Hide table of contents

Next week for the 80,000 Hours Podcast I'll be interviewing Carl Shulman, advisor to Open Philanthropy, and generally super informed person about history, technology, possible futures, and a shocking number of other topics.

He has previously appeared on our show and the Dwarkesh Podcast:

He has also written a number of pieces on this forum.

What should I ask him?

New Answer
New Comment


10 Answers sorted by

Why aren't there more people like him, and what is he doing or planning to do about that?

Related question: How does one become someone like Carl Shulman (or Wei Dai, for that matter)?

Wei Dai
75
0
0
4
4

I thought about this and wrote down some life events/decisions that probably contributed to becoming who I am today.

  • Immigrating to the US at age 10 knowing no English. Social skills deteriorated while learning language, which along with lack of cultural knowledge made it hard to make friends during teenage and college years, which gave me a lot of free time that I filled by reading fiction and non-fiction, programming, and developing intellectual interests.
  • Was heavily indoctrinated with Communist propaganda while in China, but leaving meant I then had no viable moral/philosophical/political foundations. Parents were too busy building careers as new immigrants and didn't try to teach me values/traditions. So I had a lot of questions that I didn't have ready answers to, which perhaps contributed to my intense interest in philosophy (ETA: and economics and game theory).
  • Had an initial career in cryptography, but found it a struggle to compete with other researchers on purely math/technical skills. Realized that my comparative advantage was in more conceptual work. Crypto also taught me to be skeptical of my own and other people's ideas.
  • Had a bad initial experience with academic research (received nonsensical peer review when submitting a paper to a conference) so avoided going that route. Tried various ways to become financially independent, and managed to "retire" in my late 20s to do independent research as a hobby.

A lot of these can't really be imitated by others (e.g., I can't recommend people avoid making friends in order to have more free time for intellectual interests). But here are some practical advice I can think of:

  1. Try to rethink what your comparative advantage really is.
  2. I think humanity really needs to make faster philosophical progress, so try your hand at that even if you think of yourself as more of a technical person. Same may be true for solving social/coordination problems. (But see next item.)
  3. Somehow develop a healthy dose of self-skepticism so that you don't end up wasting people's time and attention arguing for ideas that aren't actually very good.
  4. It may be worth keeping an eye out for opportunities to "get rich quick" so you can do self-supported independent research. (Which allows you to research topics that don't have legible justifications or are otherwise hard to get funding for, and pivot quickly as the landscape and your comparative advantage both change over time.)

ETA: Oh, here's a recent LW post where I talked about how I arrived at my current set of research interests, which may also be of interest to you.

Maybe if/how his thinking about AI governance has changed over the last year?

Relatedly, I'd be interested to know whether his thoughts on the public's support for AI pauses or other forms of strict regulation have updated since his last comment exchange with Katja, now that we have many reasonably high-quality polls on the American public's perception of AI (much more concerned than excited), as well as many more public conversations.

9
CarlShulman
A bit, but more on the willingness of AI experts and some companies to sign the CAIS letter and lend their voices to the view 'we should go forward very fast with AI, but keep an eye out for better evidence of danger and have the ability to control things later.' My model has always been that the public is technophobic, but that 'this will be constrained like peaceful nuclear power or GMO crops' isn't enough to prevent a technology that enables DSA and OOMs (and nuclear power and GMO crops exist, if AGI exists somewhere that place outgrows the rest of the world if the rest of the world sits on the sidelines). If leaders' understanding of the situation is that public fears are erroneous, and going forward with AI means a hugely better economy (and thus popularity for incumbents) and avoiding a situation where abhorred international rivals can safely disarm their military, then I don't expect it to be stopped. So the expert views, as defined by who the governments view as experts, are central in my picture. Visible AI progress like ChatGPT strengthens 'fear AI disaster' arguments but at the same time strengthens 'fear being behind in AI/others having AI' arguments. The kinds of actions that have been taken so far are mostly of the latter type (export controls, etc), and measures to monitor the situation and perhaps do something later if the evidential situation changes. I.e. they reflect the spirit of the CAIS letter, which companies like OpenAI and such were willing to sign, and not the pause letter which many CAIS letter signatories oppose.   The evals and monitoring agenda is an example of going for value of information rather than banning/stopping AI advances, like I discussed in the comment, and that's a reason it has had an easier time advancing.

Nice to know, Rob! I have really liked the podcasts Carl did. You may want to link to Carl's (great!) blog in your post too.

In general, I would be curious to know more about how Carl thinks about determining how much resources should go into each cause area, which I do not recall being discussed much in Carl's 3 podcasts. Some potential segways:

Carl has knowledge about lots of topics, very much like Anders Sandberg. So I think the questions I shared to ask Anders are also good questions for Carl:

  • Should more resources be directed towards patient philanthropy at the margin? How much more/less?
  • How binary is longterm value? Relevant to the importance of the concept of existential risk.
  • Should the idea that more global warming might be good to mitigate the food shocks caused by abrupt sunlight reduction scenarios (ASRSs) be taken seriously? (Anders is at the board of ALLFED, and therefore has knowledge about ASRSs.)
  • Which fraction of the expected effects of neartermist interventions (e.g. global health and development, and animal welfare) flow through longtermis considerations (e.g. longterm effects of changing population size, or expansion of the moral circle)? [Is it unclear whether Against Malaria Foundation is better/worse than Make-A-Wish Foundation, as argued in section 4.1 of Maximal cluelessness?]
  • Under moral realism, are we confident that superintelligent artificial intelligence disempowering humans would be bad?
  • Should we be uncertain about whether saving lives is good/bad because of the meat eater problem [my related calculations; related post]?
  • What is the chance that the time of perils hypothesis is true (e.g. how does the existential risk this century compare to that over the next 1 billion years)? How can we get more evidence for/against it? Relevant because, if existential risk is spread out over a long time, reducing existential risk this century has a negligible effect on total existential risk, as discussed by David Thorstad. [See also Rethink's post on this question.]
  • How high is the chance of AGI lock-in this century?
  • What can we do to ensure a bright future if there are advanced aliens on or around Earth (Magnus Vinding's thoughts)? More broadly, should humanity do anything differently due to the possibility of advanced civilisations which did not originite on Earth? [Another speculative question, which you covered a little in the podcast with Joe, is what should we do differently to improve the world if we were in a simulation?]
  • How much weight should one give to the XPT's forecasts? The ones regarding nuclear extinction seem way too pessimistic to be accurate [the reasons I think this are in this thread]. Superforecasters and domain experters predicted a likelihood of nuclear extinction by 2100 of 0.074 % and 0.55 %. My guess would be something like 10^-6 (10 % of a global nuclear nuclear war involving tens of detonations, 10 % of it escalating to thousands of detonations, and 0.01 % of that leading to extinction), in which case superforecasters would be off by 3 orders of magnitude.

Importance of the digital minds stuff compared to regular AI safety; how many early-career EAs should be going into this niche? What needs to happen between now and the arrival of digital minds? In other words, what kind of a plan does Carl have in mind for making the arrival go well? Also, since Carl clearly has well-developed takes on moral status, what criteria he thinks could determine whether an AI system deserves moral status, and to what extent.

Additionally—and this one's fueled more by personal curiosity than by impact—Carl's beliefs on consciousness. Like Wei Dai, I find the case for anti-realism as the answer to the problem of consciousness weak, yet this is Carl's position (according to this old Brian Tomasik post, at least), and so I'd be very interested to hear Carl explain his view.

IIRC Carl had a $5M discretionary funding pot from OpenPhil. What has he funded with it?

Not much new on that front besides continuing to back the donor lottery in recent years, for the same sorts of reasons as in the link, and focusing on research and advising rather than sourcing grants.

My understanding is that he believes that full non-indexical conditioning has solved many (most? all?) problems in anthropics. It might be interesting to hear his views on what has been solved, and what is remaining.

I'd like to hear his advice for smart undergrads who want to build their own similarly deep models in important areas which haven't been thought about very much e.g. take-off speeds, the influence of pre-AGI systems on the economy, the moral value of insects, preparing for digital minds (ideally including specific exercises/topics/reading/etc.).

I'm particularly interested in how he formed good economic intuitions, as they seem to come up a lot in his thinking/writing.

Can you ask him whether or not it's rational to assume AGI comes with significant existential risk as a default position, or if one has to make a technical case for it coming with x-risk? 

How did he deal with two-envelope considerations in his calculation of moral weights for OpenPhil?

[This comment is no longer endorsed by its author]

I have never calculated moral weights for Open Philanthropy, and as far as I know no one has claimed that. The comment you are presumably responding to began by saying I couldn't speak for Open Philanthropy on that topic, and I wasn't.

Comments1
Sorted by Click to highlight new comments since:

[Meta] Forum bug: when there were no comments it was showing as -1 comments

Curated and popular this week
 ·  · 52m read
 · 
In recent months, the CEOs of leading AI companies have grown increasingly confident about rapid progress: * OpenAI's Sam Altman: Shifted from saying in November "the rate of progress continues" to declaring in January "we are now confident we know how to build AGI" * Anthropic's Dario Amodei: Stated in January "I'm more confident than I've ever been that we're close to powerful capabilities... in the next 2-3 years" * Google DeepMind's Demis Hassabis: Changed from "as soon as 10 years" in autumn to "probably three to five years away" by January. What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI) by 2028?[1] In this article, I look at what's driven recent progress, estimate how far those drivers can continue, and explain why they're likely to continue for at least four more years. In particular, while in 2024 progress in LLM chatbots seemed to slow, a new approach started to work: teaching the models to reason using reinforcement learning. In just a year, this let them surpass human PhDs at answering difficult scientific reasoning questions, and achieve expert-level performance on one-hour coding tasks. We don't know how capable AGI will become, but extrapolating the recent rate of progress suggests that, by 2028, we could reach AI models with beyond-human reasoning abilities, expert-level knowledge in every domain, and that can autonomously complete multi-week projects, and progress would likely continue from there.  On this set of software engineering & computer use tasks, in 2020 AI was only able to do tasks that would typically take a human expert a couple of seconds. By 2024, that had risen to almost an hour. If the trend continues, by 2028 it'll reach several weeks.  No longer mere chatbots, these 'agent' models might soon satisfy many people's definitions of AGI — roughly, AI systems that match human performance at most knowledge work (see definition in footnote).[1] This means that, while the co
 ·  · 22m read
 · 
Summary In this article, I estimate the cost-effectiveness of five Anima International programs in Poland: improving cage-free and broiler welfare, blocking new factory farms, banning fur farming, and encouraging retailers to sell more plant-based protein. I estimate that together, these programs help roughly 136 animals—or 32 years of farmed animal life—per dollar spent. Animal years affected per dollar spent was within an order of magnitude for all five evaluated interventions. I also tried to estimate how much suffering each program alleviates. Using SADs (Suffering-Adjusted Days)—a metric developed by Ambitious Impact (AIM) that accounts for species differences and pain intensity—Anima’s programs appear highly cost-effective, even compared to charities recommended by Animal Charity Evaluators. However, I also ran a small informal survey to understand how people intuitively weigh different categories of pain defined by the Welfare Footprint Institute. The results suggested that SADs may heavily underweight brief but intense suffering. Based on those findings, I created my own metric DCDE (Disabling Chicken Day Equivalent) with different weightings. Under this approach, interventions focused on humane slaughter look more promising, while cage-free campaigns appear less impactful. These results are highly uncertain but show how sensitive conclusions are to how we value different kinds of suffering. My estimates are highly speculative, often relying on subjective judgments from Anima International staff regarding factors such as the likelihood of success for various interventions. This introduces potential bias. Another major source of uncertainty is how long the effects of reforms will last if achieved. To address this, I developed a methodology to estimate impact duration for chicken welfare campaigns. However, I’m essentially guessing when it comes to how long the impact of farm-blocking or fur bans might last—there’s just too much uncertainty. Background In
gergo
 ·  · 11m read
 · 
Crossposted on Substack and Lesswrong. Introduction There are many reasons why people fail to land a high-impact role. They might lack the skills, don’t have a polished CV, don’t articulate their thoughts well in applications[1] or interviews, or don't manage their time effectively during work tests. This post is not about these issues. It’s about what I see as the least obvious reason why one might get rejected relatively early in the hiring process, despite having the right skill set and ticking most of the other boxes mentioned above. The reason for this is what I call context, or rather, lack thereof. Subscribe to The Field Building Blog On professionals looking for jobs It’s widely agreed upon that we need more experienced professionals in the community, but we are not doing a good job of accommodating them once they make the difficult and admirable decision to try transitioning to AI Safety. Let’s paint a basic picture that I understand many experienced professionals are going through, or at least the dozens I talked to at EAGx conferences. 1. They do an AI Safety intro course 2. They decide to pivot their career 3. They start applying for highly selective jobs, including ones at OpenPhilanthropy 4. They get rejected relatively early in the hiring process, including for more junior roles compared to their work experience 5. They don’t get any feedback 6. They are confused as to why and start questioning whether they can contribute to AI Safety If you find yourself continuously making it to later rounds of the hiring process, I think you will eventually land the job sooner or later. The competition is tight, so please be patient! To a lesser extent, this will apply to roles outside of AI Safety, especially to those aiming to reduce global catastrophic risks. But for those struggling to penetrate later rounds of the hiring process, I want to suggest a potential consideration. Assuming you already have the right skillset for a given role, it might
Relevant opportunities