Chapter
Understanding the Mismatch Between Artificial Neural Nets and Biological Plausibility
00:00 - 07:48 (07:48)
The mismatch between artificial neural nets and biological plausibility is an interesting area of study for understanding how brains work and for developing new ideas on how to incorporate the differences into artificial neural nets. Training neural nets to focus on causal explanations is an area where progress can be made.
Clips
The mismatch between artificial and biological neural networks holds the potential to help researchers understand how brains work and incorporate these differences into developing new ideas that can improve artificial neural networks.
00:00 - 02:38 (02:38)
Summary
The mismatch between artificial and biological neural networks holds the potential to help researchers understand how brains work and incorporate these differences into developing new ideas that can improve artificial neural networks.
ChapterUnderstanding the Mismatch Between Artificial Neural Nets and Biological Plausibility
EpisodeYoshua Bengio: Deep Learning
PodcastLex Fridman Podcast
Professor Yoav Artzi believes that training neural nets differently, such as teaching them to focus on causal explanations and encouraging joint learning between language and the world it references, can improve language and world modeling in artificial intelligence.
02:39 - 07:48 (05:08)
Summary
Professor Yoav Artzi believes that training neural nets differently, such as teaching them to focus on causal explanations and encouraging joint learning between language and the world it references, can improve language and world modeling in artificial intelligence.