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arXiv · 2412.18161

VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities

Shray Mathur·Noah van der Vleuten·Kevin Yager·Esther Tsai
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Abstract

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion (VISION) by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an X-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instrument and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex -- a synthetic extension to the cognition of scientists -- may radically transform scientific practice and discovery.

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Shray Mathur, Noah van der Vleuten, Kevin Yager, Esther Tsai. 2024-12-24. VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities. https://doi.org/10.1088/2632-2153%2Fadd9e4

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