This presentation identifies challenges to the user experience design of smart devices (such as the Nest Thermostat, the Amazon Echo, the Edyn water monitor, etc.) that use machine learning to anticipate the needs of people and environments and adapt in response, and point to some potential design patterns to help address those challenges. The Internet of Things promises that by analyzing data from many sensors over time our experience of the world becomes better and more efficient. Our environment can predict our behavior, anticipate problems and needs, and maximize the chances of a desirable end result.
Though this notion of effortless automation is seductive (espresso machines that start just as you’re thinking it’s a good time for coffee; office lights that dim when it’s sunny and power is cheap), we don’t have good examples for designing user experiences of predictive systems. As a result, today it’s much easier to create such systems that are confusing, unpredictable and uncontrollable.