Jitendra Malik, Professor of EECS at UC Berkeley discusses building AI from the ground-up, focusing on sensorimotor capabilities before language. Topics include limitations of current AI technology, the importance of physical interaction in language models, the role of vision in robotic locomotion, progress and challenges in computer vision, the potential impact of AI in medicine and healthcare, and advice for PhD students pursuing a career in AI research.

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 – 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