The emerging world of ML sensors

Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-modal and leveraging ungodly amounts of processing power to do it. At the far opposite extreme from the world of hyperscale transformers and giant dense nets is the fast-evolving world of TinyML, where the goal is to pack AI systems onto small edge devices. My guest today is Matthew Stewart, a deep learning and TinyML researcher at Harvard University, where he collaborates with the world's leading IoT and TinyML experts on projects aimed at getting small devices to do big things with AI. Recently, along with his colleagues, Matt co-authored a paper that introduced a new way of thinking about sensing.