The Future of IoT and RF Intelligence through Large Language
5The Internet of Things (IoT) and RF systems are entering a new era, one that is driven not just by better connectivity, but by contextual understanding, reasoning, and adaptability. This talk explores how Large Language Models (LLMs) are unlocking new dimensions in IoT and RF intelligence, enabling systems that donβt just sense and report, but interpret, decide, and communicate. We will discuss how LLMs can bridge the gap between human intent and machine execution, and between low-level signals and high-level insights, driving new applications of LLMs as optimizers and interpreters within the context of IoTs.
Learning Objectives:
Identify the core functions of IoT and describe the limitations of traditional sensing and reporting mechanisms.
Explain how Large Language Models (LLMs) enhance contextual understanding and reasoning capabilities in IoT and RF environments.
Illustrate how LLMs can serve as interpreters and optimizers within real-world IoT workflows, from low-level signal interpretation to high-level decision-making.
Examine the role of LLMs in bridging human intent and machine execution in IoT systems, including their impact on communication and responsiveness.
Critique potential applications and challenges of integrating LLMs into IoT and RF architectures, particularly in terms of scalability, accuracy, and autonomy.
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Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.