Teaching things to think

What if smart devices could reason about their state, like a thermostat that explains its schedule, or a light bulb that chooses a color to suit a mood?

That’s the subject of my IEEE PerCom ‘25 paper, “Teaching Things To Think: Bootstrapping Local Reasoning for Smart(er) Devices”. We proposed a method for synthesizing training data to distill small language models for the task, leveraging a combination of formal methods and generative models. We ultimately trained and evaluated models for two “thoughtful things” – a lamp and a thermostat – then evaluated their performance at explaining and mutating their state in response to unconstrained user commands.

My long-term ambition with this work is to create meaningfully useful “thoughtful things” – like a camera that adjusts and explains its settings to help you become a better photographer, or a synthesizer that helps you learn sound design using a similar form of self-awareness. Thoughtfulness in this case implies a capacity for introspection, perception, and an attentiveness to the needs of users.

We were proud to receive a Best Paper (Runner-Up) award for this work. You can find the full paper on IEEE Xplore.