Penetrative AI: Making LLMs Comprehend the Physical World
CoRR(2023)
摘要
Recent developments in Large Language Models (LLMs) have demonstrated their
remarkable capabilities across a range of tasks. Questions, however, persist
about the nature of LLMs and their potential to integrate common-sense human
knowledge when performing tasks involving information about the real physical
world. This paper delves into these questions by exploring how LLMs can be
extended to interact with and reason about the physical world through IoT
sensors and actuators, a concept that we term "Penetrative AI". The paper
explores such an extension at two levels of LLMs' ability to penetrate into the
physical world via the processing of sensory signals. Our preliminary findings
indicate that LLMs, with ChatGPT being the representative example in our
exploration, have considerable and unique proficiency in employing the embedded
world knowledge for interpreting IoT sensor data and reasoning over them about
tasks in the physical realm. Not only this opens up new applications for LLMs
beyond traditional text-based tasks, but also enables new ways of incorporating
human knowledge in cyber-physical systems.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要