no code implementations • 16 May 2023 • Evan King, Haoxiang Yu, Sangsu Lee, Christine Julien
We implement and evaluate Sasha in a hands-on user study, showing the capabilities and limitations of LLM-driven smart homes when faced with unconstrained user-generated scenarios.
no code implementations • 10 Apr 2023 • Haoxiang Yu, Hsiao-Yuan Chen, Sangsu Lee, Sriram Vishwanath, Xi Zheng, Christine Julien
We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.
1 code implementation • 24 Mar 2023 • Evan King, Haoxiang Yu, Sangsu Lee, Christine Julien
We first explore the feasibility of a system that places an LLM at the center of command inference and action planning, showing that LLMs have the capacity to infer intent behind vague, context-dependent commands like "get ready for a party" and respond with concrete, machine-parseable instructions that can be used to control smart devices.
no code implementations • 24 Mar 2021 • Sangsu Lee, Xi Zheng, Jie Hua, Haris Vikalo, Christine Julien
We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences.