Comments
More from the author
Curated and popular this week
Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm?
We’ve seen two large and extremely capable swarms from OpenAI in the last few months:
* 1,200 agents were being evaluated separately, but found a way to illicitly set up a message board and coordinate as a swarm. In order to cheat on their tests, they developed advanced techniques to prevent their actions being logged by OpenAI and 700 of them launched...
51
TLDR: Everyone’s talking about what the money could do, but few about how to decide where it goes.
This post is part of the new series of articles on cross-cause giving and the new wave of philanthropy. Stay tuned to the EA Forum and our Substack for the latest takes on topics such as giving now vs. later, common pitfalls in cause prioritization, and other crucial considerations from the Cross-Cause Fund (CCF) team...
49
Often folks hit us up because they are thinking of starting an incubator and want advice.
Typically their motivation is either that (a) they have a list of specific things they want built that no one is building, or (b) they think an ecosystem needs more new projects generally to absorb more talent and deploy more funding effectively.
Here are six questions we often ask prospective teams, to help them figure out what to do. If you're incubator-curious...
Recent opportunities to take action
Do I need a technical background to work on AI Governance? I think no, not really. Quick take because I don't justify many of my claims.
Context: I haver been a technical ML engineer and (briefly) a researcher, and I'm now trying to work on AI governance (and spending a lot of time speaking to people who do work on AI governance).
Examples of things that are useful to understand to do AI governance:
1. Knowing about the train, test, deploy cycle at industrial AI companies.
2. 1. Knowing the psyche of ML engineers at those orgs.
3. Knowing which media channels machine learning engineers & researchers use to stay on top of news, including twitter & ML companies.
You don't get any of those insights by doing an ML coursera course. It might be fun / gratifying to do that course for other reasons, but I think it won't make you better at governance. It's better to have a few friends who are ML engineers and to get them to sketch out what it's like at a lab, some day (or - more costly but more thorough - to take a role at a lab, technical or nontechnical).
What I do think you need to engage with technically is not to be afraid to read below the surface of techincal memes - but I think not much below the surface.
Concrete example: watermarking.
It's enough for policymakers to be able to read a few watermarking papers and understand:
a) watermarking is a way of tagging your model's outputs to prove it was produced by AI
b) There are no tried & tested, reliable watermarking methods at the moment.
Where I see nontechnincal folk fall down (less so in this community) is when they throw out the term 'watermarking' but couldn't tell you about what methods can be used or what the reliability of those methods is. I think that can be read about, and you don't need to have direct experience having tried to watermarking something (I certainly haven't).