Chris Olah

University major
Cause area
AI Safety
Job title
ML Researcher


Follow Chris on Twitter: @CH402

Majored in

Didn’t complete university (see story below).

Current role:

ML researcher at Anthropic.

Chris’ background

Most people who want to pursue a research career feel they need a degree to get taken seriously. But Chris not only doesn’t have a PhD, but doesn’t even have an undergraduate degree. After dropping out of university to help defend an acquaintance who was facing bogus criminal charges, Chris started independently working on machine learning research, and eventually got an internship at Google Brain, a leading AI research group. - 80,000 Hours podcast interview

Listen to a podcast or read the relevant sections in the transcript below:

Table of contents

Chris’ background [00:00:57]

Rob Wiblin: Today, I’m speaking with Chris Olah. Chris is a machine learning researcher currently focused on neural network interpretability. Until last December, he led OpenAI’s interpretability team, but he recently left with some colleagues to help start a new AI lab focused on large models and safety. Before OpenAI, he spent four years at Google Brain developing tools to visualize what’s going on in neural networks. He was hugely influential at Google Brain, being the second author on the launch of the DeepDream article back in 2015. I think the DeepDream images are something that basically just about everyone has seen at this point.

Rob Wiblin: He also helped pioneer feature visualization, activation atlases, building blocks of interpretability, TensorFlow, and he even co-authored the famous paper Concrete Problems in AI Safety. On top of all of that, in 2018, he helped found the academic journal Distill, which is dedicated to publishing clear communication of technical concepts. Chris is himself a writer who is popular among many listeners to the show, and his blog has attracted millions of readers by trying to explain cutting-edge machine learning in highly accessible ways. He’s managed to do all of this without a degree, because he dropped out of college in 2009 to defend a friend against bogus terrorism charges. In 2012, Chris took a $100,000 Thiel Fellowship, a scholarship designed to encourage gifted young people to go straight into research or entrepreneurship rather than go to university.

What are you working on at the moment and why do you think it’s important? [00:00:57]

Chris Olah: I think one of the craziest things about machine learning is that we have all these systems that can do these amazing things — they can classify images, translate text, write essays, recognize your voice, generate videos… And yet we can’t go and produce these systems directly. No human being knows how to write a computer program directly that does those kinds of things. Instead, we go and produce systems that do these things, and we have no idea what those systems are doing. So the thing that I’ve always felt has just been the question that I’ve been obsessed with, and just feels like the burning question in machine learning to me, is: How in the wide world are these systems going and doing all of these crazy things that we don’t know how to do? I care about that for safety reasons, and honestly, I also just care about it because it seems like this incredibly crazy thing about the world that I just want to understand.

Rob Wiblin: Yeah, that makes a lot of sense. It sounds like, looking at your CV, it’s been something like an eight-year journey for you, working on this problem. Trying to pick away at it, and taking neural networks from being these black boxes to things that we can properly understand and build on.

Chris Olah: Yeah. it’s not the only thing that I’ve done for the last eight years, but it’s definitely been the biggest one, and I’ve tried lots of things. A lot of the things I tried early on didn’t work very well, but over time I think we’ve really developed. Not just me, but lots of other people and lots of collaborators that I’ve worked with have really been able to get to a point where we can actually very significantly understand neural networks, and can actually just look at their weights and read entire algorithms for doing things that we didn’t really know how to do before off of them. That’s been really cool to see.

Should people go to university? [00:13:21]

Chris Olah: I applied for the Thiel Fellowship, which is a program that provides financial support for people under the age of 20 to go and work on ambitious projects or do unusual things, and I got it, and I was like, “Well, I have two options. One is to go back to university, and the other is I can work on whatever I want for two years.” It turns out that wasn’t a difficult decision.

Chris Olah: I had a lot of experience in doing things cutting against pressure, from the previous stuff. But I think at the time, I framed it — and especially framed it to other people — as, well, I can do this for two years, and then I can still go back to university. That seems like an amazing opportunity. One other thing that comes into play here is it’s actually much easier to do unusual things when you’re validated by a third party. I think people, when they hear about the Thiel Fellowship they’re like, “Ah, the high-value thing is that they’re providing funding.” That’s certainly part of it, but I think that actually the higher value thing was actually like, adults in my life totally came around once I was given $100,000 to go and work on stuff, in a way that they really were not supportive in beforehand. I think there’s also just a really big effect in terms of legitimizing an untraditional path and making it easier.

Chris Olah: I get a lot of emails from people asking me if they should go to university. I think it’s maybe the single most common question I get asked. I think for almost everyone who emails me, they should go to university. The reason that I think that is I think that if you want to benefit from going and doing something else, you have to have a lot of, I think, self-discipline and willingness to go and work hard on things, and self-motivation to work hard on things without an external forcing function. I think that often people don’t have this, and then this kind of thing doesn’t work as well for them.

Chris Olah: On the other hand, I think for the people — and maybe to give some more context, — I think a lot of the people who I saw really thrive in the Thiel Fellowship, some had already before the age of 20 done undergrad degrees. So there were those ones. But I think a lot of people had done really significant personal projects involving software or science or something like this. I think that’s actually a pretty good test. If you have been able to, out of self-motivation, go and do your own large personal project — and obviously you are in a privileged enough position to be able to support yourself — then you’re likely to be able to do well in something like the Thiel Fellowship, or taking a year off, or taking a few years off. But if you aren’t, it’ll be much more challenging.

Combining skillsets to greater effect [00:38:13]

Chris Olah: I think probably the most useful thing I’ve extracted has been thinking about the Pareto frontier of skills. For example, a lot of my early contributions to machine learning were basically being able to create these really helpful illustrations of complicated ideas. What skills did I need to do that? Well, I needed both to understand machine learning, and I needed to be able to draw. I wasn’t an exceptionally good artist or scientific illustrator, and I wasn’t exceptionally knowledgeable about machine learning. But very plausibly, for a while, I was the person in the world who was the best of the intersection of machine learning and drawing. If you think of these two-dimensional plots of different skills, or three-dimensional plots of different skills, and you think about the Pareto frontier, very often society is good at producing people who are optimized for a particular skill set or set of skills that society has really validated as useful.

Chris Olah: We create entire pipelines training people. But I think that often, if you can find useful intersections of skills that aren’t these couple of standard skills, there can be a lot of value. And it’s much easier to go and have a big impact, and often have a big counterfactual impact. When I’m talking to people about their own careers, I often try to frame it in terms of, what are the skills that they’re cultivating, and what do we think the Pareto frontier with regards to these skills looks like? Do we think that there’s places where, rather than going and becoming the world’s best at one skill, they can produce a lot of value by being at an intersection of skills that other people don’t have?

Rob Wiblin: Yeah, that’s really interesting. Thinking about it theoretically, I suppose part of the reason is just that there’s so many combinations of two different things that you could throw together. So the space of possible combinations is vastly larger, and so you have a lot more to choose from. It also means that you could be the only person who’s interested in X and Y, if you choose two things that are sufficiently distant. Then you have a truly unique skill set, and you might just stumble on something that no one else has even tried to find.

Chris Olah: Exactly, and now the problem is the space is exponentially big, and you want to not just find an intersection, but the intersection has to be useful. So you have to have some taste in picking the skills that you develop. But I think that there are lots of opportunities like this, and that often it’s much less competitive than going and being good at one of the skills that society already really values as a thing to optimize for.

Why people should send more cold emails

Chris Olah: I get a lot of cold emails, and 99% of them are terrible. They’re like, “Can you do my homework for me?” or, “Can you answer this basic question that I could Google for one minute and answer?” I think people get this impression that cold emailing doesn’t work, because of course, if you send emails like that, people are overwhelmed and aren’t going to respond. Or, even if you just very generically are like… If you send a nicely written email and you’re like, “I’m trying to get into machine learning. Can you do a half-hour phone call with me to talk about how to do that?” Even that, you’re not very likely to get a response from. But I think the thing that people miss is that if you write really good cold emails, it’s actually not that hard to be the best email I received that week.

Chris Olah: And I think that if you’re willing to invest energy in understanding what a researcher or a group is working on, and you’re specifically referring to their papers, and you have thoughtful questions about things, yeah, I think that people will pay a lot of attention to that. Then I think that it will… It very often works well. I think there’s a big gap in what people mean when they talk about cold emails, and I think that if you’re willing to put in the work, and if you just genuinely really care about what somebody is doing, and have put in the work to understand it, and can talk about it really intelligently… That’s going to come through. It’s a much more compelling reason for the person to talk to you than other things.

Chris Olah: I think there’s a lot of people who are trying to look at how to get into machine learning, and what they do is they send lots of emails to people, or they email famous people. I think what you should actually be doing is trying to figure out who you would be really excited to work with, and really understand their work. Ideally pick somebody who’s a little bit less famous maybe, and then reach out to that person with an email where you’ve put a lot of work into it being clear that you’ve read their work, and connecting your interests to theirs, and things like this. There’s a number of emails that have been really important for me, where I spent a week writing them. I think that was a totally worthwhile investment. I think that’s not how people usually think about cold emails.