Machine Learning Sensors: Truly Data-Centric AI

"Paradoxically, data is the most under-valued and de-glamorised aspect of AI" -- Google research authors of "Data Cascades in High-Stakes AI." "Data is food for AI" -- Andrew Ng, UC Berkeley professor and pioneer of the data-centric AI philosophy. Machine learning has seen a bifurcation towards both smaller and larger models in recent years. Large-scale language models with hundreds of billions of parameters are being released regularly, and, with no signs of performance saturation, we can expect to see this trend continue. On the flip side, the field of tiny machine learning (TinyML) -- deploying machine learning models on resource-constrained microcontrollers -- is also starting to take hold. Commercial applications for TinyML already exist, from keyword spotting in smartphones (e.g., "Hey Siri" and "OK Google") to person detection for controlling intelligent lighting, HVAC, and security systems.