This week on If/Then, we’re sharing an episode of What’s Your Problem?, a show from Pushkin Industries where entrepreneurs, engineers, and scientists talk about the future they’re trying to build—and the problems they must solve to get there. Hosted by former Planet Money co-host Jacob Goldstein, each conversation explores the challenges and breakthroughs shaping the next wave of innovation.

In this episode, Goldstein speaks with Fei-Fei Li, Stanford computer scientist, former Chief Scientist of AI and Machine Learning at Google, and one of the most influential figures in the field of computer vision. Li reflects on her pioneering work developing ImageNet, the massive dataset that helped spark the modern AI revolution, and the “north star” questions that have guided her research from neuroscience to machine learning.

Together, they trace how a single insight about how humans see the world led to a paradigm shift in artificial intelligence—and how Li’s vision continues to shape the way we teach machines to see, learn, and collaborate with us.

More Resources:

• Fei Fei Li

• Stanford Institute for Human-Centered Artificial Intelligence (HAI)

• ImageNet

• What’s Your Problem?

If/Then is a podcast from Stanford Graduate School of Business that examines research findings that can help us navigate the complex issues we face in business, leadership, and society.

Chapters:

(00:00:00) Introducing “What’s Your Problem?”

Kevin Cool introduces the Pushkin Industries podcast hosted by Jacob Goldstein.

00:00:45 — What Is Computer Vision?

Jacob Goldstein and Fei-Fei Li explain how machines learn to see and interpret images.

00:03:18 — Real-World Uses of AI Vision

Li shares examples from healthcare, robotics, and environmental science.

00:05:06 — Discovering the Science of Seeing

How human vision research inspired Li’s lifelong “north star” in AI.

00:09:56 — Creating ImageNet

Li builds a massive image database that transforms computer vision research.

00:13:29 — Defining 30,000 Visual Concepts

How cognitive science helped shape ImageNet’s massive scale.

00:16:41 — Building the Dataset by Hand

Li's team uses global crowdsourcing to label millions of images.

00:19:38 — The 2012 Breakthrough

Jeff Hinton’s neural network shatters records and sparks the deep learning era.

00:22:19 — Data Meets Hardware

Li reflects on how big data and GPUs converged to power modern AI.

00:24:55 — Lightning Round with Fei-Fei Li

Quick insights on resilience, mentorship, and the future of human-AI collaboration.

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What's Your Problem: "Teaching Computers to See"