Chapter
Limitations of Current Approaches to Programming AI
50:19 - 59:04 (08:45)
The current deep learning approaches used to program artificial intelligence systems are capable of building hierarchies of concepts from raw sensory information. However, it is unlikely that these approaches can scale up to meet the demands of more complex AI systems in the future.
Clips
The current hardware can be scaled up to develop systems with greater intelligence, but there is a need to make it faster and bigger.
50:19 - 53:14 (02:55)
Summary
The current hardware can be scaled up to develop systems with greater intelligence, but there is a need to make it faster and bigger. The hope is to develop better approaches to programming the computers for greater scalability.
ChapterLimitations of Current Approaches to Programming AI
EpisodeMelanie Mitchell: Concepts, Analogies, Common Sense & Future of AI
PodcastLex Fridman Podcast
The current deep learning approaches allow us to take raw sensory information and automatically build up hierarchies of concepts.
53:14 - 56:16 (03:02)
Summary
The current deep learning approaches allow us to take raw sensory information and automatically build up hierarchies of concepts. It helps to recognize situations like people riding bikes holding a leash and the dog running alongside.
ChapterLimitations of Current Approaches to Programming AI
EpisodeMelanie Mitchell: Concepts, Analogies, Common Sense & Future of AI
PodcastLex Fridman Podcast
Deep learning in visual perception aims to extract features from the input data at different levels of complexity, from simple edges to complex shapes and objects, ultimately enabling the system to identify the most important parts of an image.
56:16 - 59:04 (02:47)
Summary
Deep learning in visual perception aims to extract features from the input data at different levels of complexity, from simple edges to complex shapes and objects, ultimately enabling the system to identify the most important parts of an image.