Today we're joined by Andreas Tolias, Professor of Neuroscience at Baylor College of Medicine and Principal Investigator of the Neuroscience-Inspired Networks for Artificial Intelligence organization.
We caught up with Andreas to discuss his recent perspective piece, "Engineering a Less Artificial Intelligence," which explores the shortcomings of state-of-the-art learning algorithms in comparison to the brain. The paper also offers several ideas about how neuroscience can lead the quest for better inductive biases by providing useful constraints on representations and network architecture. We discuss the promise of deep neural networks, the differences between inductive bias and model bias, the role of interpretability, and the exciting future of biological systems and deep learning.
