This talk will lay out the challenges and point to some potential approaches for the user experience design of dynamic, adaptive, predictive devices (such as the Nest Thermostat, the Amazon Echo, the Edyn water monitor, etc.) that use machine learning to create predictive models of people and sensors. The Internet of Things promises that by analyzing data from many IoT devices our experience of the world becomes better and more efficient. The environment predicts our behavior, anticipates problems, and intercepts them before they occur. The notion is seductive: an 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 power is cheap. However, we don’t have good examples for designing user experiences of predictive analytics. Attendees will see examples of several different systems and leave with a list of UX challenges to creating behavioral systems, along with potential approaches to addressing those challenges.
Expert: Mike Kuniavsky
Mike Kuniavsky leads user-experience design in the Innovation Services Group at PARC, a Xerox company. A 20-year veteran of digital product development, Mike designs products, business processes, and services at the leading edge of technological change. Prior to PARC, Mike cofounded several successful UX-centered companies, including ThingM, which designs and manufactures ubiquitous computing and Internet of Things products, and Adaptive Path, a well-known design consultancy. He has worked with top technology companies—including Samsung, Sony, Nokia, Whirlpool, and Qualcomm—to design new products, guide product strategy, and create user-centered design and development cultures. Mike is the author of Observing the User Experience: A Practitioner’s Guide to User Research and Smart Things: Ubiquitous Computing User Experience Design.