The computational power of “wetware”

Since its inception, the quest for automated intelligence has been inspired by biological brains, laying the groundwork for artificial neural networks.  Recent advances in machine learning have dramatically advanced the capabilities of such artificial intelligences. Some neural network systems, including those for visual recognition and for language processing, show remarkable similarities to brain activity. While “brain-inspired”, however, the components and architectures of AI systems still differ enormously in complexity from their biological counterparts. What aspects of biological computation do AI systems miss, and how might biology inform future AI design? 
 

Adrienne Fairhall is a professor of physiology and biophysics at the University of Washington in Seattle, and a visiting professor at the ENS as part of the Tocqueville-Fulbright Chair. Since January, she has been hosted in the Laboratoire de Neurosciences Cognitives et Computationnelles (LNC2) at the DEC. She will be teaching and sharing her research in conferences and workshops until June 2022. Read more