The document discusses how machine learning can help architect Internet of Things (IoT) systems for widespread consumer adoption. It describes three examples of using machine learning with IoT data: (1) identifying patterns of risky drivers to adjust insurance premiums, (2) predicting short-term driving behavior to improve road safety, and (3) using long-term driving history with recurrent neural networks to provide customized nudging to change driver behavior over time. The document argues that machine learning can create value from IoT data and benefit consumers by making systems safer, lowering costs, and incentivizing good behaviors.