User Experience for Predictive Machine Learning in the Consumer IoT
A big promise of the Internet of Things is that by analyzing millions of new sources of data from embedded, networked devices our experience of the world becomes better and more efficient. The environment automatically predicts our behavior and adjusts to it, anticipating problems and intercepting them before they occur. The notion is seductive and almost magical: an automatic espresso machine that starts a fresh latte as you’re thinking it’s a good time for coffee; office lights that dim when it’s sunny and electricity is expensive; a taco truck that arrives just as the crowd in the park is getting peckish. Exciting in theory, this promise is rather unspecific in the details. Exactly how will our experience of the world, our ability to use all the collected data, become more efficient and more pleasurable? However, we don’t have good examples for designing user experiences of predictive analytics. This walk will discuss the importance of predictive behavior to consumer Internet of Things products and services, describe UX challenges to creating such behavioral systems, and suggest patterns for addressing those challenges.
Mike Kuniavsky is a user experience designer, researcher, and author. A twenty-year veteran of digital product development, Mike designs products, business processes, and services at the leading edge of technological change. Prior to joining PARC, he co-founded several successful user experience centered companies, including ThingM, which designs and manufactures ubiquitous computing and Internet of Things products, and Adaptive Path, a well-known design consultancy. He specializes in multi-device interactions, cloud-based service design, and design of hardware products connected to cloud-based services. His background includes design for social analytics, consumer electronics, appliances, image retrieval, RGB LEDs, and financial services.