broken boat

Epistemics (Part 1: Introduction)

It is the sense of power attached to a sense of knowledge that makes men desirous of believing, and afraid of doubting. This sense of power is the highest and best of pleasures when the belief on which it is founded is a true belief, and has been fairly earned by investigation. … But if the belief has been accepted on insufficient evidence, the pleasure is a stolen one. Not only does it deceive ourselves by giving us a sense of power which we do not really possess, but it is sinful, because it is stolen in defiance of our duty to mankind. That duty is to guard ourselves from such beliefs as from pestilence, which may shortly master our own body and then spread to the rest of the town.

William Clifford, “The ethics of belief”
Listen to this post

1. About this series

Effective altruists use the term `epistemics’ to describe practices that shape knowledge, belief and opinion within a community. The term is understood broadly enough to encompass diverse categories such as forms of publication; discourse norms; institutional structures; attitudes towards authority; and means of reporting uncertainty.

Effective altruists care a lot about epistemics. They want to have good epistemics and learn to avoid bad epistemics. I think that in some ways, effective altruists already have good epistemics. In other ways, community epistemics could do with improvement.

This series will focus on areas in which community epistemics could be productively improved. Today, I want to introduce the series by giving three examples of the topics that I will be concerned about. Let me emphasize that these are only sketches of topics to be covered in the future: they are intended as previews, not as complete outlines of claims about community epistemics, and certainly not as full defenses of those claims.

2. Money talks

Effective altruists have at their fingertips billions (previously tens of billions) of dollars of pledged funding. They use this funding to influence the direction of research and debate. Effective altruists have established research institutes at major universities including Oxford and Cambridge, and funded lavish prizes including $100k in prizes for papers presented at a single conference symposium, and $1.5m for short arguments about the risks posed by artificial intelligence.

Money talks, and this kind of money introduces at least three types of distortions in the direction of research and discussion. First, there is an inflation of authoritativeness and seriousness in which money is used to buy markers of prestige for authors and topics that would otherwise be taken less seriously within debates. Research institutes at leading universities and purchased symposia at major machine learning conferences lend an aura of respectability to a movement often driven more strongly by blogs and forum posts than by scholarly research.

Second, philanthropic spending leads to topical biases in research: topics such as existential risk and AI safety are rocketed to the top of research agendas, not because scholars judge them to be the most serious or pursuit-worthy topics, but simply because research on these topics is more likely to be funded. Scholars willing to do work on these topics find themselves with prime, research-only positions at institutes flush with cash, and these conditions are attractive to many researchers who would otherwise prefer to study something else.

Finally, philanthropic funding leads to opinion biases in which the opinions expressed in published research are skewed towards those popular with funders. Opinion biases can be produced intentionally, as scholars seek to ingratiate themselves with funders. But opinion biases can also be produced unintentionally, as funders seek out and reward work that resonates with them.

I have said before that one thing I admire about effective altruists is their willingness to support scholarly research. Perhaps epistemic distortions are the price to pay for scholars’ full bellies. But even so, we should be honest about what foundations purchase in return for research funding, and how these purchases can shape research in directions unrelated to, or even contrary to truth or scholarly interest.

3. Publication practices

Publication practices matter. The way in which material is published affects its likely audience; how the material will be vetted and evaluated; and what it is likely to discuss. For example, this blog differs from my scholarly work in targeting a wider and more generalist audience; being largely unvetted; and allowing me to discuss a wide range of material that would not be of interest to leading academic journals.

Effective altruists often disseminate their work using a variety of nonscholarly media such as blog posts, forum discussions, podcasts, and reports commissioned and published by philanthropic foundations. While these media have advantages, such as accessibility, they also present unique epistemic challenges.

One question concerns the nature of peer review. Scholarly work is anonymously vetted by independent experts at the cutting edge of their fields, who are given the power to accept, reject, or demand changes to the research they evaluate. By contrast, work published by effective altruists is often unvetted, and even when reviews are commissioned, reviewers often lack terminal degrees, have limited publication records, are ideologically aligned with effective altruists, and are given no power to reject or alter the contents of research.

Another question concerns the evidentiary practices used by different publication media. For example, nonscholarly publications often make substantially fewer citations to other work and often cite their claims to other nonscholarly publications rather than to scholarly articles and books. It may be worth asking how these practices can serve to strengthen or weaken the collective evidential basis for claims made by effective altruists, as well as whether they might serve to promote a coalescing of opinion or a false sense of consensus driven by an overlapping network of citations to similarly-motivated authors.

A final question concerns the authorship of publications. Many materials circulated by effective altruists, including a good number of canonical texts, are written by authors lacking terminal degrees in their fields. These authors are frequently young, inexperienced, and lack substantial records of scholarly publication. Outside observers might be forgiven for asking whether these authors are qualified to be making confident claims about subject matters which often overlap with traditional research programs and whether it might be appropriate to show more deference to traditional scholarly work than to publications authored by untrained authors in less reliable formats.

4. Deference and authority

Effective altruism is, in many ways, a remarkably democratic and egalitarian movement. Anyone can post on the EA Forum and have their ideas engaged with by the community. But although anyone can speak, not everyone’s voice is treated equally. There are a number of celebrity figures within the movement whose opinions are much more likely to be read and believed than the opinions of average members.

Many of the questions to be asked about deference and authority have nothing to do with the specific figures to whom authority is granted. For example, too much trust in authority may allow false or undersupported claims to go unchallenged; may promote premature convergence of opinion; and may worsen citation practices as the opinions of authorities begin to be treated as legitimate citations in their own right.

However, at times we should also ask whether the specific individuals granted authority are deserving of our trust. For example, Eliezer Yudkowsky has a track record of bombastic and inaccurate predictions and has been known to contradict scholarly consensus on the basis of what turned out to be elementary confusions. Now, Yudkowsky is taken quite seriously when he urges that humanity has lost the battle for survival against artificial intelligence and should shift the focus of our efforts towards dying with dignity. Does such an individual, or such a claim, deserve the level of deference and respect shown within the community?

Sometimes, questions about the role of EA celebrities go beyond trust and become questions of amplification. Should the voices of leading figures be amplified, even when those doing the amplifying suspect that those voices are wrong? What kind of epistemic price is paid when the voices amplified hold views unsupported by the best available evidence?

5. Looking forward

In addition to the issues discussed above, this series will discuss many other questions about epistemics. For example:

  • In what ways may biases of group deliberation such as familiar biases of self-selecting, homogenous, internet-driven groups lead to failures of group deliberation?
  • May the community’s focus on probabilization and statements of credence at times serve to over-represent authors’ certainty, or to foster unwarranted convergence of opinion within the community?
  • What role do canonical texts play within the community? Do these texts deserve to play the role that they have been given?
  • Might views imported from Silicon Valley, such as a preference for longshots, quantification, and technology-driven solutions, be given undue weight in new areas to which they are less well-suited?

Please do let me know if there are other questions about epistemics that you would like to hear discussed.


Posted

in

by

Comments

10 responses to “Epistemics (Part 1: Introduction)”

  1. Jason Avatar
    Jason

    Thanks for starting this series — I think it will have a lot of value. As a reaction to the opening post, I thought I’d share a few thoughts that might be relevant to some future installments. The first two are specifically related to AI but may also apply in certain other areas as well.

    * For AI issues in particular: Money indeed does talk, and there’s a lot of money in AI (e.g., Microsoft just committed $10B, and I expect the overall spend in AI capabilities research to continue accelerating). The salaries can be (in the NYT’s words, even back in 2018) “eye-popping.” As can be shown from comparing what typical faculty members in medicine and law can command versus those in sociology or English literature, the private-sector market demand has a significant effect on what non-profits need to pay for talent. I think that’s relevant to assessing the total spend in the AI area. Is EA money distorting the field of AI research or correcting for the distortions caused by the big AI firms’ profit motives? (Also, it’s unlikely that that $1.5MM contest would have paid out anywhere near $1.5MM.)

    * The existence of all that private-sector money (and potential massive profits from AI) has and will continue to have a massive influence on what gets researched, talked about, seen as prestigious, etc. So one could look at EA activity in this area through the lens of a balancing effect — that raising the prominence of AI-skeptical viewpoints is necessary to counteract the all the influence from corporations with a vested interest in AI and restore some sort of neutral balance to the study of AI.

    * I think it would be helpful to acknowledge the epistemic challenges of the academy as well, at least in passing. Although I think it is a helpful comparison to show some weakness of EA epistemic practices, I am less convinced that it is a gold standard. In my own field (law), I could tell you which topics and viewpoints are more likely to get you ahead in legal academia by playing to the views of people who are already tenured law professors. (Note that most US law professors do not have advanced law degrees, so this is not a function of the profesoriate being better educated than other highly-qualified lawyers.) The academic system grants pretty strong deference to the tenured faculty member regarding what is worthy of study. That has its advantages, but there’s no way to enforce that the work be socially useful — and people may choose research topics and viewpoints for any number of potentially idiosyncratic reasons. And to the extent that there is a source of significant influence over tenured faculty research agendas, a lot of it comes from what external funders are willing to fund (which raises some of the same concerns as your article does). To be sure, academic epistemics may be on more solid ground overall because power and influence is more decentralized, but they have some downsides as well.

    1. David Thorstad Avatar

      Thanks Jason! These are important points to think about.

      **Does money talk in AI?**

      I would tend to agree with you that money talks in AI research. To take an example close to home, a very large number of jobs on the academic job market in philosophy suddenly call for specializations in the philosophy of AI, and indeed my own job was secured largely on my ability to relate my research to normative issues raised by AI. Philosophers are at once genuinely thankful for the funding and also concerned about the effects it may have on the direction of research within the field if we are not careful to retain our emphasis on core areas, questions and insights in philosophy. We think we can take the money while retaining the field’s emphasis on traditional questions, methods, standards and the like, but we think that some care is needed to make sure that really happens.

      I guess you know that it is controversial whether most EA writings about the risks posed by artificial intelligence should be considered as contributions to AI research. Many AI researchers would not see them in this way.

      It might also be controversial whether and to what extent AI companies have a strong ideological agenda to push when funding research. They may largely be interested simply in advancing the state of technological capabilities. I suspect EAs might say that companies are committed to advancing an “AI is safe” agenda, analogous to the “tobacco is safe” agenda of tobacco companies a half-century ago. I suspect many observers would not accept this characterization, both because they do not see tech companies (by contrast to tobacco companies) investing much money in trying to convince us that their products won’t kill us, and also because they might see the safety of AI as a claim on rather firmer epistemic ground.

      **On EA money as a balancing act against private money**

      It is definitely possible for private philanthropy to balance the playing field within a research area. I would tend to resist the characterization of EA money as balancing the research playing field within AI. Largely, I say this for the reasons sketched above: I’m not sure that I would see most EA writings about AI as contributions to the field of AI research. It might be helpful to stress two additional points.

      One point is that EA money, like AI research money, often comes from Silicon Valley. To this extent, it might be fair to view EA funding as a continuation of, rather than a counterbalance to, the research influence of Silicon Valley.

      Another point to stress is that effective altruism is far from exhausted by what the movement says about AI. When we move into other fields, the argument that there is already a massive amount of opposed money to counterbalance EA arguments tends to weaken.

      **On epistemic challenges of the academy**

      It is definitely true that academia faces epistemic challenges, and some fields face more challenges than others. The purpose of this series is to point out some epistemic challenges raised by effective altruism in the interest of solving them when they are fixable, and when they are not fixable, helping to make sure people are aware of and appropriately compensate for potential biases.

  2. S Avatar
    S

    You’ve hit upon my bugbear. I keep a distance from EA circles (online and offline) to, ironically, protect my own epistemics. When I first encountered them, I was pleasantly surprised by their degree of thoughtfulness and reflection. But it dawned over time that this is also one of the most insular groups I’ve ever encountered. It’s not just publication practices — for a significant chunk, nearly their entire social life revolves around EA.

    A very eye-opening moment was when a skeptical acquaintance remarked that the main correlate for becoming an AI doomer was hanging out with EAs/rationalists rather than anything to do with knowledge of the subject matter itself. I myself have a relevant PhD and research experience and feel out of place for not being a “believer”.

    Even with active awareness, we can’t protect ourselves from our primate instincts to conform. It’s strange that a community that prides itself on navigating cognitive biases can’t see this risk. Now equipped with a full-blown eschatology, we’re set for religion-level degrees of insularity. I still appreciate (aspects of) EA, but the only solution I found was to keep interaction to an acceptable minimum.

    1. David Thorstad Avatar

      Thanks S.

      I hope more people see this comment!

  3. River Avatar
    River

    You use the word “scholarly” a lot, so my first question is what exactly do you mean by that? I would hope you mean discovery of truth, otherwise why should I care about “scholarly” things? But you also list it side by side with truth, which suggests you mean something else.

    The impression I get is that you are just going to evaluate EA’s epistemics by how closely it matches the academy, and for that to be useful, you need to make a case that what the academy does is right, in the sense of leading to truth. You can’t take that as a given. To illustrate this point, compare physicists to theologians. (If you are religious, imagine I am talking about the theologians of someone else’s religion.) One succeeds at discovering truth, the other does not, yet both follow the forms of the academy. How do we know? Because physicists build cool things like space ships and particle colliders, and theologians don’t. Ultimately out knowledge about what epistemic norms work and what epistemic norms don’t has to be grounded in empirical observations, just like anything else. For your argument to work, you need to ground your claims about epistemic norms in empirical observations, not just the observation that the academy uses them.

    Also, you do realize that in practice, a lot of “peer” review is actually delegated to random grad students, who lack terminal degrees, who do not have long publication records, and who sometimes leave the academy to become the EAs you are discussing, right?

    1. David Thorstad Avatar

      Hi River,

      Scholarship certainly involves the pursuit of truth, but it also involves a good deal more than that. Some things generally associated with sound scholarship include a high degree of rigor, carefulness, and thoughtfulness in reasoning; a strong knowledge of background literatures; disciplinary grounding in cutting-edge knowledge and methods from the relevant scholarly discipline; advanced training involving a course of graduate education at the hands of leading scholars; structured assessment of a specific type (typically peer review); detailed citation to relevant literature; appropriate deference to scholarly consensus; and the like.

      Early effective altruists recognized the importance of relying on cutting-edge scholarship to guide altruistic decisionmaking. In areas such as global health and development, only the best randomized controlled trials would do for early effective altruists. The recent turn to longtermism has brought something of a liberalization of research standards and in some corners even a tendency to attack cannons of scholarly knowledge, along with associated methods of research and assessment. This is not a good look for those longtermists. It is no way to reliably reach the truth, and no way to guide altruistic decisionmaking.

      I am intimately involved in the practice of peer review and I am aware of the broad shape that this practice takes at leading journals. While some leading journals do commission graduate students as reviewers, no leading journal commissions reviewers randomly. When graduate students are chosen as reviewers, they are chosen for their specific expertise combined with either a recognition of their scholarly achievements or else the recommendation of a trusted scholar.

      To be honest, I often think that late-stage graduate students make very good reviewers. When they are appropriately knowledgeable and skilled, they tend to put a good deal of effort into the review process and give detailed comments on the work that they review. Graduate students can sometimes be a bit overzealous in posing challenges to the work that they review, but in general, I would be very happy to have my work reviewed by an appropriately selected late-stage graduate student with the relevant skills and knowledge.

  4. yarrow Avatar
    yarrow

    I loved this post. It makes many great points, and it has some really helpful citations. For instance, the Effective Altruism Forum post that describes how, among other things, Eliezer Yudkowsky believed in 2001 that he and a few colleagues would invent would invent artificial general intelligence within about 10 years, 20 years tops, which of course didn’t happen. I think I read that post a long time ago but completely forgot about that part.

    Around 2015-2017, I had an interaction with Yudkowsky in an EA-related Facebook group about existential risk from artificial intelligence. I think I asked something along the lines of why MIRI didn’t focus more on machine learning, since this is where the AI field was going and where AI had had the most empirical success. He said that he didn’t think neural networks were the royal road to AGI (how things have changed — in his 2023 TED Talk, he says the complete opposite) and that if he had to guess, symbolic AI would be more likely to get to AGI. This struck me as preposterous at the time, since symbolic AI had been such a bust, empirically, for such a long time, and was considered forlorn by seemingly the majority of experts, not just in AI but in philosophy as well. By the time Yudkowsky and I had this exchange, machine learning had much more to recommend it than symbolic AI. For instance, in 2014, DeepMind showed off its deep reinforcement learning agent that could play Atari games. At the time, that seemed mind-blowing.

    To be clear, I didn’t think machine learning offered a path to AGI, and I still don’t. (I also didn’t buy MIRI’s existential risk arguments for unrelated reasons, and I still don’t buy them.) But it just seemed so strange that if you had to pick between symbolic AI and machine learning, you would pick symbolic AI.

    Yudkowsky has been oddly quiet on this topic in any public source I can find. I know that in 2008 he wrote a post on LessWrong expressing strong skepticism about neural networks. By his 2023 TED Talk, he has gone completely in the other direction. I can’t find any public discussion during the 15-year period where he explains when his view changed or why. This is a conspicuous absence for someone for whom detailed discussion of AI and AGI is his primary life’s work, and who is willing to weigh in, sometimes at great length, about many topics, including some he says he believes are much less important than AI. What gives?

    I have tried searching extensively, and I can’t find anything. I’ve asked people on LessWrong and the EA Forum, and nobody seems to have a clearer answer than I have. I would love to hear his explanation. As noted in the EA Forum post I mentioned above and in one of the comments on that post, in terms of credibility, authority, or trust, it’s highly suspect that Yudkowsky doesn’t comment on or explain his intellectual track record, specifically the mistakes or where he’s changed his mind. Yet he also seems at times to insist that people listen to him because of his track record or to trust his judgment, even if you don’t understand his reasoning. It’s a different topic, but this reminds me of the obscurantism and mysticism you can find in a lot of his writing. All of these are parts of the recipe he’s used to gain a devoted following that fiercely defends him, whether intentionally or not, whether consciously or not.

    Anyway, this post is great and Reflective Altruism is an excellent blog. It’s too bad more people involved in effective altruism don’t read it. Someone should make a forum bot that automatically cross-posts all the new posts to the EA Forum.

    1. David Thorstad Avatar

      Thanks Yarrow! I really appreciate the kind words.

      That’s an interesting note about Yudkowsky’s views on AI. I hadn’t heard of that. I think that is a nice way of adding fuel to the fire about just how hard it is to predict the future of AI, and also perhaps of reminding us that the very same people cashing in on some true predictions also made some false predictions.

      On readership, I’m always very happy to see cross-posts and happy to help make this easier if anyone ever wants to do it (and I’m not opposed to a bot). My practice is not to directly cross-post myself, because I want to be able to write in a way that is not directed only at EA audiences. I guess you may have noticed lately in some of your EA Forum posts that there are some topics on which EA-adjacent audiences can have quite strong views and reactions, and I find it hard to always write in a way that will appeal to EA-adjacent audiences without driving my other readers (and sometimes my own interests) away. But I’m very happy to see cross-posts as well as mentions on the EA Forum, and I think that this kind of organic readership growth can be a good way of reaching EA audiences without changing the content or style of what I want to say.

      At the same time, I should say that most of my readers are EA-adjacent, and a number of core EAs are subscribed to this blog. I’m fairly impressed with the number of EA-adjacent readers who do regularly read my work, including not only this blog but also some fairly technical academic papers.

      1. yarrow Avatar
        yarrow

        Has Yudkowsky made any correct predictions? What people tend to cite is that he correctly predicted AGI/AI risk would be a big deal, but this is not a prediction that has been objectively resolved, and it’s partly circular, since part of the reason the people who say this believe AGI/AI risk is a big deal is because Yudkowsky said so (and founded an organization and a community that further perpetuates this message).

        I think rather than coding a bot, it might actually be faster and easier to just manually cross-post to the EA Forum. Although I thought it might be funny to call the account “Unofficial Reflective Altruism Bot” or something and then put an image of the Mechanical Turk on the profile.

        I’m happy to hear your EA/EA-adjacent readership is already good. I guess it’s hard to tell how many people are reading just from the comments. Still, I feel more people could stand to read this blog! It’s hard to think of any higher-quality critical appraisals of or skeptical takes on EA ideas.

      2. yarrow Avatar
        yarrow

        I want to flag that I misremembered some important details about my exchanges on Facebook with Eliezer Yudkowsky and others at MIRI around 2016-2018. I went through the trouble of digging up those ancient posts to fact check my memory. I had exchanges with both Eliezer Yudkowsky and Rob Bensinger, MIRI’s Head of Research Communications. In my recollection above, I mixed up what Yudkowsky said with what Bensinger said. I also mixed up symbolic AI with ‘de novo AI’.

        This comment is going to be long and will go into more detail than a lot of people probably want to know, but I feel an obligation to correct and clarify the details. (I will try to make sure I also correct myself anywhere else I’ve shared this anecdote before.)

        The part of my memory that (if I’m understanding everything correctly) is confirmed by the old posts I’ve found is that MIRI seemed to be downplaying deep learning as late in the game as early 2016. MIRI pivoted toward more of a research focus on deep learning in or around mid-2016, possibly in response to AlphaGo winning against Lee Sedol in March 2016. (I’ve seen someone speculate that AlphaGo catalyzed Eliezer Yudkowsky’s shift of thinking on deep learning and Yudkowsky has one or more strongly worded Facebook posts about AlphaGo from back in 2016.)

        A big distinction that was talked about in the mid-2010s was opaque vs. transparent AI, i.e. whether AI is an inscrutable black box or an interpretable, explainable white box. MIRI much preferred transparent AI. My argument was that the human brain is opaque and deep learning is opaque, and that as AI became more capable and more human-like over time, it would probably become even more complex and opaque than deep learning. MIRI’s bias or tendency in this era was toward either thinking that a) AGI or AI with advanced capabilities would be relatively more transparent or b) that the scenarios in which AGI/advanced AI is relatively more transparent would be more dangerous and hence more worth focusing on. This stance seems to have flipped on its head in more recent years, or at least changed in a way that’s hard to follow. (An overarching problem here is that Yudkowsky and others at MIRI tend to be cagey or oblique about what they actually think, so any exercise in trying to follow how their views on AI have changed — or not changed — over the years frustratingly involves some reading of the tea leaves. As I said in my comment above, obscurantism and even some mysticism are the stock and trade of much of Yudkowsky’s long-form writing, and some of Yudkowsky’s followers follow suit with that style.)

        To corroborate my point about Yudkowsky’s and MIRI’s views on deep learning, Clara Collier said much the same in an essay published in Asterisk Magazine. (I don’t know if I can post links here, so just Google the title: ‘More Was Possible: A Review of If Anyone Builds It, Everyone Dies’.) Collier wrote:

        “We’ve learned a lot since 2008. The models Yudkowsky describes in those old posts on LessWrong and Overcoming Bias were hand-coded, each one running on its own bespoke internal architecture. Like mainstream AI researchers at the time, he didn’t think deep learning had much potential, and for years he was highly skeptical of neural networks. (To his credit, he’s admitted that that was a mistake.) But If Anyone Builds It, Everyone Dies very much is about deep learning-based neural networks. The authors discuss these systems extensively — and come to the exact same conclusions they always have. The fundamental architecture, training methods and requirements for progress for modern AI systems are all completely different from the technology Yudkowsky imagined in 2008, yet nothing about the core MIRI story has changed.”

        When Collier says Yudkowsky admitted this was a mistake, I don’t know if she’s referring to anything Yudkowsky has explicitly said, or just acknowledging that he’s implicitly admitted this was a mistake by talking up deep learning in the post-AlphaGo era. I suspect it’s the latter.

        I think ‘hand-coded AI’ might be a more useful and accurate term for what I’ve been getting at about MIRI’s old views than ‘symbolic AI’, which is more narrow than ‘hand-coded AI’. For a long time, Yudkowsky and MIRI seemed to think hand-coded AI could lead to AGI in a relatively short timeframe — indeed, a younger Yudkowsky apparently thought he and a few colleagues could build it themselves by around 2010 — and that’s the part I was always skeptical about, and about which I expressed skepticism on multiple occasions in those effective altruism and AI safety groups on Facebook. It always seemed to be that anything with human-level intelligence would need to be at least a small fraction as complex as the human brain, if not equally complex, and even a small fraction of the human brain would be way more complex than any hand-coded software system.

        To put it crudely, I don’t think a glorified Deep Blue could ever be sentient or capable of taking over the world (or automating all labour), and I never saw MIRI’s arguments as plausible for that reason. MIRI and Yudkowsky have pivoted to talking about deep learning in the post-AlphaGo era now that deep learning is ascendant in industry, academia, and the popular imagination, but they seem to be, in my perception, riding the coattails of this trend that, largely or partly, goes against what they were saying previously, without explicitly acknowledging the shift between before and after, and also while saying, ‘See? I was right all along.’ This doesn’t really feel transparent or honest to me. At the very least, it’s confusing and it seems like the kind of thing MIRI and Yudkowsky should have spent 10,000+ words explaining by now. And certainly not nothing. The silence is weird. I could potentially give someone the benefit of the doubt in this situation, but Yudkowsky has a long track record of bold claims that are either externally disconfirmed or that he apparently changed his mind on, without any meaningful acknowledgement or explanation from him. Effective altruists prize reasoning transparency (for good reason, I think), and this is deep reasoning opaqueness.

        Related to this pattern of opaqueness, Yudkowsky has an odd habit of scorning others for AGI forecasts that put the median year for AGI too far in the future, but explicitly refuses to give his own forecast. This leaves people who want to know what Yudkowsky thinks about when AGI will arrive to read the tea leaves, which is just bafflingly obscure way to approach the topic for someone who is perhaps the leading prophet of AI doom. I sort of get the impression that Yudkowsky, whether consciously or subconsciously, may write in such a way to minimize the risk he can ever be proven wrong about anything, and to maximize his opportunities to gloat about being right. I find that a lot of Yudkowsky’s communication lacks transparency, openness, and directness (perhaps partly influenced by his apparent belief in secrecy at MIRI). It’s hard to even track Yudkowsky’s or MIRI’s views over the last 10-20 years because it’s so hard to find out what those views currently are or what they ever were at various points in the past.

        To completely tip my hand about my (uncertain, inchoate) hunch about Yudkowsky: I think he is fulfilling the role of a mystic, but a troubling version of one. The benign, positive version of a mystic would be someone like the Franciscan friar Richard Rohr or the theologically liberal preacher Rob Bell, or various other spiritual thinkers and leaders who have advocated non-violence, compassion, and religious tolerance. There is something important for the human mind to grapple with beyond the sort of logic or reasoning one typically sees in analytic philosophy (or similar domains), and various spiritual traditions (or wisdom traditions) have said that. It’s also part of the modern interest in psychedelics. By contrast, Yudkowsky’s mysticism seems to serve to glorify himself (e.g. he believes he’s a singular person on Earth with the best chance by far of saving the world from an AI apocalypse due to his unrivalled natural intellectual gifts — a sort of Chosen One), and I believe part of its appeal for his followers in the LessWrong community is the sense of belonging to an elite club with a cosmic destiny. (The organization that runs LessWrong is called “Lightcone”!)

        Yudkowsky is good enough at some of the rhythms of mystical speech and writing to captivate some number of people — he entices readers with an air of mystery, adopting a role akin to an old Zen master — but it’s in service of something ugly. On this interpretation of Yudkowsky, the obscuration and opaqueness is part of the point, part of the role of a mystic. Mystery is enticing, and reasoning transparency is boring — and often disappointing, since it will show your guru to be human and fallible. To try to interpret Yudkowsky on a purely rational level, the way one would interpret a typical analytic philosopher or an economist or a machine learning researcher would likely miss something important about what he’s doing.

        To repeat, this is just an uncertain, inchoate hunch. I don’t know how confident I am in this interpretation of Yudkowsky and LessWrong. There are other potential explanations — there is obscure writing in some areas of academia and there are public figures who are cagey about what they say publicly or what mistakes they admit, and neither of these seem to be explained by mysticism. But I figured it would be worth putting out there. Others may be able to find some use in this idea.

Leave a Reply

Discover more from Reflective altruism

Subscribe now to keep reading and get access to the full archive.

Continue reading