Hey, I’m Owen. I think rationality is pretty rad.
lifelonglearner
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This is a good point, and this is where I think a good amount of the difficulty lies, especially as the cited example of human interpretable NNs (i.e. Microscope AI) doesn’t seem easily applicable to things outside of image recognition.
Interesting stuff!
My understanding is that the OpenAI Microscope (is this what you meant by microscope AI?) is mostly feature visualization techniques + human curation by looking at the visualized samples. Do you have thoughts on how to modify this for the text domain?
Same here. I am working for a small quant trading firm, and the collective company wisdom is to prefer CDFs over PDFs.
Regarding how interpretability can help with addressing motivation issues, I think Chris Olah’s views present situations where interpretability can potentially sidestep some of those issues. One such example is that if we use interpretability to aid in model design, we might have confidence that our system isn’t a mesa-optimizer, and we’ve done this without explicitly asking questions about “what our model desires”.
I agree that this is far from the whole picture. The scenario you describe is an example where we’d want to make interpretability more accessible to more end-users. There is definitely more work to be done to bridge “normal” human explanations with what we can get from our analysis.
I’ve spent more of my time thinking about the technical sub-areas, so I’m focused on situations where innovations there can be useful. I don’t mean to say that this is the only place where I think progress is useful.
I think that the general form of the problem is context-dependent, as you describe. Useful explanations do seem to depend on the model, task, and risks involved.
However, from an AI safety perspective, we’re probably only considering a restricted set of interpretability approaches, which might make it easier. In the safety context, we can probably less concerned with interpretability that is useful for laypeople, and focus on interpretability that is useful for the people doing the technical work.
To that end, I think that “just” being careful about what the interpretability analysis means can help, like how good statisticians can avoid misuse of statistical testing, even though many practitioners get it wrong.
I think it’s still an open question, though, what even this sort of “only useful for people who know what they’re doing” interpretability analysis would be. Existing approaches still have many issues.
I mostly focused on the interpretability section as that’s what I’m most familiar with, and I think your criticisms are very valid. I also wrote up some thoughts recently on where post-hoc interpretability fails, and Daniel Filan has some good responses in the comments below.
Also, re: disappointment on tree regularization, something that does seem more promising is Daniel Filan and others at CHAI working on investigating modularity in neural nets. You can probably ask him more, but last time we chatted, he also had some thoughts (unpublished) on how to enforce modularization as a regularizer, which seems to be what you wished the tree reg paper would have done.
Overall, this is great stuff, and I’ll need to spend more time thinking about the design vs search distinction (which makes sense to me at first glance)/
Got it.
I think unbundling them seems like a good thing to strive for.
I guess the parts that I might still be worried about are:
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I see below that you claim that more accountability is probably net-good for most students, in the sense that would help improve learning? I’m not sure that I fully agree with that. My experience in primary to upper education has been that there is a great many students who don’t seem that motivated to learn due to differing priorities, home situations, or preferences. I think improving education will need to find some way of addressing this beyond just accountability.
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Do you envision students enrolling in this Improved Education program for free? Public schools right now have a distinct advantage because they receive a lot of funding from taxpayers.
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I think the issue of, “Why can’t we just immediately get switch everyone to a decoupled situation where credentialing and education are separate?” is due to us being stuck in an inadequate equilibrium. Do you have plans to specifically tackle these inertia-related issues that can make mass-adoption difficult? (e.g until cheap credentialing services become widespread, why would signaling-conscious students decide to enroll in Improved Education instead of Normal Education?)
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I think figuring out how to make education better is definitely a worthwhile goal, and I’m reading this post (and your other one) with interest.
I’m curious to what extent you’re going to be addressing the issue of education as-partially-or-mostly signaling, like what Caplan argues for in The Case Against Education? I can imagine a line of argument that says paying for public education is worthwhile, even if all it does is accreditation because it’s useful to employers. What those actual costs look like and what they should be is, of course, up for debate.
I could also see the point that all this signaling stuff is orthogonal if all we “really” care about is optimizing for learning. Just wondering what stance you’re taking.
I think the OSC’s reproducibility project is much more of what you’re looking for, if you’re worried that Many Labs is selecting only for a specific type of effect.
They focus on selecting studies quasi-randomly and use a variety of reproducibility measures (confidence interval, p-value, effect size magnitude + direction, subjective assessment). They find that around 30-50% of effects replicate, depending on the criteria used. They looked at 100 studies, in total.
I don’t know enough about the biomedical field, but a brief search on the web yields the following links, which might be useful?
Science Forum: The Brazilian Reproducibility Initiative which aims to reproduce 60-100 Brazilian studies, results due in 2021.
Section 2 of this symposium report from 2015 which collects some studies (including the OSC one I list above)
This page references some studies from around early 2010-2011 which find base rates of ~10% for replicating oncology-related stuff.
A quote from the thread which suggests weighing Google and FB more than Amazon, or at least more consideration than above.
I don’t understand why one would invest more in Amazon over Alphabet. Alphabet owns 1. the strongest industry research division around (especially DeepMind), 2. a very strong vertical with Google Cloud → Tensorflow/Jax → TPUs. Amazon only has an arguably more established cloud (I’m not sure if this is even true for machine learning purposes), but has a much weaker research division and doesn’t own any of the underlying stack. I mean, for example, GPT2 was trained primarily on TPUs. So Google owns better shovels and also better diggers.
Facebook owns the second best industry research division, as well as PyTorch (which is the most popular framework in ML research right now). Unfortunately for FB stock, it doesn’t have a particularly clear path towards monetizing it. However, many of the other companies mentioned (Microsoft and OpenAI for example) are heavily invested in it.
Relevant thread from r/slatestarcodex which has some additional discussion.
This makes sense to me, given the situation you describe.
Some OpenAI people are on LW. It’d be interesting to hear their thoughts as well.
Two general things which have made me less optimistic about OpenAI are that:
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They recently spun-out a capped-profit company, which seems like the end goal is monetizing some of their recent advancements. The page linked in the previous sentence also has some stuff about safety and about how none of their day-to-day work is changing, but it doesn’t seem that encouraging.
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They’ve recently partnered up with Microsoft, presumably for product integration. This seems like it positions them as less of a neutral entity, especially as Alphabet owns DeepMind.
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I hadn’t heard of the Delphi method before, so this paper brought it to my attention.
It’s nice to see concrete forecasting questions laid out in a principled way. Now the perhaps harder step is trying to get traction on them ;^).
Note: The tables in pages 9 and 10 are a little blurry to read. They are also not text, so it’s not easy to copy-paste them into another format for better viewing. I think it’d be good to update the images to either be clearer or translate it into a text table.
Interpretability in ML: A Broad Overview
Operationalizing Interpretability
I found your categorization of three ways to improve explanations to be useful, and they seem like they cover most of the issues.
However, I feel like the brunt of the article itself was too short to give me a good sense of what canonical forms are like in math, or how to apply them conversationally. In particular, I think having more examples (or making the examples clearer) for each item on your list would have been helpful.
Also, I personally would have also enjoyed a more technical explanation of how to think about canonical forms mathematically. (Which I would guess would help me understand the connection to conversations.)
I pattern-matched many of your between-task ambiguities to the different types of scheduling algorithms that can occur in operating systems.
I’ve been working through the textbook as well. I know I’ve commented a few times before, but, once again, thanks for writing up your thoughts for each chapter. They’ve been useful for summarizing / checking my own understanding.
The way I did this for a specific ordering of cards (used for a set of magic tricks called Mnemonica) was to have some sort of 1 to 1 mapping between each card and its position in the deck.
Some assorted examples: 5 : 4 of Hearts because 4 is 5 minus 1 (and the Hearts are just there). 7 : Ace of Spades because 7 is a lucky number and the Ace of Spades is a lucky card. 8 : 5 of Hearts because 5 looks a little like 8. 49 : 5 of Clubs because 4.9 is almost 5 (and the Clubs are just there).