Learning from a learning thermostat: lessons for intelligent systems for the home

By Rayoung Yang, Mark W. Newman. Ubiquitous Computing, 2013

311 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems and Management, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/c2407ecdc03eba2f5f964ba1fa944a6db96e42a0

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Information Systems and Management

In Human-Computer Interaction

Citations in the corpus

Incremental on-line learning: A review and comparison of state of the art algorithms (2018, background): “This is particularly interesting for smart home products [2, 3].”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “As a result of the important role sensemaking plays in user experience and design, many researchers have explored sensemaking around automated systems, in domains from thermostats and home sensors [58, 63] to self tracking devices [30, 47, 64] and other personal data [62] to classification algorithm”
User Perceptions of Smart Home IoT Privacy (2018, background): “More recently, Yang and Newman [64] investigated user experiences living with a Nest learning thermostat to inform the development of intelligent home technologies.”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “Research explored the need for [111] and provision of explanations in physical space, such as smart environments [177, 178, 179], the office [26], smart homes [18, 30, 131, 183] and on the move [90, 116].”
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material (2017, background): “They may inadvertently display an inability to understand the intent behind users’ behavior, which results in “intelligent” features being perceived as useless and unintuitive [57].”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “Providing system explanations may also undermine user perceptions when users lack the attentional capacity to process complex explanations, for example while they are executing a demanding task [12,81].”
Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers (2015, background): “Interaction with intelligent machines Much research has investigated how people respond to intelligent machines such as automated manufacturing technologies [23, 36, 44], recommender systems [6], context-aware systems [3, 43], agents [35], and robots [30].”
Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015, background): “As Yang and Newman found when studying users of learning thermostats [46], a system that appears to disregard user feedback deters users from continuing to provide feedback.”
Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques (2021, background): “In this context, research works have been oriented at more advanced building control systems based on Artificial Intelligence (AI), taking into account several aspects such as the user preference over time [7].”