Chris Olah
speaker
254 appearances
1 recordings
1 series
first heard Nov 2024
last heard Nov 2024
Chris Olah’s voice in public audio — every appearance, attributed to the second.
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I think philosophy is actually a really good subject if you are kind of fascinated with everything. Because there's a philosophy of everything. So if you do philosophy of mathematics for a while, and then you decide that you're actually really interested in chemistry, you can do philosophy of chemistry for a while. You can move into ethics or philosophy of politics.
I think towards the end, I was really interested in ethics primarily. So that was what my PhD was on. It was on a kind of technical area of ethics, which was... Ethics, where worlds contain infinitely many people, strangely. A little bit less practical on the end of ethics.
And then I think that one of the tricky things with doing a PhD in ethics is that you're thinking a lot about the world, how it could be better, problems. And you're doing a PhD in philosophy. And I think when I was doing my PhD, I was kind of like... This is really interesting. It's probably one of the most fascinating questions I've ever encountered in philosophy. And I love it.
But I would rather see if I can have an impact on the world and see if I can do good things. And I think that was around the time that AI was still probably not as widely recognized as it is now. That was around 2017, 2018. I had been following progress and it seemed like it was becoming kind of a big deal.
And I was basically just happy to get involved and see if I could help because I was like, well, if you try and do something impactful, if you don't succeed, you tried to do the impactful thing and you can go be a scholar and feel like you tried. And if it doesn't work out, it doesn't work out. And so then I went into AI policy at that point.
At the time, this was more thinking about sort of the political impact and the ramifications of AI. And then I slowly moved into sort of AI evaluation, how we evaluate models, how they compare with like human outputs, whether people can tell like the difference between AI and human outputs. And then when I joined Anthropic, I was more interested in doing sort of technical alignment work.
And again, just seeing if I could do it and then being like, if I can't, then, you know, that's fine. I tried. sort of the way I lead life, I think.
I think that sometimes people do this thing that I'm like not that keen on where they'll be like, is this person technical or not? Like you're either a person who can like code and isn't scared of math or you're like not. And I think I'm maybe just more like, I think a lot of people are actually very capable of working these kinds of areas if they just like try it.
And so I didn't actually find it like that bad. In retrospect, I'm sort of glad I wasn't speaking to people who treated it like it. You know, I've definitely met people who are like, whoa, you like learned how to code. And I'm like, well, I'm not like an amazing engineer. Like I'm surrounded by amazing engineers. My code's not pretty. But I enjoyed it a lot.
And I think that in many ways, at least in the end, I think I flourished like more in the technical areas than I would have in the policy areas.
Yeah. And I feel like I have kind of like one or two sticks that I hit things with, you know, and one of them is like arguments and like, you know, so like just trying to work out what a solution to a problem is and then trying to convince people that that is the solution and be convinced if I am wrong. And the other one is sort of more empiricism.
So like just like finding results, having a hypothesis, testing it. And I feel like a lot of policy and politics feels like it's layers above that. Like somehow I don't think if I was just like, I have a solution to all of these problems. Here it is written down. If you just want to implement it, that's great. That feels like not how policy works.
And so I think that's where I probably just like wouldn't have flourished is my guess.
Yeah, I think it depends on what they want to do. And in many ways, it's a little bit strange where I've... I thought it's kind of funny that I think I ramped up technically at a time when... Now I look at it and I'm like, models are so good at assisting people with this stuff that it's probably easier now than when I was working on this.
So part of me is like, I don't know, find a project and see if you can actually just carry it out is probably my best advice. I don't know if that's just because I'm very project-based in my learning. I don't think I learn very well from, say, courses or even from books, at least when it comes to this kind of work.
The thing I'll often try and do is just have projects that I'm working on and implement them. And, you know, and this can include like really small, silly things. Like if I get slightly addicted to like word games or number games or something, I would just like code up a solution to them because there's some part of my brain and it just like completely eradicated the itch.
You know, you're like once you have like solved it and like you just have like a solution that works every time, I would then be like, cool, I can never play that game again. That's awesome. Yeah.
Yeah.
Yeah. And then it's also just like trying things. Like part of me is like, maybe it's that attitude that I like as the whole... figure out what seems to be like the way that you could have a positive impact and then try it.
And if you fail and you, in a way that you're like, I actually like can never succeed at this, you'll like know that you tried and then you go into something else and you probably learn a lot.
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