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

• URL: http://arxiv.org/abs/2412.18161v1
• Date: Tue, 24 Dec 2024 04:37:07 GMT
• Title: VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities
• Authors: Shray Mathur, Noah van der Vleuten, Kevin Yager, Esther Tsai,
• Abstract summary: generative AI presents an opportunity to bridge this knowledge gap. We present a modular architecture for the Virtual Scientific Companion (VISION) 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.
• Score: 0.19736111241221438
• License:
• 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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