catherio

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microCOVID.org: A tool to estimate COVID risk from common activities

"I've heard people make this claim before but without explaining why. [...] the key risk factors for a dining establishment are indoor vs. outdoor, and crowded vs. spaced. The type of liquor license the place has doesn't matter."

I think you're misunderstanding how the calculator works. All the saved scenarios do is fill in the parameters below. The only substantial difference between "restaurant" and "bar" is that we assume bars are places people speak loudly. That's all. If the bar you have in mind isn't like that, just change the parameters.

3 Cultural Infrastructure Ideas from MAPLE

entry-level leadership

It has become really salient to me recently that good practice involves lots of prolific output in low-stakes throwaway contexts. Whereas a core piece of EA and rationalist mindsets is steering towards high-stakes things to work on, and treating your outputs as potentially very impactful and not to be thrown away. In my own mind “practice mindset” and “impact mindset” feel very directly in tension.

I have a feeling that something around this mindset difference is part of why world-saving orientation in a community might be correlated with inadequate opportunities for low-stakes leadership practice.

No, it's not The Incentives—it's you

Here's another further-afield steelman, inspired by blameless postmortem culture.

When debriefing / investigating a bad outcome, it's better for participants to expect not to be labeled as "bad people" (implicitly or explicitly) as a result of coming forward with information about choices they made that contributed to the failure.

More social pressure against admitting publicly that one is contributing poorly contributes to systematic hiding/obfuscation of information about why people are making those choices (e.g. incentives). And we need all that information to be out in the clear (or at least available to investigators who are committed & empowered to solve the systemic issues), if we are going to have any chance of making lasting changes.


In general, I'm curious what Zvi and Ben think about the interaction between "I expect people to yell at me if I say I'm doing this" and promoting/enabling "honest accounting".

No, it's not The Incentives—it's you

Another distinction I think is important, for the specific example of "scientific fraud vs. cow suffering" as a hypothetical:

Science is a terrible career for almost any goal other than actually contributing to the scientific endeavor.

I have a guess that "science, specifically" as a career-with-harmful-impacts in the hypothetical was not specifically important to Ray, but that it was very important to Ben. And that if the example career in Ray's "which harm is highest priority?" thought experiment had been "high-frequency-trading" (or something else that some folks believe has harms when ordinarily practiced, but is lucrative and thus could have benefits worth staying for, and is not specifically a role of stewardship over our communal epistemics) that Ben would have a different response. I'm curious to what extent that's true.

No, it's not The Incentives—it's you

One distinction I see getting elided here:

I think one's limited resources (time, money, etc) are a relevant question in one's behavior, but a "goodness budget" is not relevant at all.

For example: In a world where you could pay $50 to the electric company to convert all your electricity to renewables, or pay $50 more to switch from factory to pasture-raised beef, then if someone asks "hey, your household electrical bill is destroying the environment, why didn't you choose the green option", a relevant reply is "because I already spent my $50 on cow suffering".

However, if both options cost $0, then "but I already switched to pasture-raised beef" is just irrelevant in its entirety.

You Have About Five Words

The recent EA meta fund announcement linked to this post (https://www.centreforeffectivealtruism.org/blog/the-fidelity-model-of-spreading-ideas ) which highlights another parallel approach: in addition to picking idea expressions that fail gracefully, to prefer transmission methods that preserve nuance.

Boring Advice Repository

Nah, it's purely a formatting error - the trailing parenthesis was included in the link erroneously. Added whitespace to fix now.

Boring Advice Repository

If you have ovaries/uterus, a non-zero interest in having kids with your own gametes, and you're at least 25 or so: Get a fertility consultation.

They do an ultrasound and a blood test to estimate your ovarian reserve. Until you either try to conceive or get other measurements, you don't know if you have normal fertility for your age, or if your fertility is already declining without knowing it.

This is important information to know, in order to make later informed decisions (such as when and whether to freeze your eggs, when to start looking for a child-raising partner, when you need to decide by before it's too late, etc.)

(I wrote more about this here: https://paper.dropbox.com/doc/Egg-freezing-catherios-info-for-friends--AbyB0V0bRUZsCM~QbeEzkNuMAg-tI98uI9kmLOlLRRuO80Zh )

How much funding and researchers were in AI, and AI Safety, in 2018?

Two observations:

  • I'd expect that most "AI capabilities research" that goes on today isn't meaningfully moving us towards AGI at all, let alone aligned AGI. For example, applying reinforcement learning to hospital data. So "how much $ went to AI in 2018" would be a sloppy upper bound on "important thoughts/ideas/tools on the path to AGI".
  • There's a lot of non-capabilities non-AGI research targeted at "making the thing better for humanity, not more powerful". For example, interpretability work on models simpler than convnets, or removing bias from word embeddings. If by "AI safety" you mean "technical AGI alignment" or "reducing x-risk from advanced AI" this category definitely isn't that, but it also definitely isn't "AI capabilities" let alone "AGI capabilities".
Current AI Safety Roles for Software Engineers

Important updates to your model:

  • OpenAI recently hired Chris Olah (and his collaborator Ludwig Schubert), so *interpretability* is going to be a major and increasing focus at that org (not just deep RL). This is an important upcoming shift to have on your radar.
  • DeepMind has at least two groups doing safety-related research: the one we know of as "safety" is more properly the "Technical AGI Safety" team, but there is also a "Safe and Robust AI team" that does more like neural net verification and adversarial examples.
  • RE "General AI work in industry" - I've increasingly become aware of a number of somewhat-junior researchers who do work in a safety-relevant area (learning from human preferences, interpretability, robustness, safe exploration, verification, adversarial examples, etc.), and who are indeed long-term-motivated (determined once we say the right shibboleths at each other) but aren't on a "safety team". This gives me more evidence that if you're able to get a job anywhere within Brain or DeepMind (or honestly any other industry research lab), you can probably hill-climb your way to relevant mentorship and start doing relevant stuff.

Less important notes:

  • I'm at Google Brain right now, not OpenAI!
  • I wrote up a guide which I hope is moderately helpful in terms of what exactly one might do if one is interested in this path: https://80000hours.org/articles/ml-engineering-career-transition-guide/
  • Here's a link for the CHAI research engineering post: https://humancompatible.ai/jobs#engineer
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