When we think about how artificial intelligence works, we often make comparisons to the human brain. Terms like neural networks seem to cement this connection. How much do computer models and the human brain have in common, especially when it comes to processing language? Dr. Alona Fyshe is a computer scientist with research interests that combine computational linguistics, machine learning and neuroscience. She draws on all of these areas to study the ways in which human brains and computer models process language. We talk about her journey from the arts to artificial intelligence, moving from industry to academia, women in AI, the remarkable ways in which human brains and computers represent concepts similarly and what this means for future work in this field and for the future of medicine.  “It’s very clear neural network models do not do the same thing the human brain does….but…two systems (the human brain and computers) “trained” on completely different data and for completely different prediction problems, show up with similar representations…I think that’s interesting and worth further exploration.” – Alona Fyshe Dr. Alona Fyshe is an Assistant Professor in the departments of Computing Science and Psychology at the University of Alberta. She is a fellow of the Alberta Machine Intelligence Institute and holds a CIFAR AI Chair. She received her BSc and MSc in Computing Science from the University of Alberta, and a PhD in Machine Learning from Carnegie Mellon University. Dr. Fyshe uses machine learning to analyze brain images collected while people read, which allows her to study how humans represent the meaning they encounter in text, and how they combine words to understand higher-order meaning. She also studies how computers learn to represent meaning when trained on text or images.