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:- BetterHelp: http://betterhelp.com/lex- ExpressVPN: https://www.expressvpn.com/lexpod

If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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 - Introduction03:17 - Computer vision is hard10:05 - Tesla Autopilot21:20 - Human brain vs computers23:14 - The general problem of computer vision29:09 - Images vs video in computer vision37:47 - Benchmarks in computer vision40:06 - Active learning45:34 - From pixels to semantics52:47 - Semantic segmentation57:05 - The three R's of computer vision1:02:52 - End-to-end learning in computer vision1:04:24 - 6 lessons we can learn from children1:08:36 - Vision and language1:12:30 - Turing test1:16:17 - Open problems in computer vision1:24:49 - AGI1:35:47 - Pick the right problem