Neural Architectures for Embodied AI and Cognition

This lecture by the instructor covers neural architectures for embodied AI and cognition, exploring topics such as neural networks, neuromorphic computing, cognitive architectures, and the limitations of traditional AI approaches. It delves into the integration of computing and robotics, the challenges of real-world complexity, and the advancements in artificial intelligence for robotics. The lecture also discusses biological intelligence, signal processing in biological systems, and the principles of attractor dynamics and neural state machines. Furthermore, it addresses autonomous learning, reinforcement learning, and the performance of neuromorphic hardware in intelligent systems.