Brain Intelligence: Continual Learning of Representational Models

This lecture explores the intersection of neuroscience and artificial intelligence, focusing on the continual learning of representational models after deployment. The instructor discusses the limitations of current artificial neural networks in capturing the rapid adaptability and intelligence of the human brain. Key topics include the generalization behavior of convolutional neural networks, object recognition, unsupervised motion segmentation, and the interdisciplinary cross-fertilization between neuroscience and AI. Ongoing research aims to enhance machines' ability to disentangle physical properties of objects and model appearance changes. The lecture emphasizes the importance of continual learning post-deployment as a paradigm for understanding brain intelligence.

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