Chapter
Understanding Symbolic AI and Self-supervised Learning for Deep Sense of the World
26:03 - 31:26 (05:23)
The podcast discusses the importance of common sense in building a deep understanding of the world and how self-supervised learning can play a role in achieving this understanding.
Clips
The speaker discusses the concept of self supervised learning and how it relates to the process of object annotation, posing deeper questions about the nature of objects themselves.
26:03 - 27:20 (01:16)
Summary
The speaker discusses the concept of self supervised learning and how it relates to the process of object annotation, posing deeper questions about the nature of objects themselves.
ChapterUnderstanding Symbolic AI and Self-supervised Learning for Deep Sense of the World
Episode#206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
PodcastLex Fridman Podcast
The development of symbolic artificial intelligence that can understand concepts and build hierarchies and graphs on top of them in order to reason and interpret 2D images is a significant challenge that requires further understanding of the capabilities of the human mind.
27:20 - 30:28 (03:08)
Summary
The development of symbolic artificial intelligence that can understand concepts and build hierarchies and graphs on top of them in order to reason and interpret 2D images is a significant challenge that requires further understanding of the capabilities of the human mind.
ChapterUnderstanding Symbolic AI and Self-supervised Learning for Deep Sense of the World
Episode#206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
PodcastLex Fridman Podcast
The general problem of self-supervised learning for humor detection is difficult to solve, although a supervised learning route can be utilized to construct a dataset and predict whether there is humor present in a given text.
30:28 - 31:26 (00:57)
Summary
The general problem of self-supervised learning for humor detection is difficult to solve, although a supervised learning route can be utilized to construct a dataset and predict whether there is humor present in a given text.