Fei Fei Li

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223 appearances 1 recordings 1 series first heard Dec 2024 last heard Dec 2024

Fei Fei Li’s voice in public audio — every appearance, attributed to the second.

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Armchair Expert with Dax Shepard · Fei Fei Li (on a human-centered approach to AI) · 11 Dec 2024 podcast
I live in Silicon Valley. You have to claim yourself to be a CEO of something. Yeah, exactly. Bob and Jean. It's incredible. I don't even think their kids knew about this till they read my book. Wow.
Yeah, so Princeton is a quirky school. It didn't have minors. So it has these certificates, but they're just minors. I had a computational mathematics as well as an engineering physics minors.
It's actually really interesting. I never necessarily thought I would be a physicist, but I wanted to be a scientist. That was almost a sacred calling for me.
It was an identity. For some reason, this girl who works in dry cleaners just wanted to be a scientist. And then I loved physics. But I loved physics for its audacity and curiosity. I didn't necessarily feel I'm married to a big telescope inquiry. So I was just reading a lot. And what really caught my attention was the physicist's I admired so much Einstein, Schrodinger, Roger Penrose.
They actually are curious beyond just atomic world. They were curious about other things, especially life, intelligence, minds. And that was immediately the no point and the eye opener for me. I realized I love that.
Right. But for me, it was the science of intelligence. I always believed it's the science of intelligence that will unite our understanding of both the brain and the computers.
I know we're 15 minutes away from Caltech here, 20 minutes. So I was choosing among MIT, Stanford, and Caltech. And honest to God, I almost chose Caltech because of the weather. Yeah, that's fair. It was so balmy. And the vibe. Yes, the turtles, the garden-like campus. And of course, I walk into this building. I think it was Moore Building at Caltech. And guess whose photo was there?
It was Albert Einstein. And I was like, what? It turned out he was visiting. And of course, there was Richard Feynman, the Feynman Lecture. So I just followed these physicists, apparently. And New Jersey was cold. And also, I really have an issue with cold because my mom's illness is exacerbated by cold. So every winter, she suffers a lot.
So I have this negative affinity to coldness coming from taking care of my mom. So coming to Southern California, I was like, oh my God, I love this place. Did your parents come with you? Later they did. In the middle of my grad school, they did. Were you worried about that, leaving? I had to switch from being on site to remotely run the dry cleaning.
The dry cleaning was stabilized that the customers are all returning customers. So my mom would be able to handle with one part-time worker. And Bob Sabella was doing bills for my mom. Oh my God. Yeah. He was just helping me. And another thing he helped me as a young graduate student, I would be entering the world of writing scientific articles. That's pretty intense.
He would still proofread my English for me, all my papers.
Yes. The prelude of the North Star was my education from physics is always about asking the right questions. If you go to the Nobel Museum in Stockholm, there is an Einstein quote about much of science is asking the right questions. Once you ask the right questions, solutions follow. You'll find a way for solutions. Some people call it hypothesis driven thinking.
I've always been just thinking this way. So as I was studying computational neuroscience, as well as artificial intelligence at Caltech, I was always kind of seeking what is that audacious question I wanted to ask. And of course, my co-advisor Pietro Perona and Christophe Koch, they were great mentors guiding me. But many things start to converge, not just my own work, but the field.
People working on visual intelligence from neuroscience, from AI, start to orbit around this idea that the ability to recognize things all kinds of objects is so critical for human visual intelligence. When I say all kinds of objects, I really mean all kinds. I'm sitting here in your beautiful room. There's table, bottles, couch, pillow, a globe, books, flower, vase, plants.
Okay, that's behind you. That's behind you. It's about to eat you. Shirts and skirts and boots and TV. So the ability for humans to be able to learn such a complicated world of objects.
It's so fascinating, and I started to believe, along with my advisors, this is a critical problem for the foundation of intelligence. And that really started to become the North Star of my scientific pursuit, is how do we crack the problem of object recognition?
Right. So that's the parallel story I was writing the book about.
Right. Well, the field of computing, thanks to people like Alan Turing von Neumann, was starting during World War II time, basically. Of course, for the The world of AI, a very important moment was 1956, when what we now call the founding fathers of AI, like Marvin Minsky, John McCarthy, Claude Shannon, they get together under, I believe, a U.S. government grant.
DARPA funded to have a summer-long workshop at Dartmouth with a group of computer scientists. At that point, the field of AI was barely kind of boring, not boring yet. They got together and wrote this memo or this white paper about artificial intelligence. In fact, John McCarthy, one of the group leaders, was responsible for coining the term artificial intelligence. Okay.
It could calculate.
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