Background in philosophy, international development, statistics. Doing a technical AI PhD at Bristol.
Financial conflict of interest: technically the British government through the funding council.
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Big old US >> UK pay gap imo. Partial explanation for that: 32 days holiday in the UK vs 10 days US.
(My base pay was 85% of total; 100% seems pretty normal in UK tech.)
Other big factor: this was in a sorta sleepy industry that tacitly trades off money for working the contracted 37.5 h week, unlike say startups. Per hour it was decent, particularly given 10% study time.
If we say hustling places have a 50 h week (which is what one fancy startup actually told me they expected), then 41 looks fine.
Agree with the spirit - there is too much herding, and I would love for Schubert's distinctions to be core concepts. However, I think the problem you describe appears in the gap between the core orgs and the community, and might be pretty hard to fix as a result.
What material implies that EA is only about ~4 things?
- the Funds
- semi-official intro talks and Fellowship syllabi
- the landing page has 3 main causes and mentions 6 more
- the revealed preferences of what people say they're working on, the distribution of object-level post tags
What emphasises cause divergence and personal fit?
- 80k have their top 7 of course, but the full list of recommended ones has 23
- Personal fit is the second thing they raise, after importance
- New causes, independent thinking, outreach, cause X, and 'question > ideology' is a major theme at every EAG and (by eye) in about a fifth of the top-voted Forum posts.
So maybe limited room for improvements to communication? Since it's already pretty clear.
Intro material has to mention some examples, and only a couple in any depth. How should we pick examples? Impact has to come first. Could be better to not always use the same 4 examples, but instead pick the top 3 by your own lights and then draw randomly from the top 20.
Also, I've always thought of cause neutrality as conditional - "if you're able to pivot, and if you want to do the most good, what should you do?" and this is emphasised in plenty of places. (i.e. Personal fit and meeting people where they are by default.) But if people are taking it as an unconditional imperative then that needs attention.
Brian Christian is incredibly good at tying the short-term concerns everyone already knows about to the long-term concerns. He's done tons of talks and podcasts - not sure which is best, but if 3 hours of heavy content isn't a problem, the 80k one is good.
There's already a completely mainstream x-risk: nuclear weapons (and, popularly, climate change). It could be good to compare AI to these accepted handles. The second species argument can be made pretty intuitive too.
Bonus: here's what I told my mum.
AIs are getting better quite fast, and we will probably eventually get a really powerful one, much faster and better at solving problems than people. It seems really important to make sure that they share our values; otherwise, they might do crazy things that we won't be able to fix. We don't know how hard it is to give them our actual values, and to assure that they got them right, but it seems very hard. So it's important to start now, even though we don't know when it will happen, or how dangerous it will be.
[I don't know you, so please feel free to completely ignore any of the following.]
I personally know three EAs who simply aren't constituted to put up with the fake work and weak authoritarianism of college. I expect any of them to do great things. Two other brilliant ones are Chris Olah and Kelsey Piper. (I highly recommend Piper's writing on the topic for deep practical insights and as a way of shifting the balance of responsibility partially off yourself and onto the ruinous rigid bureaucracy you are in. She had many of the same problems as you, and things changed enormously once she found a working environment that actually suited her. Actually just read the whole blog, she is one of the greats.)
80k have some notes on effective alternatives to a degree. kbog also wrote a little guide.
In the UK a good number of professions have a non-college "apprenticeship" track, including software development and government! I don't know about the US.
This is not to say that you should not do college, just that there are first-class precedents and alternatives.
More immediately: I highly recommend coworking as a solution to ugh. Here's the best kind, Brauner-style, or here are nice group rooms on Focusmate or Complice.
You're a good writer and extremely self-aware. This is a really good start.
If you'd like to speak to some other EAs in this situation (including one in the US), DM me.
Not recent-recent, but I also really like Carey's 2017 work on CIRL. Picks a small, well-defined problem and hammers it flush into the ground. "When exactly does this toy system go bad?"
If we take "tangible" to mean executable:
- A primitive prototype and a framework for safety via debate (2018-9). Bit quiet since.
- Carey's 2019 proof of concept / extension of quantilizers
- Stiennon et al (2020) is an extremely encouraging example of a large negative "alignment tax" (making it safer also made it work better)
But as Kurt Lewin once said "there's nothing so practical as a good theory". In particular, theory scales automatically and conceptual work can stop us from wasting effort on the wrong things.
- CAIS (2019) pivots away from the classic agentic model, maybe for the better
- The search for mesa-optimisers (2019) is a step forward from previous muddled thoughts on optimisation, and they make predictions we can test them on soon.
- The Armstrong/Shah discussion of value learning changed my research direction for the better.
Also Everitt et al (2019) is both: a theoretical advance with good software.
I think you're right, see my reply to Ivan.
I think I generalised too quickly in my comment; I saw "virality" and "any later version" and assumed the worst. But of course we can take into account AGPL backfiring when we design this licence!
One nice side effect of even a toothless AI Safety Licence: it puts a reminder about safety into the top of every repo. Sure, no one reads licences (and people often ignore health and safety rules when it gets in their way, even at their own risk). But maybe it makes things a bit more tangible like LICENSE.md gives law a foothold into the minds of devs.
Not a bio guy, but in general: talk to more people! List people you think are doing good work and ask em directly.
Also generically: try to do some real work in as many of them as you can. I don't know how common undergrad research assistants are in your fields, or in Australian unis, but it should be doable (if you're handling your courseload ok).
PS: Love the username.