313Joined Aug 2014


Senior Research Scientist at NTT Research, Physics & Informatics Lab. , jessriedel[at]gmail[dot]com


Just to be clear: we mostly don’t argue for the desirability or likelihood of lock-in, just its technological feasibility. Am I correctly interpreting your comment to be cautionary, questioning the desirability of lock-in given the apparent difficulty of doing so while maintaining sufficiently flexibility to handle unforeseen philosophical arguments?

If the Federal government is just buying, on the open market, an amount of coal comparable to how much would have been sold without government action, then it's going to drive up the price of coal and increase the total amount of coal extracted.  How much extra coal gets extracted depends on the supply and demand curves, and the amount of coal actually burned will almost certainly be less than in the world where the government didn't act, but it does mean the environmental benefits of this plan will be significantly muted.

Paul Graham writes that Noora Health is doing something like this.

Regarding your 4 criteria, I think they don't really delineate how to make the sort of judgment calls we're discussing here, so it really seems like it should be about a 5th criterion that does delineate that.

Sorry I was unclear.  Those were just 4 desiderata that the criteria need to satisfy; the desiderata weren't intended to fully specify the criteria.

If a small group of researchers at MIRI were trying to do work on verification but not getting much traction in the academic community, my intuition is that their papers would reliably meet your criteria.

Certainly possible, but I think this would partly be because MIRI would explicitly talk in their paper about the (putative) connection to TAI safety, which makes it a lot easier for me see. (Alternative interpretation: it would be tricking me, a non-expert, into thinking there was more of a substantive connection to TAI safety than actually is there.)  I am trying not to penalize researchers for failing to talk explicitly about TAI, but I am limited.

I think it's more likely the database has inconsistencies of the kind you're pointing at from CHAI, Open AI, and (as you've mentioned) DeepMind, since these organizations have self-described (partial) safety focus while still doing lots of research non-safety and near-term-safety research.  When confronted with such inconsistencies, I will lean heavily toward not including any of them since this seems like the only feasible choice given my resources. In other words, I select your final option: "The hypothetical MIRI work shouldn't have made the cut".

I definitely agree that you shouldn't just include every paper on robustness or verification, but perhaps at least early work that led to an important/productive/TAI-relevant line should be included

Here I understand you to be suggesting that we use a notability criterion that can make up for the connection to TAI safety being less direct.  I am very open to this suggestion, and indeed I think an ideal database would use criteria like this.  (It would make the database more useful to both researchers and donors.)  My chief concern is just that I have no way to do this right now because I am not in a position to judge the notability.  Even after looking at the abstracts of the work by Raghunathan et al. and Wong & Kolter, I, as a layman, am unable to tell that they are quite notable.  

Now, I could certainly infer notability by (1) talking to people like you and/or (2) looking at a citation trail.  (Note that a citation count is insufficient because I'd need to know it's well cited by TAI safety papers specifically.)  But this is just not at all feasible for me to do for a bunch of papers, much less every paper that initially looked equally promising to my untrained eyes. This database is a personal side project, not my day job.  So I really need some expert collaborators or, at the least, some experts who are willing to judge batches of papers based on a some fixed set of criteria.

Sure, sure, we tried doing both of these. But they were just taking way too long in terms of new papers surfaced per hour worked. (Hence me asking for things that are more efficient than looking at reference lists from review articles and emailing the orgs.) Following the correct (promising) citation trail also relies more heavily on technical expertise, which neither Angelica nor I have.

I would love to have some collaborators with expertise in the field to assist on the next version. As mentioned, I think it would make a good side project for a grad student, so feel to nudge yours to contact us!

for instance if you think Wong and Cohen should be dropped then about half of the DeepMind papers should be too since they're on almost identical topics and some are even follow-ups to the Wong paper).

Yea, I'm saying I would drop most of those too.

I think focusing on motivation rather than results can also lead to problems, and perhaps contributes to organization bias (by relying on branding to asses motivation).

I agree this can contribute to organizational bias.

I do agree that counterfactual impact is a good metric, i.e. you should be less excited about a paper that was likely to soon happen anyways; maybe that's what you're saying? But that doesn't have much to do with motivation.

Just to be clear: I'm using "motivation" here in the technical sense of "What distinguishes this topic for further examination out of the space of all possible topics?", i.e., is the topic unusually likely to lead to TAI safety results down the line?" (It's not anything to do with the author's altruism or whatever.)

I think what would best advance this conversation would be for you to propose alternative practical inclusion criteria which could be contrasted the ones we've given.

Here's how is how I arrived at ours. The initial desiderata are:

  1. Criteria are not based on the importance/quality of the paper. (Too hard for us to assess.)

  2. Papers that are explicitly about TAI safety are included.

  3. Papers are not automatically included merely for being relevant to TAI safety. (There are way too many.)

  4. Criteria don't exclude papers merely for failure to mention TAI safety explicitly. (We want to find and support researchers working in institutions where that would be considered too weird.)

(The only desiderata that we could potentially drop are #2 or #4. #1 and #3 are absolutely crucial for keeping the workload manageable.)

So besides papers explicitly about TAI safety, what else can we include given the fact that we can't include everything relevant to safety? Papers that TAI safety researchers are unusually likely (relative to other researchers) to want to read, and papers that TAI safety donors will want to fund. To me, that means the papers that are building toward TAI safety results more than most papers are. That's what I'm trying to get across by "motivated".

Perhaps that is still too vague. I'm very in your alternative suggestions!

Thanks Jacob.  That last link is broken for me, but I think you mean this?

 You sort of acknowledge this already, but one bias in this list is that it's very tilted towards large organizations like DeepMind, CHAI, etc.

Well,  it's biased toward safety organizations, not large organizations.  (Indeed, it seems to be biased toward small safety organizations over larges ones since they tend to reply to our emails!)  We get good coverage of small orgs like Ought, but you're right we don't have a way to easily track individual unaffiliated safety researchers and it's not fair.

I look forward to a glorious future where this database is so well known that all safety authors naturally send us a link to their work when its released, but for now the best way we have of finding papers is (1) asking safety organizations for what they've produced and (2) taking references from review articles.  If you can suggest another option for getting more comprehensive coverage per hour of work we'd be very interested to hear it (seriously!).

For what it's worth, the papers by Hendrycks are very borderline based on our inclusion criteria, and in fact I think if I were classifying it today I think I would not include it.  (Not because it's not high quality work, but just because I think it still happens in a world where no research is motivated by the safety of transformative AI; maybe that's wrong?) For now I've added the  papers you mention by Hendrycks, Wong, and Cohen to the database, but my guess is they get dropped for being too near-term-motivated when they get reviewed next year.

More generally, let me mention that  we do want to recognize great work, but our higher priority is to (1) recognize work that is particularly relevant to TAI safety and (2) help donors assess safety organizations. 

Thanks again!  I'm adding your 2019 review to the list.


Jaime gave a great thorough explanation. My catch-phrase version: This is not a holistic Bayesian prediction. The confidence intervals come from bootstrapping (re-sampling) a fixed dataset, not summing over all possible future trajectories for reality.

I was curious about the origins of this concept in the EA community since I think it's correct, insightful, and I personally had first noticed it in conversation among people at Open Phil. On Twitter, @alter_ego_42 pointed out the existence of the Credal Resilience page in the "EA concepts" section of this website. That page cites

Skyrms, Brian. 1977. Resiliency, propensities, and causal necessity. The journal of philosophy 74(11): 704-713. [PDF]

which is the earliest thorough academic reference to this idea that I know of. With apologies to Greg, this seems like the appropriate place to post a couple comments on that paper so others don't have to trudge through it.

I didn't find Skyrms's critique of frequentism at the beginning, or his pseudo-formalization of resilency on page 705 (see for instance the criticism "Some Remarks on the Concept of Resiliency" by Patrick Suppes in the very next article, pages 713-714), to be very insightful, so I recommend the time-pressed reader concentrate on

  • The bottom of p. 705 ("The concept of probabilistic resiliency is nicely illustrated...") to the top of p. 708 ("... well confirmed to its degree of instantial resiliency, as specified above..").
  • The middle of p. 712 ("The concept of resiliency has connections with...") to p. 713 (the end).

Skyrms quotes Savage (1954) as musing about the possibility of introducing "second-order probabilities". This is grounded in a relative-frequency intuition: when I say that there is a (first-order) probability p of X occurring but that I am uncertain, what I really mean is something like that there is some objective physical process that generates X with (second-order) probability q, but I am uncertain about the details of that process (i.e., about what q is), so my value of p is obtained by integrating over some pdf f (q).

There is, naturally, a Bayesian version of the same idea: We shouldn't concern ourselves with a hypothetical giant (second-order) ensemble of models, each of which generates a hypothetical (first-order) ensemble of individual trials. Resilience about probabilities is best measured by our bets on how future evidence would change those probabilities, just as probabilities is best measured by our bets on future outcomes.

(Unfortunately, and unlike the case for standard credences, there seems to be multiple possible formulations depending on which sorts of evidence we are supposing: what I expect to learn in the actual future, what I could learn if I thought about it hard, what a superforecaster would say in my shoes, etc.)

Were there a lot of new unknown or underappreciated facts in this book? From the summary, it sounds mostly like a reinterpretation of the standard history, which hinges on questions of historical determinism.

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