Lex Fridman Podcast

#110 – Jitendra Malik: Computer Vision

Jul 21, 2020

Jitendra Malik, a distinguished professor at UC Berkeley and a pioneer in computer vision, shares his insights on the complexities of replicating human visual perception. He discusses the challenges of Tesla's Autopilot, emphasizing the gap between human and computer processing. Malik explores how integrated approaches and knowledge schemas can enhance action recognition. He critiques current evaluation methods, advocating for measures that reflect true understanding. Additionally, he highlights the importance of interdisciplinary research and the need for children's experiences in AI development.

01:42:04

Creator website

Episode guests

Jitendra Malik

AI Chapters

Episode notes

Intro

4min

The Challenges of Computer Vision and Autonomous Driving

18min

Comparative Analysis of Human Brain and Modern Computer Power

2min

Navigating the Challenges of Computer Vision

11min

Enhancing Action Recognition through Knowledge and Schemas

4min

Redefining Benchmarks in Computer Vision

9min

Navigating the Complexities of Computer Vision

27min

Redefining AI Evaluation: Beyond the Turing Test

2min

Challenges in Visual Scene Understanding

22min

10.

Finding Solvable Problems in Research and Interdisciplinary Connections

4min

Jitendra Malik is a professor at Berkeley and one of the seminal figures in the field of computer vision, the kind before the deep learning revolution, and the kind after. He has been cited over 180,000 times and has mentored many world-class researchers in computer science.

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Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:
00:00 – Introduction
03:17 – Computer vision is hard
10:05 – Tesla Autopilot
21:20 – Human brain vs computers
23:14 – The general problem of computer vision
29:09 – Images vs video in computer vision
37:47 – Benchmarks in computer vision
40:06 – Active learning
45:34 – From pixels to semantics
52:47 – Semantic segmentation
57:05 – The three R’s of computer vision
1:02:52 – End-to-end learning in computer vision
1:04:24 – 6 lessons we can learn from children
1:08:36 – Vision and language
1:12:30 – Turing test
1:16:17 – Open problems in computer vision
1:24:49 – AGI
1:35:47 – Pick the right problem