Lenny's Podcast: Product | Career | Growth
The Godmother of AI on jobs, robots, and why world models are next | Dr. Fei-Fei Li
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A lot of people call you the godmother of AI. The work you did actually was the spark that brought us out of AI winter. In the middle of 2015, middle of 2016, some tech companies avoid using the word AI because they were not sure if AI was a dirty word. 2017-ish was the beginning of companies calling themselves AI companies. There's this line, I think this was when you're presenting to Congress. There's nothing artificial about AI. It's inspired by people, it's created by people, and most importantly, it impacts people. It's not like I think AI will have no impact on jobs or people. In fact, I believe that whatever AI does, currently or in the future, is up to us. It's up to the people. I do believe technology is a net positive for humanity, but I think every technology is a double-edged sword. If we're not doing the right thing, as a society, as individuals, we can screw this up as well. You had this breakthrough inside of just, okay, we can train machines to think like humans, but it's just missing the data that humans have to learn as a child. I chose to look at artificial intelligence through the lens of visual intelligence, because humans are deeply visual animals. We need to train machines with as much information as possible on images of objects, but objects are very, very difficult to learn. A single object can have infinite possibilities that is shown on an image. In order to train computers with tens and thousands of object concepts, you really need to show it millions of examples. - Today, my guest is Dr. Faye Faitley, who's known as the Godmother of AI. Faye Faye has been responsible for and at the center of many of the biggest breakthroughs that sparked the AI revolution that we were currently living through. She spearheaded the creation of ImageNet, which was basically her realizing that AI needed a ton of clean label data to get smarter. And that data set became the breakthrough that led to the current approach to building and scaling AI models.
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