Chapter
The Importance of Data Engineering in Machine Learning
1:21:59 - 1:30:28 (08:28)
To improve the neural network, constant improvement of the data landscape is necessary. Companies should weigh the benefits of building their own neural network training hardware from scratch.
Clips
The success of neural networks depends on constantly improving the data landscape by iterating the network and automating the steps as much as possible.
1:21:59 - 1:25:19 (03:20)
Summary
The success of neural networks depends on constantly improving the data landscape by iterating the network and automating the steps as much as possible. However, the networks may be too big for certain applications, such as cars.
ChapterThe Importance of Data Engineering in Machine Learning
Episode#162 – Jim Keller: The Future of Computing, AI, Life, and Consciousness
PodcastLex Fridman Podcast
The effectiveness of self-driving cars and the role of driving data in developing them remains an open question.
1:25:19 - 1:27:30 (02:11)
Summary
The effectiveness of self-driving cars and the role of driving data in developing them remains an open question. Additionally, the question of whether we have the right order of magnitude for the compute necessary to develop these vehicles is still an open issue.
ChapterThe Importance of Data Engineering in Machine Learning
Episode#162 – Jim Keller: The Future of Computing, AI, Life, and Consciousness
PodcastLex Fridman Podcast
This episode talks about the debate on whether companies should build their own neural network training hardware from scratch, and how the use of self-driving cars could be a safer alternative to human drivers.
1:27:30 - 1:30:28 (02:57)
Summary
This episode talks about the debate on whether companies should build their own neural network training hardware from scratch, and how the use of self-driving cars could be a safer alternative to human drivers.