An interview-style conversation where two researchers recount their academic roots and motivations for entering AI. Speaker A describes a background in physics, math and electrical engineering and how AI seemed fundamentally applicable across disciplines; Speaker B contrasts traditional symbolic AI (search-based methods) with machine learning and says neural networks felt like the satisfying direction to pursue.
Chapters
So what I really liked in school was physics and math. And so from there, it seemed pretty natural to study engineering, which is applying physics and math in the real world. And actually then after my undergrad in electrical engineering, actually wasn't so sure what to do, because literally anything engineering seemed interesting to me. Like, understanding how anything works seems interesting. Trying to build anything is interesting. And in some sense, artificial intelligence won out because it seemed like it could somehow help all disciplines in some way. And also it seemed somehow a little more at the core of everything. Like, if you think about how a machine can think, then maybe that's more the core of everything else than picking any specific discipline.
I was very interested in artificial intelligence, and so I took classes in artificial intelligence. But a lot of what I was seeing there was just very not satisfying. Like, it was a lot of kind of, you know, depth first search, breadth first search, alpha beta pruning and all these things. And I was, like, not understanding how. Like, I was not satisfied. And so when I was seeing neural networks for the first time, like in machine learning, which is kind of this term that I think is more technical and not as well known in kind of, you know, most people talk about artificial intelligence, Machine learning was more kind of a technical term, I would almost say. And so I was dissatisfied with artificial intelligence. When I saw machine learning. I was like, this is the AI that I want to kind of spend time on.
