This podcast episode explores the work of Tomaso Poggio, a professor at MIT, and his research on understanding intelligence. It discusses the possibilities and limitations of creating intelligence systems without a comprehensive understanding of the human brain. The episode delves into the differences between biological and artificial neural networks in deep learning and the concept of face recognition in the brain. It also explores the use of functional MRI in analyzing brain activity, the levels of abstraction in studying intelligence, and the puzzle of neural networks and over-parameterization. The podcast touches on the curse of dimensionality, the use of generative adversarial networks, and the future of AI education. It concludes with discussions on the timeline for achieving general intelligence, the engineering of ethical machines, the hard problem of consciousness, and the potential breakthroughs inspired by neuroscience. The episode also highlights the Center for Brains, Minds, and Machines at MIT and the qualities needed for success in science and engineering careers.
