Learning through AI’s winters and springs: unexpected truths on the road to AGI

I am VP of Research at DeepMind. I joined DeepMind in 2014 to pursue new solutions for artificial general intelligence. Currently, I oversee the strategy for DeepMind’s exploratory research efforts, leading teams exploring new innovations in AI that might address the open questions that today's techniques cannot answer. Before joining DeepMind in early 2014, I had found my way into AI research obliquely. After an undergraduate degree in religion and philosophy from Reed College, I veered off-course (on-course?) and became a computer scientist. My PhD with Yann LeCun, at NYU, focused on machine learning using Siamese neural nets (often called a 'triplet loss' today), face recognition algorithms, and on deep learning for mobile robots in the wild. My thesis, 'Learning Long-range vision for offroad robots', was awarded the Outstanding Dissertation award in 2009. I spent a post-doc at CMU Robotics Institute, working with Drew Bagnell and Martial Hebert, and then became a research scientist at SRI International, at the Vision and Robotics group in Princeton, NJ. After joining DeepMind, then a small 50-person startup that had just been acquired by Google, my research focused on a number of fundamental challenges in AGI, including continual and transfer learning, deep reinforcement learning for robotics and control problems, and neural models of navigation (see publications). I have proposed neural approaches such as policy distillation, progressive nets, and elastic weight consolidation to solve the problem of catastrophic forgetting. In the broader AI community, I am founder and Editor-in-Chief of a new open journal, TMLR. I sit on the executive board of CoRL, am a fellow of the European Lab on Learning Systems (ELLIS), and a founding organizer of NAISys (Neuroscience for AI Systems). I also serves as a CIFAR advisor and have previously sat on the Executive Board for WiML (Women in Machine Learning).