Jitendra Malik: Computer Vision | Lex Fridman Podcast #110

ℹ️ EPISODE INFO

Released

July 21, 2020

Duration

1h 41m

Channel

Lex Fridman Podcast

Watch on YouTube

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📝 EPISODE DESCRIPTION

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. Support this podcast by supporting our sponsors:

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EPISODE LINKS: Jitendra's website: https://people.eecs.berkeley.edu/~malik/ Jitendra's wiki: https://en.wikipedia.org/wiki/Jitendra_Malik PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 3:17 - 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 CONNECT:

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🎤 SPEAKERS

• Lex Fridman

  host

• Jitendra Malik

  guest

🧾 EPISODE SUMMARY

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Jitendra Malik, Jitendra Malik: Computer Vision | Lex Fridman Podcast #110 explores jitendra Malik explains why real computer vision is still hard Jitendra Malik and Lex Fridman explore why computer vision is fundamentally more difficult than it appears from human experience, and why the field repeatedly underestimates that difficulty. Malik argues that vision is deeply tied to cognition, prediction, and action, and that current deep learning systems solve only parts of the problem, often with unrealistic amounts of supervision and data. They discuss autonomous driving, 3D understanding, video and long-form activity recognition, and child-like learning as core open challenges. Malik also reflects on brain–computer compute differences, multimodal and embodied learning, the limits of end-to-end supervised learning, and what constitutes good research problems and realistic pathways to human-level intelligence.

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