Q: …Okay, so I want to respect your time and not go too long over an hour, but I’d love to ask you some slightly more personal questions. One about Douglas Hofstadter.

You open up your book [AI: A Guide for Thinking Humans] with an interesting anecdote about him giving a talk at Google and basically telling all the Google engineers that AI could be calamitous, but not in the ways that we typically hear about. It seems like he was more worried about the possibility that we would succeed in building AI and this would mean that current approaches worked and would sort of trivialize human intelligence in some sense. We would lose the magic of our thinking.

I’m wondering if you could tell us a bit more about that and then, you seem to have different concerns. I’m wondering about your journey of deviating from his thinking.
Melanie Mitchell: Interestingly, Hofstadter’s worry stems from him reading some of Ray Kurzweil’s books about the Singularity. They range between very science fiction-like to somewhat compelling arguments about technology and its exponential increase in progress. Hofstadter was pretty worried that something like what Kurzweil was describing might actually happen. He kept saying he didn’t want this to happen in the lifetime of his children. He didn’t want the human race to be made irrelevant because these machines are now much smarter, much more creative than humans. He didn’t think it was going to happen but he was kind of worried about it. He actually organized two different conferences about this topic and he invited Ray Kurzweil and a bunch of other people. It was kind of an early version of what you might call the current things that we see on AI future predictions and AI alignment kind of stuff.

That was one of the things that really worried him and this would have been in the 1980s or 1990s. He started organizing these conferences in the 1990s. Kurzweil had already come up with the singularity idea. I was a lot less worried about the singularity scenario for some reason. I just didn’t see AI going in that direction at all. Kurzweil’s arguments were all about hardware and the exponential increase in hardware. But clearly, software is different from hardware and doesn’t follow the same exponential rules. Our knowledge and our ideas about how intelligence works in the biological world were not increasing exponentially and so I didn’t see us being able to replicate biological AI anytime soon.

There was actually a program at DARPA in the 1990s where they were trying to recreate the intelligence of a cat using neural networks and it was a total failure. [Mitchell seems to misremember here & referring to an obscure 2008 program where $7.02^$5₂₀₀₈m was spent on an IBM spiking-neural net chip hardware project, DARPA’s System of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project, Ananthanarayanan et al 2009] It really impressed me that that’s actually super hard and we’re just so far away from that. How can we think that in just like 20–30 years we’re going to get to human-level intelligence? Hofstadter and I have had a lot of discussions about this. He’s obviously disturbed by these large language models and their behavior. He’s not sure what to think, like a lot of us. You see their behavior and it’s amazing at least some of the time but it’s also you realize we don’t have intuitions about how to deal with statistical models at that scale.

I don’t think of myself as an AI skeptic necessarily. I work in AI. I think it’s a very hard problem but I feel like a lot of times people who criticize current approaches are kind of labeled as overall AI skeptics. I even saw an article that called people like that AI deniers. I don’t like being labeled as an AI skeptic or AI critic because I do think that AI is really interesting and is going to produce a lot of really interesting insights and results. I just don’t think it’s going to be as easy to achieve something like human-level intelligence.

Q: I have a couple of questions about analogies because that’s a huge area of your thought that I’d just love to know more about. I know that with Hofstadter, your PhD thesis was on analogy making.

I had never thought of analogies as being particularly insightful into human intelligence but I’m curious what your interest in analogies is and what we all stand to learn from the study of analogy.
M. Mitchell: I think most people have a narrow view of what analogy means. We all take IQ tests or SATs that have these single-word analogies and those are a lot less interesting. But I think analogy is much broader than that. It’s when we notice some kind of abstract similarity between two different situations or two different events. If you’ve ever had somebody tell you a little story about something that happened in their life and you say “oh the same thing happened to me”, you’re making an analogy. It’s not the same thing but it’s reminding you of something that is abstractly similar.

In science, analogy in scientific invention is paramount. There’s a nice article I saw today about this recent result from DeepMind about matrix multiplication. The idea was that the researchers saw that matrix multiplication could be mapped into a kind of game-playing framework and therefore they had these reinforcement systems that could be applied to that framework. But it was that initial leap of analogy that allowed them to apply these AI systems.

We also are constantly, in our language, making analogies. One of Hofstadter’s examples is anytime there’s a scandal we say “oh it’s another Watergate” or we call it something-gate. That’s an analogy. That kind of thing is just all over the place. I think it’s a really important part of transfer learning which is sort of the Holy Grail of machine learning. It’s about learning something in one domain and applying it to another domain. That’s really about making analogies. It’s about saying what is it that I’ve learned that’s important here that I can now apply to this new situation.

Q: I have one final question. Do you have advice for someone who wants to both be engaged in academic research but also sort of keep their head above the water and write general articles and be able to engage with a general audience? I’m impressed by the ability to be writing NeurIPS papers but also writing books for a general audience and articles. I’m curious whether you have any advice for someone who would like to do that and how you sort of resist the pressure to get caught in the academic rabbit hole where it’s just like always the next paper because there’s always something new to do. How do you balance that with writing a New York Times op-ed or something?
Mitchell: I haven’t really published papers in NeurIPS and all those places as much as many people who are prominent in AI and machine learning. I try to have time to think which is often hard if you’re under pressure all the time to publish the next paper. It’s a challenge because if you’re in an academic position and you’re on a tenure track, there’s all kinds of pressure to publish and get citations for your publications and publish in top-tier venues. Some of the people I know who are really successful at writing for the popular audience often aren’t publishing as much in academic venues.

Publishing for a popular audience is hard because imagine trying to explain your research to someone in your family who isn’t a technical person. It’s pretty hard to do. Learning how to do that is like learning how to have a theory of mind of people. I think that’s also the key to being a good teacher. It’s about having a theory of mind of the students and sort of knowing what they don’t know and making sure that you address that. It’s a challenge and it takes practice.

Q: Thank you so much for coming on the podcast. This was a very wonderful and enlightening conversation. Where can our audience find more of your work?
M M: They can go to my webpage which is MelanieMitchell.me or they can follow me on Twitter at @MelMitchell1.
Q: We’ll put links into the show notes. I just want everyone to explore your work and enjoy it as much as we have.
M: Great, well thank you very much. It’s been a lot of fun.