If you want to get people to do things (like learn about AI Safety) you have to offer them something valuable.
Here’s one of the posters we used when I was in charge of marketing for the Columbia EA group:
It’s a pretty graphic, but what valuable thing is it offering?
The message is “scan this link to talk about AI.” To be fair, people like talking about AI. We had applicants.
But we didn’t attract talented ML students.
If you want to attract talented people, you have to know what they want. Serious and ambitious people probably don’t want to sit around having philosophical discussions. They want to build their careers.
Enter ML @ Berkeley, a thriving group of 50 ML students who put 15 hours per week into projects and courses to become better at ML. No one gets paid – not even the organizers. And they are very selective. Only around 7% of applicants get in.
Why is this group successful? For starters, they offer career capital. They give students projects that often turn into real published papers. They also concentrate talent. Ambitious people want to work with other ambitious people.
AI safety student groups should consider imitating ML @ Berkeley.
I’m not saying that we should eliminate philosophical discussions and replace them with resume boosting factories. We still want people to think AI Safety and X-risk are important. But discussions don’t need to be the primary selling point.
Maybe for cultivating conceptual researchers, it makes more sense for discussions to be central. But conceptual and empirical AI Safety research are very different. ML students are probably more interested in projects and skill-building.
More rigorous programming could also make it easier to identify talent.
- Talking about AI is fun, but top ML researchers work extremely hard. Rigorous technical curricula can filter out the ones that are driven.
- There is nothing like a trial by fire. Instead of trying to predict in advance who will be good at research, why not have lots of people try it and invest in those that do well?
USC field builders are experimenting with a curriculum that, in addition to introducing X-risk, is packed-full with technical projects. In their first semester, they attracted 30 students who all have strong ML backgrounds. I’m interested in seeing how this goes and would be excited about more AI Safety groups running experiments on these lines.
People could also try:
- checking whether grad students are willing to supervise group research projects.
- running deep learning courses and training programs (like Redwood’s MLAB)
- running an in-person section of intro to ML Safety (a technical course that covers safety topics).
As far as I can tell, no one has AI safety university field-building all figured out. Rather than copying the same old discussion group model, people should experiment with new approaches. A good start could be to imitate career development clubs like ML @ Berkeley that have been highly successful.
Thanks to Nat Li and Oliver Zhang for thoughts and feedback and to Dan Hendrycks for conversations that inspired this post.