Luke Muehlhauser

Media diet for Q1 2024

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Music

Music I most enjoyed discovering this quarter:

I also watched many YouTube “reaction” videos for my two favorite vocal performances of all time: Ankudinova’s “Can’t help falling in love” and Dimash’s “S.O.S.” Lots of warranted shock, goosebumps, speechlessness, and weeping on display. A few examples: one, two, three, four, five, six, seven, eight, nine. YouTuber “Fairy Voice Mother” has excellent explainers on how each performance is achieved technically: Ankudinova, Dimash. You may also enjoy her reaction/analysis for Johnny’s Cash’s “Hurt.”

Another very impressive vocal performance I discovered this quarter is Will Ramos’ “To the Hellfire,” but I can’t exactly say I “enjoy” it, so I didn’t list it above. How many different species of demon do you hear?

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  1. Unsurprisingly I also like the arrangement for orchestra. [↩]
  2. Still cool even though the highest whistle notes appear to be manipulated. [↩]

Media diet for Q4 2023

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Music

Music I most enjoyed discovering this quarter:

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Media diet for Q3 2023

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Music

Music I most enjoyed discovering this quarter:

This quarter, I was reminded that I often like orchestral arrangements of originally non-orchestral pieces (by a different composer). Here are some favorites, most of which I discovered for the first time this quarter [playlist]:

  • Leopold Stokowski (as arranger): Bach’s Toccata and Fugue in D Minor and “Sheep May Safely Graze” and “Jesu, Joy of Man’s Desiring,” Rachmaninov’s “The Bells of Moscow,” Clarke’s “The Prince of Denmark’s March,” Mozart’s “Turkish March,” Beethoven’s Moonlight Sonata mvt 1, Franz Schubert’s “Serenade,” Chopin’s “Funeral March” and “Minute Waltz” and Preludes (Opus 28) No. 24, Albeniz’s “Festival in Seville,” Handel’s “Dead March,” Purcel’s “Dido’s Lament,” Mussorgsky’s “St. John’s Night on the Bare Mountain,” Debussy’s “Clair de Lune”
  • Ferde Grofé (as arranger): Gershwin’s Rhapsody in Blue
  • Henry Wood (as arranger): Grieg’s “Funeral March in Memory of Rikard Nordraak,” Rachmaninoff’s Prelude in C-sharp minor
  • Maurice Ravel (as arranger): Mussorgsky’s Pictures at an Exhibition
  • Henri Büsser: Debussy’s Petite Suite
  • Gustav Mahler (as arranger): Schubert’s String Quartet No. 14
  • Steven Stucky (as arranger): Purcell’s Music for the Funeral of Queen Mary
  • Arnold Schoenberg (as arranger): Brahms’ Piano Quartet No. 1
  • Benjamin Britten (as arranger): Purcell’s Chacony in G Minor
  • Franz Liszt (as arranger): Schubert’s Wanderer Fantasy
  • Erwin Schulhoff (as arranger): Beethoven’s “Rage Over a Lost Penny”
  • Claud Debussy (as arranger): Satie’s Gymnopédies
  • Francis Poulenc (as arranger): Satie’s Gnossiennes No. 3
  • Gustav Holst (as arranger): Bach’s Fugue a la gigue in G Major [BWV 577]
  • Eugene Ormandy (as arranger): Bach’s “Sleepers Wake” [BWV 140]
  • Various arrangers: Brahms’ Hungarian Dances

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  1. Of course this is closer to a “most popular” list than a “greatest” list, but regardless, these pieces are great, and do seem to me like a great selection for introducing someone to classical music. [↩]

Media diet for Q2 2023

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Music

Music I most enjoyed discovering this quarter:

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Media diet for Q4 2022

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Music I most enjoyed discovering this quarter:

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Media diet for Q3 2022

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Music I most enjoyed discovering this quarter:

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Media diet for Q2 2022

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Music I most enjoyed discovering this quarter:

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  1. I really enjoy this, but did we really need another Richter “recomposition” of this exact piece before Richter has provided the same treatment to other beloved works? Could he next recompose Beethoven’s 9th or Stravinsky’s Rite of Spring or something else? [↩]

Effective altruism as I see it

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Here’s the main way I think about effective altruism, personally:

  1. I was born into incredible privilege. I can satisfy all of my needs, and many of my wants, and still have plenty of money, time, and energy left over. So what will I do with those extra resources?
  2. I might as well use them to help others, because I wish everyone was as well-off as I am. Plus, figuring out how to help others effectively sounds intellectually interesting.
  3. With whatever portion of my resources I’m devoting to helping others, I want my help to be truly other-focused. In other words, I want to benefit others by their own lights, as much as possible (with whatever portion of resources I’ve devoted to helping others). This is very different from other approaches to helping others, such as helping in a way that makes me feel good (e.g. a cause I have a personal connection to, or “giving back” to a community that has benefited me), or helping specific kinds of people that I feel special empathy for (e.g. identifiable victims, people with whom I share particular characteristics, or people who face particular deprivations that are salient to me), or helping in a way that allows me to achieve particular virtues, or helping in ways that aren’t scope-sensitive (e.g. spending $1 million to save one life via bone marrow transplant rather than spending the same amount to save ~220 lives via malaria prevention).1 I might do those other things too, but I wouldn’t count them as coming from my budget for other-focused altruism. (See also: Harsanyi’s veil of ignorance and aggregation theorem.)
  4. Okay, so what can I do that will benefit others by their own lights, as much as possible (with the other-focused portion of my resources)? Here is where things get complicated, drawing from domains as diverse as ethics, welfare economics, consciousness studies, global health, macrohistory, AI, innovation economics, exploratory engineering, and so much more. There will be many legitimate debates, and I’ll never be certain that I’ve come to the right conclusions about how to help others as much as possible, but the goal of all this research will remain the same: to figure out how to benefit others as much as possible and then devote my other-focused resources toward doing that.

In other words, I’m pretty happy with the most canonical definition of effective altruism I know of, from MacAskill (2019), which defines effective altruism as:

(i) the use of evidence and careful reasoning to work out how to maximize the good with a given unit of resources, tentatively understanding ‘the good’ in impartial welfarist terms, and

(ii) the use of the findings from (i) to try to improve the world.

This notion of effective altruism doesn’t demand that you use all your resources to help others. It doesn’t even say that you should use your other-focused budget of resources to help others as much as possible.2 Instead, it merely describes an intellectual project (clause i) and a practical project (clause ii) that some people are excited about but most people aren’t.3

Effective altruism is radically different from many other suggestions for what it looks like to do good or help others.4 True, the portion of resources devoted to helping others may not differ hugely (though it may differ some5 ) between an effective altruist and a non-EA Christian or humanist or social justice activist, since the canonical notion of effective altruism doesn’t take a stance on what that portion should be.6 Instead, effective altruism differs from other approaches to helping others via one or more of its defining characteristics, namely its aspiration to be maximizing, impartial, welfarist, and evidence-based.7

For example, I think it’s difficult for an effective altruist to conclude that the following popular ideas for how to do good or help others are plausible contenders for helping others as much as possible (in an impartial, welfarist, evidence-based way):

  1. Providing basic necessities (food, shelter, health care, education) to people who are poor by wealthy-country standards, at a cost that’s ≥100x the cost per person of providing those necessities to people who are poor by global standards. (Not maximizing, not impartial.)
  2. Funding for the arts. (Not maximizing: there is already more great art than anyone can enjoy in a lifetime, and the provision of marginal artistic experience benefits others much less than e.g. providing the poorest people in the world with basic necessities.)
  3. Religious evangelism, e.g. to spare souls from hell. (Not evidence-based.)
  4. Funding advocacy against GMOs or nuclear power. (Not evidence-based.)
  5. Funding animal shelters rather than efforts against factory farming, which tortures and slaughters billions of animals annually.8 (Not maximizing.)
  6. (Many, many other examples.)

Of course, even assuming effective altruism’s relatively distinctive joint commitment to maximization, impartialism, welfarism, and evidence, there will still be a wide range of reasonable debates about which interventions help others as much as possible (in an impartial, welfarist, evidence-based way), just as there will always be a wide range of reasonable debates about any number of scientific questions (and that’s no objection to scientific epistemology).9

Moreover, these points don’t just follow from the canonical definition of effective altruism; they are also observed in the practice of people who call themselves “effective altruists.” For example, EAs are somewhat distinctive in how they debate the question of how best to help others (the debates are generally premised on maximization, welfarism, impartialism, and careful interpretation of whatever evidence is available), and they are very distinctive with regard to which causes they end up devoting the most money and labor to. For example, according to this estimate, the top four EA causes in 2019 by funding allocated were:10

  1. Global health ($185 million)
  2. Farm animal welfare ($55 million)
  3. (Existential) biosecurity ($41 million)11 — note this was before COVID-19, when biosecurity was a much less popular cause
  4. Potential (existential) risks from AI ($40 million)

Global health is a fairly popular cause among non-EAs, but farm animal welfare, (existential) biosecurity, and potential (existential) risks from AI are very idiosyncratic. Indeed, I suspect that EAs are responsible for ≥40% of all funding for each of farm animal welfare, potential existential risks from AI, and existential biosecurity.12

  1. My estimate of the cost of a bone marrow transplant is taken from Millman (2020). My estimate for the cost of saving lives via malaria prevention is $4,500, which is GiveWell’s estimate for the average cost-effectiveness of GiveWell-directed funding to Malaria Consortium in 2020, taken from this page on June 4th 2022. I might be misunderstanding the cost estimate in Millman (2020), though in any case I suspect that basic point will stand, that paying for bone marrow transplants will save much fewer lives per dollar than funding malaria prevention via donations to Malaria Consortium. [↩]
  2. See MacAskill (2019), section 2. [↩]
  3. You could call this a “watered down” or “weak” version of moral realist utilitarianism, but it is not a watered down or weak version of effective altruism (as suggested by Nielsen). It is the primary, canonical notion of effective altruism, crafted with input from a survey of effective altruism “thought leaders” from a few years ago. [↩]
  4. Contra Srinivasan and Nielsen. [↩]
  5. It’s hard to get comparable numbers on this (or any numbers at all), but my anecdotal sense is that highly engaged EAs are substantially more likely to choose a “direct work” EA career than similarly engaged members of most other morally-motivated communities are, though of course it’s not at all rare in other morally-motivated communities. EAs might also on average donate a bit more than most other morally-motivated communities, though it’s worth noting that e.g. the median EA (in this survey) donates much less than 10% of their income. If anyone has numbers for any of these claims, let me know!

    Some EAs embrace a more morally demanding version of EA, even though strong moral demandingness is not part of the canonical definition of EA. I applaud and respect these EAs and think they are morally superior to me, but my sense is that they are in the minority of EAs, and of course many other morally-motivated communities also have a minority of practitioners who embrace an especially morally demanding lifestyle. [↩]

  6. See again MacAskill (2019), section 2. [↩]
  7. MacAskill (2019) uses the phrase “science-aligned” rather than “evidence-based,” and notes that effective altruism’s impartialism and welfarism is “tentative.” [↩]
  8. For context, Lewis Bollard estimates that “US animal rescue shelters had a combined budget of $3.2B in 2021. Shelters house 6.3M animals/year, so that’s ~$500 spent per shelter animal (often for a short period of time). US farm animal advocacy orgs had a combined budget of ~$100M, while the US has 2.7B farm animals alive at any time, so that’s $0.04 spent per farm animal.” [↩]
  9. Indeed, after making a small number of moral assumptions, questions about which interventions will help others the most just are, in a broad sense, scientific questions. [↩]
  10. See the same post for estimates of how much EA labor goes to different causes; that picture is harder to describe succinctly so I’ve skipped it here. [↩]
  11. By “existential biosecurity” I just mean “Biosecurity and pandemic preparedness interventions focused on avoiding existential catastrophe from pathogens,” which is generally the sort of biosecurity work that EAs fund. [↩]
  12. This is harder to determine for the case of existential biosecurity, since existential biosecurity interventions overlap a lot with lower-stakes biosecurity interventions, and biosecurity in general has become a more popular cause since COVID-19. On farm animal welfare, Lewis Bollard estimates that EA funders were responsible for perhaps ~45% for farm animal advocacy work in 2021, though Leah Edgerton’s estimate of 25% for 2018 may have been correct (EA funding in this area has increased in recent years). [↩]

Media diet for Q1 2022

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Music

Music I most enjoyed discovering this quarter:

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Media diet for Q4 2021

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Music

Spotify playlist for this quarter is here. Playlists for past quarters and years here.

Music I most enjoyed discovering this quarter:

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Media diet for Q3 2021

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Music

Spotify playlist for this quarter is here. Playlists for past quarters and years here.

Music I most enjoyed discovering this quarter:

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Musk’s non-missing mood

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Over the years, my colleagues and I have spoken to many machine learning researchers who, perhaps after some discussion and argument, claim to think there’s a moderate chance — a 5%, or 15%, or even a 40% chance — that AI systems will destroy human civilization in the next few decades.1 However, I often detect what Bryan Caplan has called a “missing mood“; a mood they would predictably exhibit if they really thought such a dire future was plausible, but which they don’t seem to exhibit. In many cases, the researcher who claims to think that medium-term existential catastrophe from AI is plausible doesn’t seem too upset or worried or sad about it, and doesn’t seem to be taking any specific actions as a result.

Not so with Elon Musk. Consider his reaction (here and here) when podcaster Joe Rogan asks about his AI doomsaying. Musk stares at the table, and takes a deep breath. He looks sad. Dejected. Fatalistic. Then he says:

I tried to convince people to slow down AI, to regulate AI. This was futile. I tried for years. Nobody listened. Nobody listened. Nobody listened… Maybe [one day] they will [listen]. So far they haven’t.

…Normally the way regulations work is very slow… Usually it’ll be something, some new technology, it will cause damage or death, there will be an outcry, there will be an investigation, years will pass, there will be some kind of insight committee, there will be rulemaking, then there will be oversight, eventually regulations. This all takes many years… This timeframe is not relevant to AI. You can’t take 10 years from the point at which it’s dangerous. It’s too late.

……I was warning everyone I could. I met with Obama, for just one reason [to talk about AI danger]. I met with Congress. I was at a meeting of all 50 governors, I talked about AI danger. I talked to everyone I could. No one seemed to realize where this was going.

Moreover, I believe Musk when he says that his ultimate purpose for founding Neuralink is to avert an AI catastrophe: “If you can’t beat it, join it.” Personally, I’m not optimistic that brain-computer interfaces can avert AI catastrophe — for roughly the reasons outlined in the BCIs section of Superintelligence ch. 2 — but Musk came to a different assessment, and I’m glad he’s trying.

Whatever my disagreements with Musk (I have plenty), it looks to me like Musk doesn’t just profess concern about AI existential risk.2 I think he feels it in his bones, when he wakes up in the morning, and he’s spending a significant fraction of his time and capital to try to do something about it. And for that I am grateful.

  1. Indeed, this is not a rare view among researchers who publish at some of the top AI conferences, at least as of 2015; see here. [↩]
  2. For collections of other AI-related quotes from Musk, see e.g. here and here, along with this panel. [↩]

Media diet for Q2 2021

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Music

Spotify playlist for this quarter is here. Playlists for past quarters and years here.

Music I most enjoyed discovering this quarter:

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Treacherous turns in the wild

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Bostrom (2014) worries about an AI “treacherous turn”:

…one idea for how to ensure superintelligence safety… is that we validate the safety of a superintelligent AI empirically by observing its behavior while it is in a controlled, limited environment (a “sandbox”) and that we only let the AI out of the box if we see it behaving in a friendly, cooperative, responsible manner. The flaw in this idea is that behaving nicely while in the box is a convergent instrumental goal for friendly and unfriendly AIs alike. An unfriendly AI of sufficient intelligence realizes that its unfriendly final goals will be best realized if it behaves in a friendly manner initially, so that it will be let out of the box. It will only start behaving in a way that reveals its unfriendly nature when it no longer matters whether we find out; that is, when the AI is strong enough that human opposition is ineffectual.

Some people have told me they think this is unrealistic, apparently even for a machine superintelligence far more capable than any current AI system. But I learned today1 that this has already happened at least once before —  twenty years ago!

As part of a project studying the evolution of (simulated) organisms, computer scientist Charles Ofria wanted to limit the replication rate of a digital organism. So, he programmed the system to pause after each mutation, measure the mutant’s replication rate in an isolated test environment, and delete the mutant if it replicated faster than its parent. However, the organisms evolved to recognize when they were in the test environment and “play dead” (pause replication) so they would not be eliminated and instead be kept in the population where they could continue to replicate outside the test environment. Once he discovered this, Ofria randomized the inputs of the test environment so that it couldn’t be so easily detected, but the organisms evolved a new strategy, to probabilistically perform tasks that would accelerate their replication, thus slipping through the test environment some percentage of the time and continuing to accelerate their replication thereafter.

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  1. Hat-tip to Carl Shulman, who noticed it as an example in a Chinese social media post (#26 here), and then noticed that it had previously appeared in Lehmann et al. (2018), pp. 8-9. According to Charles Ofria’s account there, the events described in this post occurred as part of the research eventually published as Wilke et al. (2001). [↩]

Media diet for Q1 2021

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Music

Spotify playlist for this quarter is here. Playlists for past quarters and years here.

Music I most enjoyed discovering this quarter:

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Media I’m looking forward to, Q2 2021 edition

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Added this quarter:

  • Johnson, Extra Life (May 2021)
  • Grossman, How Social Science Got Better (Aug 2021)
  • Buchanan & Imbrie, The New Fire (Mar 2022)
  • Kemp, Downfall (TBD)
  • Rick and Morty: season 5 (Jun 2021)
  • Better Call Saul: season 6 (TBD 2022)
  • Curb Your Enthusiasm: season 11 (TBD)
  • Succession: season 3 (TBD 2021)
  • Barry: season 3 (TBD)
  • Atlanta: season 3 (TBD)
  • Master of None: season 3 (TBD)

Books

bold = especially excited

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