Delving into mortal computation and cybernetics, the podcast explores biomimetic intelligence and the benefits of in-memory computing for neural simulations. It discusses minimizing variational free energy in biological and artificial systems, as well as the concept of cybernetic variety in problem-solving. The episode also touches on active inference, Bayesian surprise, and the limitations of current robotics technology in achieving adaptive intelligence.

In this engaging discussion, Alexander Ororbia, a professor at the Rochester Institute of Technology and head of the Neural Adaptive Computing Laboratory, shares fresh insights on enhancing AI capabilities beyond mere scaling. He contrasts the efficiencies of biological systems with current AI, proposing biomimetic techniques to address challenges like sparse rewards and catastrophic forgetting. Delving into concepts like 'mortal computation' and the intricacies of consciousness, Ororbia advocates for neuromorphic computing, inviting new perspectives on artificial sentience and cognition.