Modern AI and the state of interdisciplinary exchange with neuroscience
Director of Augmented Intelligence Research
Artificial neural networks have emerged a workhorse of practical AI systems, and machine learning is quickly becoming standard practice in software engineering. But how much do these advances connect back to the study of neural computation and learning in biological brains? What are the prospects for the fruitful exchange of ideas across disciplines as AI tech veers towards the practical and away from the biologically plausible? Is there any hope in studying synthetic systems for inspiration about the principles governing natural systems? In this talk we will review some of the basic observations and consider these and other questions.
Greg Corrado is an alum of the Stanford neurosciences PhD program. He works at the nexus of artificial intelligence, computational neuroscience and scalable machine learning, and has published in fields ranging from behavioral economics, to particle physics, to deep learning. He is currently a principal scientist and research director within Google AI, and the co-founder of the Google Brain team. In his time at Google, he has worked to put AI directly into the hands of users via products like RankBrain and Smart Reply, and into the hands of developers via open-source software releases like TensorFlow and word2vec. He currently leads several research efforts in advanced applications of machine learning, ranging from natural human communication to expanded healthcare availability. Before coming to Google, he worked at IBM Research on neuromorphic silicon devices and large-scale neural simulations.