacognition seems pretty out of reach for now. In the worst case, if an agent reflects, it can be taken over by subagents, refactor all of its concepts into a more efficient language, invent a new branch of moral philosophy that changes its priorities, or a dozen other things. There's just way too much to worry about, and the ability to do these things is -- at least in humans -- possibly tightly connected to why we're good at science. 

Can you elaborate? I'd love to see a complete list of all the problems you know of (the dozen things!). I'd also love to see it annotated with commentary about the extent to which you expect these problems to arise in practice vs. probably only if we get unlucky. Another nice thing to include would be a definition of reflection/metacognition. Finally it would be great to say some words about why ability to do reflection/metacognition might be tightly connected to ability to do science.

The issues you describe seem fairly similar to Academia, where you get a PhD advisor but don't talk to them very often, in large part because they're busy.

FWIW this depends on the advisor, sometimes you do get to talk to the advisor relatively often.

[1]: To clarify what I mean by "pairs of intuitions", here are two that seem more live to me:

• Is it viable to rely on incomplete [LW · GW] preferences for corrigibility? Sami Petersen showed incomplete agents don't have to violate some consistency properties. But it's still super unclear how one would keep an agent's preferences naturally coming into conflict with yours, if the agent can build a moon rocket ten times faster than you.
• What's going on with low-molecular-weight exhaust and rocket engine efficiency? There's a claim on the internet that since rocket engine efficiency is proportional to velocity of exhaust molecules, and since $KE=\frac{1}{2}m{v}^2$, lighter exhaust molecules give you higher velocity for a given amount of energy. This is validated by the fact that in hydrogen-oxygen rockets, the optimum $I_{sp}$ is achieved when using an excess of lighter hydrogen molecules. But this doesn't make sense, because energy is limited and you can't just double the number of molecules while keeping energy per molecule fixed. (I might write this up)

[2]: Note I'm not endorsing logical decision theory over CDT/EDT because there seem to be some problems and also LDT is not formalized.

Some of these comments about in-person communication with Nate seem like maybe they could be addressed by communicating over text instead. E.g., over text, Nate could choose not to convey an initial distressed reaction to someone not understanding.

Also, I figure this would be a good time to say: I have tremendous respect for all of you (the authors), thanks & keep up the good work! <3

infohazards could be limited by being public until we start producing impressive results, then going private.

That is not how information theory works!

Curated! (And here's the late curation notice) 

I don’t really know what I think about retrospectives in general, and I don’t always find them that helpful, because causal attribution is hard. Nonetheless, here are some reasons why I wanted to curate this:

• I like that it both covers kind of a broad spectrum of stuff that influences a research project, and also manages to go into some interesting detail: high-level research direction and its feasibility, concrete sub-problems attempted and the outcome, particular cognitive and problem-solving strategies that were tried, as well as motivational and emotional aspects of the project. Hearing about what it was like when the various agent confusions collided with the various humans involved, was quite interesting and I feel like it actually gave me a somewhat richer view of both
• It discusses some threads that seem important and that I’ve heard people talk about offline, but that I’ve seen less discussed online recently (the impact of infohazard policies, ambiguous lines between safety and capabilities research and how different inside views might cause one to pursue one rather than the other, people’s experience of interfacing with the MIRI_Nate way of doing research and communicating)
• It holds different perspectives from the people involved in the research group, and I like how the dialogues feature kind of allows each to coexist without feeling a need to squeeze them into a unified narrative (the way things might feel if one were to coauthor a post or paper).

(Note: I chose to curate this, and I am also listed as a coauthor. I think this is fine because ultimately the impetus for writing up this content came from Thomas. Me and Raemon mostly just served as facilitators and interlocutors helping him get this stuff into writing.)

Question: why is a set of ideas about alignment being adjacent to capabilities only a one-way relationship? More directly, why can't this mindset be used to pull alignment gains out of capability research?

I agree with this sentiment ("having lots of data is useful for deconfusion") and think this is probably the most promising avenue for alignment research. In particular, I think we should prioritize the kinds of research that give us lots of bits [LW · GW] about things that could matter. Though from my perspective actually most empirical alignment work basically fails this check, so this isn't just a "empirical good" take.

So is this ability related to Nate's supposed ability to "keep reframing the problem ever-so-slightly until the solution seems obvious"? And does he, in fact, have that later ability? I want that ability. It would speed things up so much. But I've got few leads for how to acquire it.

But to me, this felt like a confusion about the definition of the word "could"

Could you explain what you see as the confusion?