Chapter
Self-Supervised Learning for Human-Significant Problems
1:22:50 - 1:27:25 (04:35)
This podcast discusses the possibility of applying self-supervised learning to human-significant problems such as autonomous vehicles and robotics applications. It raises questions about the limits of self-play and neural networks in language models in the context of AGI.
Clips
The discussion covers important problems that can be solved using self-supervised approaches, such as self-play for autonomous vehicles, robotics applications, and simulation.
1:22:50 - 1:24:52 (02:02)
Summary
The discussion covers important problems that can be solved using self-supervised approaches, such as self-play for autonomous vehicles, robotics applications, and simulation. Also, it questions the limits of self-play and neural networks within AGI and highlights recent breakthroughs achieved in natural language processing.
ChapterSelf-Supervised Learning for Human-Significant Problems
Episode#144 – Michael Littman: Reinforcement Learning and the Future of AI
PodcastLex Fridman Podcast
The belief that supporters of the opposition are not intelligent is common in politics.
1:24:52 - 1:27:25 (02:32)
Summary
The belief that supporters of the opposition are not intelligent is common in politics. Natural language generation capabilities of AI can be easily flawed and limited to imitation, suggesting either a lack of intelligence among people or that our daily actions may not be that complex.