I am Issa Rice. https://issarice.com/
riceissa(Issa Rice)
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Robert Heaton calls this (or a similar enough idea) the Made-Up-Award Principle.
Maybe this? (There are a few subthreads on that post that mention linear regression.)
I think Discord servers based around specific books are an underappreciated form of academic support/community. I have been part of such a Discord server (for Terence Tao’s Analysis) for a few years now and have really enjoyed being a part of it.
Each chapter of the book gets two channels: one to discuss the reading material in that chapter, and one to discuss the exercises in that chapter. There are also channels for general discussion, introductions, and a few other things.
Such a Discord server has elements of university courses, Math Stack Exchange, Reddit, independent study groups, and random blog posts, but is different from all of them:
Unlike courses (but like Math SE, Reddit, and independent study groups), all participation is voluntary so the people in the community are selected for actually being interested in the material.
Unlike Math SE and Reddit (but like courses and independent study groups), one does not need to laboriously set the context each time one wants to ask a question or talk about something. It’s possible to just say “the second paragraph on page 76” or “Proposition 6.4.12(c)” and expect to be understood, because there is common knowledge of what the material is and the fact that everyone there has access to that material. In a subject like real analysis where there are many ways to develop the material, this is a big plus.
Unlike independent study groups and courses (but like Math SE and Reddit), there is no set pace or requirement to join the study group at a specific point in time. This means people can just show up whenever they start working on the book without worrying that they are behind and need to catch up to the discussion, because there is no single place in the book everyone is at. This also makes this kind of Discord server easier to set up because it does not require finding someone else who is studying the material at the same time, so there is less cost to coordination.
Unlike random forum/blog posts about the book, a dedicated Discord server can comprehensively cover the entire book and has the potential to be “alive/motivating” (it’s pretty demotivating to have a question about a blog post which was written years ago and where the author probably won’t respond; I think reliability is important for making it seem safe/motivating to ask questions).
I also like that Discord has an informal feel to it (less friction to just ask a question) and can be both synchronous and asynchronous.
I think these Discord servers aren’t that hard to set up and maintain. As long as there is one person there who has worked through the entire book, the server won’t seem “dead” and it should accumulate more users. (What’s the motivation for staying in the server if you’ve worked through the whole book? I think it provides a nice review/repetition of the material.) I’ve also noticed that earlier on I had to answer more questions in early chapters of the book, but now there are more people who’ve worked through the early chapters who can answer those questions, so I tend to focus on the later chapters now. So my concrete proposal is that more people, when they finish working through a book, should try to “adopt” the book by creating a Discord server and fielding questions from people who are still working through the book (and then advertising in some standard channels like a relevant subreddit). This requires little coordination ability (everyone from the second person onward selfishly benefits by joining the server and does not need to pay any costs).
I am uncertain how well this format would work for less technical books where there might not be a single answer to a question/a “ground truth” (which leaves room for people to give their opinions more).
(Thanks to people on the Tao Analysis Discord, especially pecfex for starting a discussion on the server about whether there are any similar servers, which gave me the idea to write this post, and Segun for creating the Tao Analysis Discord.)
I learned about the abundance of available resources this past spring.
I’m curious what this is referring to.
Rob, are you able to disclose why people at Open Phil are interested in learning more decision theory? It seems a little far away from the AI strategy reports they’ve been publishing in recent years, and it also seemed like they were happy to keep funding MIRI (via their Committee for Effective Altruism Support) despite disagreements about the value of HRAD research, so the sudden interest in decision theory is intriguing.
I am also running into this problem now with the Markdown editor. I switched over from the new rich editor because that one didn’t support footnotes, whereas the Markdown one does. It seems like there is no editor that can both scale images and do footnotes, which is frustrating.
Edit: I ended up going with the rich editor despite broken footnotes since that seemed like the less bad of the two problems.
Re (a): I looked at chapters 4 and 5 of Superintelligence again, and I can kind of see what you mean, but I’m also frustrated that Bostrom seems really non-committal in the book. He lists a whole bunch of possibilities but then doesn’t seem to actually come out and give his mainline visualization/”median future”. For example he looks at historical examples of technology races and compares how much lag there was, which seems a lot like the kind of thinking you are doing, but then he also says things like “For example, if human-level AI is delayed because one key insight long eludes programmers, then when the final breakthrough occurs, the AI might leapfrog from below to radically above human level without even touching the intermediary rungs.” which sounds like the deep math view. Another relevant quote:
Building a seed AI might require insights and algorithms developed over many decades by the scientific community around the world. But it is possible that the last critical breakthrough idea might come from a single individual or a small group that succeeds in putting everything together. This scenario is less realistic for some AI architectures than others. A system that has a large number of parts that need to be tweaked and tuned to work effectively together, and then painstakingly loaded with custom-made cognitive content, is likely to require a larger project. But if a seed AI could be instantiated as a simple system, one whose construction depends only on getting a few basic principles right, then the feat might be within the reach of a small team or an individual. The likelihood of the final breakthrough being made by a small project increases if most previous progress in the field has been published in the open literature or made available as open source software.
Re (b): I don’t disagree with you here. (The only part that worries me is, I don’t have a good idea of what percentage of “AI safety people” shifted from one view to the other, whether were were also new people with different views coming in to the field, etc.) I realize the OP was mainly about failure scenarios, but it did also mention takeoffs (“takeoffs won’t be too fast”) and I was most curious about that part.
I was reading parts of Superintelligence recently for something unrelated and noticed that Bostrom makes many of the same points as this post:
If the frontrunner is an AI system, it could have attributes that make it easier for it to expand its capabilities while reducing the rate of diffusion. In human-run organizations, economies of scale are counteracted by bureaucratic inefficiencies and agency problems, including difficulties in keeping trade secrets. These problems would presumably limit the growth of a machine intelligence project so long as it is operated by humans. An AI system, however, might avoid some of these scale diseconomies, since the AI’s modules (in contrast to human workers) need not have individual preferences that diverge from those of the system as a whole. Thus, the AI system could avoid a sizeable chunk of the inefficiencies arising from agency problems in human enterprises. The same advantage—having perfectly loyal parts—would also make it easier for an AI system to pursue long-range clandestine goals. An AI would have no disgruntled employees ready to be poached by competitors or bribed into becoming informants.
Ok I see, thanks for explaining. I think what’s confusing to me is that Eliezer did stop talking about the deep math of intelligence sometime after 2011 and then started talking about big blobs of matrices as you say starting around 2016, but as far as I know he has never gone back to his older AI takeoff writings and been like “actually I don’t believe this stuff anymore; I think hard takeoff is actually more likely to be due to EMH failure and natural lag between projects”. (He has done similar things for his older writings that he no longer thinks is true, so I would have expected him to do the same for takeoff stuff if his beliefs had indeed changed.) So I’ve been under the impression that Eliezer actually believes his old writings are still correct, and that somehow his recent remarks and old writings are all consistent. He also hasn’t (as far as I know) written up a more complete sketch of how he thinks takeoff is likely to go given what we now know about ML. So when I see him saying things like what’s quoted in Rob’s OP, I feel like he is referring to the pre-2012 “deep math” takeoff argument. (I also don’t remember if Bostrom gave any sketch of how he expects hard takeoff to go in Superintelligence; I couldn’t find one after spending a bit of time.)
If you have any links/quotes related to the above, I would love to know!
(By the way, I was was a lurker on LessWrong starting back in 2010-2011, but was only vaguely familiar with AI risk stuff back then. It was only around the publication of Superintelligence that I started following along more closely, and only much later in 2017 that I started putting in significant amounts of my time into AI safety and making it my overwhelming priority. I did write several timelines though, and recently did a pretty thorough reading of AI takeoff arguments for a modeling project, so that is mostly where my knowledge of the older arguments comes from.)
Thanks! My understanding of the Bostrom+Yudkowsky takeoff argument goes like this: at some point, some AI team will discover the final piece of deep math needed to create an AGI; they will then combine this final piece with all of the other existing insights and build an AGI, which will quickly gain in capability and take over the world. (You can search “a brain in a box in a basement” on this page or see here for some more quotes.)
In contrast, the scenario you imagine seems to be more like (I’m not very confident I am getting all of this right): there isn’t some piece of deep math needed in the final step. Instead, we already have the tools (mathematical, computational, data, etc.) needed to build an AGI, but nobody has decided to just go for it. When one project finally decides to go for an AGI, this EMH failure allows them to maintain enough of a lead to do crazy stuff (conquistadors, persuasion tools, etc.), and this leads to DSA. Or maybe the EMH failure isn’t even required, just enough of a clock time lead to be able to do the crazy stuff.
If the above is right, then it does seem quite different from Paul+Katja, but also different from Bostrom+Yudkowsky, since the reason why the outcome is unipolar is different. Whereas Bostrom+Yudkowsky say the reason one project is ahead is because there is some hard step at the end, you instead say it’s because of some combination of EMH failure and natural lag between projects.
Which of the “Reasons to expect fast takeoff” from Paul’s post do you find convincing, and what is your argument against what Paul says there? Or do you have some other reasons for expecting a hard takeoff?
I’ve seen this post of yours, but as far as I know, you haven’t said much about hard vs soft takeoff in general.
(I have only given this a little thought, so wouldn’t be surprised if it is totally wrong. I’m curious to hear what people think.)
I’ve known about deductive vs inductive reasoning for a long time, but only recently heard about abductive reasoning. It now occurs to me that what we call “Solomonoff induction” might better be called “Solomonoff abduction”. From SEP:
It suggests that the best way to distinguish between induction and abduction is this: both are ampliative, meaning that the conclusion goes beyond what is (logically) contained in the premises (which is why they are non-necessary inferences), but in abduction there is an implicit or explicit appeal to explanatory considerations, whereas in induction there is not; in induction, there is only an appeal to observed frequencies or statistics.
In Solomonoff induction, we explicitly refer to the “world programs” that provide explanations for the sequence of bits that we observe, so according to the above criterion it fits under abduction rather than induction.
What alternatives to “split-and-linearly-aggregate” do you have in mind? Or are you just identifying this step as problematic without having any concrete alternative in mind?
There is a map on the community page. (You might need to change something in your user settings to be able to see it.)
I’m curious why you decided to make an entirely new platform (Thought Saver) rather than using Andy’s Orbit platform.
Messaging sounds good to start with (I find calls exhausting so only want to do it when I feel it adds a lot of value).
Ah ok cool. I’ve been doing something similar for the past few years and this post is somewhat similar to the approach I’ve been using for reviewing math, so I was curious how it was working out for you.
Have you actually tried this approach, and if so for how long and how has it worked?
So there’s a need for an intermediate stage between creating an extract and creating a flashcard. This need is what progressive highlighting seeks to address.
I haven’t actually done incremental reading in SuperMemo so I’m not sure about this, but I believe extract processing is meant to be recursive: first you extract a larger portion of the text that seems relevant, then when you encounter it again the extract itself is treated like an original article itself, so you might extract just a single sentence, then when you encounter that sentence again you might make a cloze deletion or Q&A card.
Thanks. I read the linked book review but the goals seem pretty different (automating teaching with the Digital Tutor vs trying to quickly distill and convey expert experience (without attempting to automate anything) with the stuff in Accelerated Expertise). My personal interest in “science of learning” stuff is to make self-study of math (and other technical subjects) more enjoyable/rewarding/efficient/effective, so the emphasis on automation was a key part of why the Digital Tutor caught my attention. I probably won’t read through Accelerated Expertise, but I would be curious if anyone else finds anything interesting there.