#72 - Miles Brundage and Tim Hwang

**Craig Cannon** (0:00)
Hey, how's it going? This is Craig Cannon, and you're listening to Y Combinator's podcast. Today's episode is with Miles Brundage and Tim Hwang. Miles is an AI Policy Research Fellow with the Strategic AI Research Center at the Future of Humanity Institute. He's also a PhD candidate in Human and Social Dimensions of Science and Technology at Arizona State University.
And Tim is the Director of the Harvard-MIT Ethics and Governance of AI Initiative. He's also a visiting associate at the Oxford Internet Institute and a fellow of the Knight-Stanford Project on Democracy and the Internet.
And this is Tim's second time on the podcast. He was also on episode 11, and I'll link that one up in the description. All right, here we go. All right, guys, I think the most important and pressing question is, now that cryptocurrency gets all the attention and AI is no longer the hottest thing in technology, how are you dealing with it?

**Miles Brundage** (0:51)
Yeah, Ben Hamner of Kaggle had a good line on this. He said something like, great thing about cryptocurrency is people no longer ask me about whether there's an AI bubble.
And yeah, it's hard to compete with the crypto bubble or phenomenon, whatever you want to call it.

**Tim Hwang** (1:08)
I think it's actually a good development, right? I mean, the history of AI is like all of these winners, and having another hype cycle to kind of balance it out might actually be a good thing.

**Craig Cannon** (1:18)
Yeah, absolutely. Let's talk about your paper to start off, Miles.

**Miles Brundage** (1:21)
Sure.

**Craig Cannon** (1:21)
So yeah, what is it called and where do you go from there?

**Miles Brundage** (1:23)
Yeah, it's called The Malicious Use of Artificial Intelligence, and then there's a subtitle like Forecasting, Prevention, and Mitigation, and it's attempting to be the most comprehensive analysis to date of the various ways which AI could be deliberately misused, so not just things like bias and lack of fairness in an algorithm that are not necessarily intentional, but deliberately using it for things like fake news generation and, you know, combining AI with drones to carry out terrorist attacks or offensive cyber security applications. And, you know, the essential argument that we make is that that needs to be taken seriously, the fact that AI is a dual use or even omni-use technology, and that similar to other fields like biotechnology and computer security, we need to think about whether there are norms that account for that, so things like responsible disclosure when you find out about a new vulnerability is something that's pervasive in the computer security community, but hasn't yet been seriously discussed for things like adversarial examples where you might want to say, hey, there's this new misuse opportunity or way in which you could fool this commercial system that is currently, you know, running driverless cars or whatever, and so there should be some more discussion about those sorts of issues.

**Craig Cannon** (2:36)
Okay, and so is it going into the technical details, or is it kind of a survey of where you think things are now?

**Miles Brundage** (2:41)
Yeah, so most of it's a general survey, but then there's like an appendix on different areas, like, you know, how to deal with the privacy issues, how to deal with, you know, the robustness issues, and, you know, different places to look for lessons.

**Craig Cannon** (2:53)
Okay, and so Tim, have you been focusing on any of this stuff while you've been here at Oxford, or is your work totally unrelated?

**Tim Hwang** (3:00)
It's somewhat related, actually. I mean, I would say that I'm mostly been focusing on what you might think of as a subset of the problems that Miles is working on, where he's sort of saying, look, AI isn't going to be inherently used for good, and in fact, there's lots of intentional ways to use it for bad, right? And one of the things I've been thinking about is the sort of interface between these techniques and the problems of disinformation, and whether or not you think these techniques will be used to make ever more believable fakes in the future and what that does to the media ecosystem. So I would say it's like a very particular kind of bad actor use that Miles is talking about.

**Craig Cannon** (3:32)
And so when you're doing this research for both of these topics, are you digging into actual code? Like how are you spotting this in the wild?

**Tim Hwang** (3:41)
So I mean, my methodology is really kind of focused on looking at what is the research that's coming out right now and like trying to extrapolate what the uses might be, right? Because I think one of the really interesting things we're seeing in the AI space is that it is becoming more available for people to do, right? Like you've got these cloud services, you know, we've got the tools are like widely available now.