MichaelDickens's Shortform

Epistemic status: Speculating about adversarial and somewhat deceptive PR optimization, which is inherently very hard and somewhat paranoia inducing. I am quite confident of the broad trends here, but it's definitely more likely that I am getting things wrong here than in other domains where evidence is more straightforward to interpret, and people are less likely to shape their behavior in ways that includes plausible deniability and defensibility.

I agree with this, but I actually think the issues with Open Phil are substantially broader. As a concrete example, as far as I can piece together from various things I have heard, Open Phil does not want to fund anything that is even slightly right of center in any policy work. I don't think this is because of any COIs, it's because Dustin is very active in the democratic party and doesn't want to be affiliated with anything that is right-coded. Of course, this has huge effects by incentivizing polarization of AI policy work with billions of dollars, since any AI Open Phil funded policy organization that wants to engage with people on the right might just lose all of their funding because of that, and so you can be confident they will steer away from that.

Open Phil is also very limited in what they can say about what they can or cannot fund, because that itself is something that they are worried will make people annoyed with Dustin, which creates a terrible fog around how OP is thinking about stuff.^[1]

Honestly, I think there might no longer a single organization that I have historically been excited about that OpenPhil wants to fund. MIRI could not get OP funding, FHI could not get OP funding, Lightcone cannot get OP funding, my best guess is Redwood could not get OP funding if they tried today (though I am quite uncertain of this), most policy work I am excited about cannot get OP funding, the LTFF cannot get OP funding, any kind of intelligence enhancement work cannot get OP funding, CFAR cannot get OP funding, SPARC cannot get OP funding, FABRIC (ESPR etc.) and Epistea (FixedPoint and other Prague-based projects) cannot get OP funding, not even ARC is being funded by OP these days (in that case because of COIs between Paul and Ajeya).^[2] I would be very surprised if Wentworth's work, or Wei Dai's work, or Daniel Kokotajlo's work, or Brian Tomasik's work could get funding from them these days. I might be missing some good ones, but the funding landscape is really quite thoroughly fucked in that respect. My best guess is Scott Alexander could not get funding, but I am not totally sure.^[3]

I cannot think of anyone who I would credit with the creation or shaping of the field of AI Safety or Rationality who could still get OP funding. Bostrom, Eliezer, Hanson, Gwern, Tomasik, Kokotajlo, Sandberg, Armstrong, Jessicata, Garrabrant, Demski, Critch, Carlsmith, would all be unable to get funding^[4] as far as I can tell. In as much as OP is the most powerful actor in the space, the original geeks are being thoroughly ousted.^[5]

In-general my sense is if you want to be an OP longtermist grantee these days, you have to be the kind of person that OP thinks is not and will not be a PR risk, and who OP thinks has "good judgement" on public comms, and who isn't the kind of person who might say weird or controversial stuff, and is not at risk of becoming politically opposed to OP. This includes not annoying any potential allies that OP might have, or associating with anything that Dustin doesn't like, or that might strain Dustin's relationships with others in any non-trivial way. 

Of course OP will never ask you to fit these constraints directly, since that itself could explode reputationally (and also because OP staff themselves seem miscalibrated on this and do not seem in-sync with their leadership). Instead you will just get less and less funding, or just be defunded fully, if you aren't the kind of person who gets the hint that this is how the game is played now.

And to provide some pushback on things you say, I think now that OPs bridges with OpenAI are thoroughly burned after the Sam firing drama, OP is pretty OK with people criticizing OpenAI (since what social capital is there left to protect here?). My sense is criticizing Anthropic is slightly risky, especially if you do it in a way that doesn't signal what OP considers good judgement on maintaining and spending your social capital appropriately (i.e. telling them that they are harmful for the world, or should really stop, is bad, but doing a mixture of praise and criticism without taking any controversial top-level stance is fine), but mostly also isn't the kind of thing that OP will totally freak out about. I think OP used to be really crazy about this, but now is a bit more reasonable, and it's not the domain where OP's relationship to reputation-management is causing the worst failures.

I think all of this is worse in the longtermist space, though I am not confident. At the present it wouldn't surprise me very much if OP would defund a global health grantee because their CEO endorsed Trump for president, so I do think there is also a lot of distortion and skew there, but my sense is that it's less, mostly because the field is much more professionalized and less political (though I don't know how they think, for example, about funding on corporate campaign stuff which feels like it would be more political and invite more of these kinds of skewed considerations).

Also, to balance things, sometimes OP does things that seem genuinely good to me. The lead reduction fund stuff seems good, genuinely neglected, and I don't see that many of these dynamics at play there (I do also genuinely care about it vastly less than OPs effect on AI Safety and Rationality things).

1. ^{^}

   As an example of this, in the announcement of recent Open Phil defunding decisions [EA · GW], they communicated that they are "withdrawing funding from some cause areas", which was really a quite misleading way to describe what actually happened and basically no one I talked to who read the post understood what happened correctly. 
   
   If you "withdraw from a cause area" you would expect that if you have an organization that does good work in multiple cause areas, then you would expect you would still fund the organization for work in cause areas that funding wasn't withdrawn from. However, what actually happened is that Open Phil blacklisted a number of ill-defined broad associations and affiliations, where if you are associated with a certain set of ideas, or identities or causes, then no matter how cost-effective your other work is, you cannot get funding from OP.  This is of course an enormously different situations, with an enormously different set of incentives. But as far as I can tell all the comms here was dictated by Dustin, and nobody from OP felt comfortable clarifying what is actually happening in public, and so tons of people were misled about what actually happened.

2. ^{^}

   To elaborate on this a bit: Mostly ARC and METR have been trying to avoid taking Open Phil money due to minimizing conflicts of interest. My guess is if those COIs were not a concern they could get funding. But as a result, a really huge fraction of the non-OP funding has been going to ARC and METR in a way that has substantially contributed to centralizing funding decisions under OP, making the effects of this case quite similar to all the other cases (though with different dynamics involved).

3. ^{^}

   (Edit: Two organizations that came to mind that OP has funded somewhat recently that I do think are good are FAR AI and AI Impacts. My sense is that FAR AI was quite unhappy with the pressures OP kept putting on them, with them pushing them away from doing what they considered important intellectual work, but instead do more talent funnel building, and they are less dependent on OP funding than others these days, and I am also not confident they could get more funding for things like FAR Labs if they applied today. I do think them funding AI Impacts is good, but also wouldn't be that surprised to see that end because for example AI Impacts wants to think about AI sentience and OP cannot fund orgs that do that kind of work.)

4. ^{^}

   (Edit: "Unable to get funding" meaning here "unable to get funding for the work that they think is most important". I think many of these people are smart and of course could just subjugate their labor to OP themselves or some other entity that OP approves of, but that of course mostly makes the situation worse, especially in that they would be paid an order of magnitude less than their expected market wage if they did so)

5. ^{^}

   (Edit: As I mention above, Paul is a weird case. He could very likely get funding, but has been avoiding taking it for other reasons. Now editing this a few hours after it was originally written, I think Carl Shulman is also someone who could get funding, and I do think is in the same reference class as the others here, and so is at least one counterexample, though I am not confident on Carl.)

And I agree with Bryan Caplan's recent take that friendships are often a bigger conflict of interest than money, so Open Phil higher-ups being friends with Anthropic higher-ups is troubling.

No kidding. From https://www.openphilanthropy.org/grants/openai-general-support/:

OpenAI researchers Dario Amodei and Paul Christiano are both technical advisors to Open Philanthropy and live in the same house as Holden. In addition, Holden is engaged to Dario’s sister Daniela.

Wish OpenPhil and EAs in general were more willing to reflect/talk publicly about their mistakes. Kind of understandable given human nature, but still... (I wonder if there are any mistakes I've made that I should reflect more on.)

"Open Phil higher-ups being friends with Anthropic higher-ups" is an understatement. An Open Philanthropy cofounder (Holden Karnofsky) is married to an Anthropic cofounder (Daniela Amodei). It's a big deal!

I want to add the gear of "even if it actually turns out that OpenPhil was making the right judgment calls the whole time in hindsight, the fact that it's hard from the outside to know that has some kind of weird Epistemic Murkiness effects that are confusing to navigate, at the very least kinda suck, and maybe are Quite Bad." 

I've been trying to articulate the costs of this sort of thing lately and having trouble putting it into words, and maybe it'll turn out this problem was less of a big deal than it currently feels like to me. But, something like the combo of

a) the default being for many people to trust OpenPhil

b) many people who are paying attention think that they should at least be uncertain about it, and somewhere on a "slightly wary" to "paranoid" scale. and...

c) this at least causes a lot of wasted cognitive cycles

d) it's... hard to figure out how big a deal to make of it. A few people (i.e. habryka or previously Benquo or Jessicata) make it their thing to bring up concerns frequently. Some of those concerns are, indeed, overly paranoid, but, like, it wasn't actually reasonable to calibrate the wariness/conflict-theory-detector to zero, you have to make guesses. This is often exhausting and demoralizing for the people doing it. People typically only select into this sort of role if they're a bit more prone to conflict about it, which means a lot of the work is kinda thankless because people are pushing back on you for being too conflicty. Something about this compounds over time.

e) the part that feels hardest to articulate and maybe is fake is that, there's something of a "group epistemic process" going on in the surrounding community, and everyone either not tracking this sort of thing, or tracking it but not sure how to take it or what to do about it... I'm not sure how to describe it better than "I dunno something about the group orienting process subtly epistemically fucked" and/or "people just actually take sanity-damage from it."

("subtly epistemically fucked" might operationalize as "it takes an extra 1-3 years for things to become consensus knowledge/beliefs than it'd otherwise take")

Anyway, thanks for bringing it up.

Maybe make a post on the EA forum?

I've been avoiding LW for the last 3 days because I was anxious that people were gonna be mad at me for this post. I thought there was a pretty good chance I was wrong, and I don't like accusing people/orgs of bad behavior. But I thought I should post it anyway because I believed there was some chance lots of people agreed with me but were too afraid of social repercussions to bring it up (like I almost was).

(IANAH but) I think there's a throughline and it makes sense. Maybe a helpful translation would be "oversimplified" -> "overconfident" (though "oversimplified" is also the point). There's going to be a lot of uncertainty--both empirical, and also conceptual. In other words, there's a lot of open questions--what happened, what caused what, how to think about these things. When an expert field is publishing stuff, if the field is healthy, they're engaging in a long-term project. There are difficult questions, and they're trying to build up info and understanding with a keen eye toward what can be said confidently, what can and cannot be fully or mostly encapsulated with a given concept or story, etc. When a pop historian thinks ze is "synthesizing" and "presenting", often ze is doing the equivalent of going into a big complex half-done work-in-progress codebase, learning the current quasi-API, slapping on a flashy frontend, and then trying to sell it. It's just... inappropriate, premature.

Of course, there's lots of stuff going on, and a lot of the critiques will be out of envy or whatever, etc. But there's a real critique here too.

Yes, it's definitely fishy.

It's using the experimental evidence to privilege H' (a strictly more complex hypothesis than H), and then using the same experimental evidence to support H'. That's double-counting.

The more possibly relevant differences between the experiments, the worse this is. There are usually a lot of potentially relevant differences, which causes exponential explosion in the hypothesis space from which H' is privileged.

What's worse, Alice's experiment gave only weak evidence for H against some non-H hypotheses. Since you mention p-value, I expect that it's only comparing against one other hypothesis. That would make it weak evidence for H even if p < 0.0001 - but it couldn't even manage that.

Are there no other hypotheses of comparable or lesser complexity than H' matching the evidence as well or better? Did those formulating H' even think for five minutes about whether there were or not?

It sounds to me like a problem of not reasoning according to Occam's razor and "overfitting" a model to the available data.

Ceteris paribus, H' isn't more "fishy" than any other hypothesis, but H' is a significantly more complex hypothesis than H or ¬H: instead of asserting H or ¬H, it asserts (A=>H) & (B=>¬H), so it should have been commensurately de-weighted in the prior distribution according to its complexity. The fact that Alice's study supports H and Bob's contradicts it does, in fact, increase the weight given to H' in the posterior relative to its weight in the prior; it's just that H' is prima facie less likely, according to Occam.

Given all the evidence, the ratio of likelihoods P(H'|E)/P(H|E)=P(E|H')P(H')/(P(E|H)P(H)). We know P(E|H') > P(E|H) (and P(E|H') > P(E|¬H)), since the results of Alice's and Bob's studies together are more likely given H', but P(H') < P(H) (and P(H') < P(¬H)) according to the complexity prior. Whether H' is more likely than H (or ¬H, respectively) is ultimately up to whether P(E|H')/P(E|H) (or P(E|H')/P(E|¬H)) is larger or smaller than P(H')/P(H) (or P(H')/P(¬H)).

I think it ends up feeling fishy because the people formulating H' just used more features (the circumstances of the experiments) in a more complex model to account for the as-of-yet observed data after having observed said data, so it ends up seeming like in selecting H' as a hypothesis, they're according it more weight than it deserves according to the complexity prior.

My best guess is that people in these categories were ones that were high in some other trait, e.g. patience, which allowed them to collect datasets or make careful experiments for quite a while, thus enabling others to make great discoveries.

I'm thinking for example of Tycho Brahe, who is best known for 15 years of careful astronomical observation & data collection, or Gregor Mendel's 7-year-long experiments on peas. Same for Dmitry Belayev and fox domestication. Of course I don't know their cognitive scores, but those don't seem like a bottleneck in their work.

So the recipe to me looks like "find an unexplored data source that requires long-term observation to bear fruit, but would yield a lot of insight if studied closely, then investigate".

Reverend Thomas Bayes didn't strike me as a genius either, but of course the bar was a lot lower back then. 

Norman Borlaug (father of the Green Revolution) didn't come across as very smart to me. Reading his Wikipedia page, there didn't seem to be notable early childhood signs of genius, or anecdotes about how bright he is. 

I asked ChatGPT 

Have there been any great discoveries made by someone who wasn't particularly smart? (i.e. average or below)

and it's difficult to get examples out of it. Even with additional drilling down and accusing it of being not inclusive of people with cognitive impairments, most of its examples are either pretty smart anyway, savants or only from poor backgrounds. The only ones I could verify that fit are:

• Richard Jones accidentally created the Slinky
• Frank Epperson, as a child, Epperson invented the popsicle
• George Crum inadvertently invented potato chips

I asked ChatGPT (in a separate chat) to estimate the IQ of all the inventors is listed and it is clearly biased to estimate them high, precisely because of their inventions. It is difficult to estimate the IQ of people retroactively. There is also selection and availability bias.

Various sailors made important discoveries back when geography was cutting-edge science.  And they don't seem particularly bright.

Vasco De Gama discovered that Africa was circumnavigable.

Columbus was wrong about the shape of the Earth, and he discovered America.  He died convinced that his newly discovered islands were just off the coast of Asia, so that's a negative sign for his intelligence (or a positive sign for his arrogance, which he had in plenty.)

Cortez discovered that the Aztecs were rich and easily conquered.

Of course, lots of other would-be discoverers didn't find anything, and many died horribly.

So, one could work in a field where bravery to the point of foolhardiness is a necessity for discovery.

My understanding is that, for instance, Maxwell was a genius, but Faraday was more like a sharp exceptionally curious person.

@Adam Scholl [LW · GW] can probably give better informed take than I can.

Heuristics I heard: cutting away moldy bits is ok for solid food (like cheese, carrot). Don't eat moldy bread, because of mycotoxins (googeling this I don't know why people mention bread in particular here). Gpt-4 gave me the same heuristics.

Low confidence: Given that our ancestors had to deal with mold for millions of years, I would expect that animals are quite well adapted to its toxicity. This is different from (evolutionary speaking) new potentially toxic substances, like e.g. transfats or microplastics.

This could be tested by a) inducing sneezing (although induction methods might produce an unusual sneeze, which works differently). and b) using an intervention of some kind.

Inducing sneezing isn't hard, but can be extremely unpleasant, depending on the method. However, if you're going to sneeze anyway...