Chapter
The importance of building general AI systems
1:18:45 - 1:25:29 (06:44)
The goal of building general AI systems is not just to perform better in one or multiple tasks, but also to prepare for future challenges. Building a multitask network, with a core trained on multiple tasks and different heads for each, can lead to better performance overall.
Clips
Competitive co-evolution is a fascinating topic where neural networks compete against each other, and the possibility of discovering something new is exciting.
1:18:45 - 1:20:33 (01:47)
Summary
Competitive co-evolution is a fascinating topic where neural networks compete against each other, and the possibility of discovering something new is exciting. The behavior of zebras and gazelles is an example of competitive co-evolution.
ChapterThe importance of building general AI systems
Episode#177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation
PodcastLex Fridman Podcast
Multi-task learning in AI involves building a general model that can be applied to multiple tasks and challenges, resulting in better performance and representations for each individual task.
1:20:33 - 1:23:29 (02:56)
Summary
Multi-task learning in AI involves building a general model that can be applied to multiple tasks and challenges, resulting in better performance and representations for each individual task. This approach involves a core model trained on specific tasks, as well as several heads trained on different tasks simultaneously for improved embeddings and representations.
ChapterThe importance of building general AI systems
Episode#177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation
PodcastLex Fridman Podcast
The difficulty of building a human language system and the human vision system from an engineering and evolutionary perspective is discussed, along with the equivalent of these systems in AI, particularly in the area of language and vision.
1:23:29 - 1:25:29 (02:00)
Summary
The difficulty of building a human language system and the human vision system from an engineering and evolutionary perspective is discussed, along with the equivalent of these systems in AI, particularly in the area of language and vision. Datasets with visual and verbal components can be used to advance both systems.