Chapter
End-to-End Deep Learning for Autonomous Vehicles.
1:37:20 - 1:41:14 (03:54)
The use of end-to-end deep learning for autonomous vehicles has potential to make predictions more holistic and annotation tasks easier, but also presents challenges in creating accurate models that are able to perform numerous tasks. Additionally, the use of these models could lead to new tools and an ecosystem for autonomous vehicle development.
Clips
Tesla has the potential to monetize its chips by opening up and selling them, which could generate significant revenue and benefit from an ecosystem of tooling.
1:37:20 - 1:38:41 (01:21)
Summary
Tesla has the potential to monetize its chips by opening up and selling them, which could generate significant revenue and benefit from an ecosystem of tooling. However, the company needs to decide where its advantages lie and avoid competing with itself.
ChapterEnd-to-End Deep Learning for Autonomous Vehicles.
Episode#132 – George Hotz: Hacking the Simulation & Learning to Drive with Neural Nets
PodcastLex Fridman Podcast
The use of neural networks in autonomous vehicle technology allows for a more holistic approach to annotation and prediction, providing more accurate representations of the environment.
1:38:41 - 1:41:14 (02:32)
Summary
The use of neural networks in autonomous vehicle technology allows for a more holistic approach to annotation and prediction, providing more accurate representations of the environment. However, challenges such as the flat world hypothesis and sensor integration may require fundamental changes to the current approach.