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Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction

Machine Learning Street Talk (MLST)

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Adapting Intelligence: The Quest for Continual Learning

This chapter examines the challenges and strategies associated with continual learning in neural networks, particularly combating catastrophic forgetting. It highlights the role of dynamic control in robotics that enables adaptive learning with various tools and the importance of sample efficiency for real-world applications. Additionally, it discusses the interplay between representational learning and theoretical frameworks in artificial intelligence, drawing parallels with insights from brain research.

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