Chapter
Applications of self-supervised learning in computer vision-based autonomous driving
1:42:50 - 1:53:06 (10:15)
This episode discusses the successes and challenges of computer vision-based autonomous driving systems, particularly on highways and freeways, and the need for AGI to overcome the limitations of edge cases. Also, possible applications of self-supervised learning in this context are explored.
Clips
In this podcast, the speakers discuss the edge cases that pose problems for the widespread adoption of autonomous driving and the potential applications of self-supervised learning in addressing those cases.
1:42:50 - 1:46:28 (03:37)
Summary
In this podcast, the speakers discuss the edge cases that pose problems for the widespread adoption of autonomous driving and the potential applications of self-supervised learning in addressing those cases. They also touch on the current success of computer vision-based autonomous driving on highways and freeways.
ChapterApplications of self-supervised learning in computer vision-based autonomous driving
Episode#206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
PodcastLex Fridman Podcast
Tesla is moving from a camera and radar-based autonomous driving system to a vision-based one, which the company believes will push the technology even farther.
1:46:28 - 1:48:34 (02:05)
Summary
Tesla is moving from a camera and radar-based autonomous driving system to a vision-based one, which the company believes will push the technology even farther. The use of these technology will make their way for autonomous driving in the industry.
ChapterApplications of self-supervised learning in computer vision-based autonomous driving
Episode#206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
PodcastLex Fridman Podcast
This transcript discusses the potential of self-supervised learning in solving driving problems by considering driving as a robot on robot versus the environment problem, rather than involving humans.
1:48:34 - 1:53:06 (04:32)
Summary
This transcript discusses the potential of self-supervised learning in solving driving problems by considering driving as a robot on robot versus the environment problem, rather than involving humans. The transcript also highlights Tesla's advancements in this field, indicating that AGI might be built to solve driving problems.