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The reason why I would recommend people get a machine learning PhD, if they’re in a position to do so, is that this is where we are currently the most talent constrained. So, at DeepMind, and for the technical AI safety team, we’d love to hire more people who have a machine learning PhD or equivalent experience, and just get them to work on AI safety.

Jan Leike

Want to help steer the 21st century’s most transformative technology? First complete an undergrad degree in computer science and mathematics. Prioritize harder courses over easier ones. Publish at least one paper before you apply for a PhD. Find a supervisor who’ll have a lot of time for you. Go to the top conferences and meet your future colleagues. And finally, get yourself hired.

That’s Dr Jan Leike’s advice on how to join him as a Research Scientist at DeepMind, the world’s leading AI team.

Jan is also a Research Associate at the Future of Humanity Institute at the University of Oxford, and his research aims to make machine learning robustly beneficial. His current focus is getting AI systems to learn good objective functions in cases where we can’t easily specify the outcome we actually want.

How might you know you’re a good fit for this kind of research?

Jan says to check whether you get obsessed with puzzles and problems, and find yourself mulling over questions that nobody knows the answer to. To do research in a team you also have to be good at clearly and concisely explaining your new ideas to other people.

We also discuss:

  • Where do Jan’s views differ from those expressed by Dario Amodei in episode 3?
  • Why is AGI alignment one of the world’s most pressing problems?
  • Common misconceptions about artificial intelligence
  • What are some of the specific things DeepMind is researching?
  • The ways in which today’s AI systems can fail
  • What are the best techniques available today for teaching an AI the right objective function?
  • What’s it like to have some of the world’s greatest minds as coworkers?
  • Who should do empirical research and who should do theoretical research
  • What’s the DeepMind application process like?
  • The importance of researchers being comfortable with the unknown.

The 80,000 Hours podcast is produced by Keiran Harris.


In the longer term, the vision for this project is that we’re thinking about when we actually do build AGI, what would be the objective function? The idea here is that this is kind of a small step into the direction of learning what humans value or what you would want, say, a household robot that you buy, what you want them to do, and in a way that you don’t need to be an expert in reinforcement learning, but you can just give feedback in other forms, like really easy to do for humans.

I think, ultimately, we want to take feedback in a way that humans want to give it. Right now, what we do is we have two video clips, and you kind of say is the left better, is the right better, or are they kind of the same. But, I think it will be great if we have better ways of giving feedback.

There’s lots of interesting projects going on in DeepMind, and one of the perks of working at DeepMind is that you kind of get to see them as they unfold. So, last year, there was a lot of stuff going on with AlphaGo. Most of that happened before I was even working there. But, right now, a lot of people are really excited about StarCraft, and we recently released a research environment for that so that other people can also work on that. Yeah, I think there’s lots of other really exciting things going on.

I think, overall, something that is really important is that you should be comfortable with navigating a space that you don’t really understand very well, because researchers kind of necessarily are on the frontier of human knowledge and things that we understand, so you have to be comfortable with the unknown. In some ways, this is in contrast to the skills that an undergraduate degree selects for, where you’re really basically learning about things that we understand well, and then it’s more you need to be able to remember them and you need to be able to understand them quickly rather than dealing with the unknown.

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About the show

The 80,000 Hours Podcast features unusually in-depth conversations about the world's most pressing problems and how you can use your career to solve them. We invite guests pursuing a wide range of career paths — from academics and activists to entrepreneurs and policymakers — to analyse the case for and against working on different issues and which approaches are best for solving them.

The 80,000 Hours Podcast is produced and edited by Keiran Harris. Get in touch with feedback or guest suggestions by emailing [email protected].

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