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 similarities between artificial neural networks and real neurons in the brain's ventral pathway, emphasizing the importance of rapid adaptation to unstructured environments. Key topics include object recognition, task transfer, adversarial attacks, and common corruptions in convolutional neural networks. Ongoing research on unsupervised motion segmentation for continual object learning is also presented, highlighting the potential for interdisciplinary collaboration between neuroscience and AI.