一个对话式的大型语言模型导师,可以加速在常规生物分析工作流程中的机器学习方法开发
An T H Le1, Thomas Shvekher1, Lewis Nguyen1
1Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada.
Chembiochem : a European journal of chemical biology
|September 29, 2025
概括
本研究介绍了一种对话式人工智能助理,可以帮助没有机器学习 (ML) 背景的科学家设计ML工作流程. 该工具通过模型开发指导用户,使ML可用于实验化学和其他科学领域.
科学领域:
- 实验化学 实验化学 实验化学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 在实验化学中,机器学习 (ML) 的采用受到缺乏用户培训的阻碍.
- 现有的AutoML平台缺乏对非ML专家提供必要的教学支持.
- 弥合差距需要可访问的工具来设计ML工作流程.
研究的目的:
- 开发一个对话式的人工智能助理,通过ML工作流程设计指导科学家.
- 降低在数据丰富的实验环境中采用ML的进入壁垒.
- 创建一个可定制的系统,用于构建特定领域的人工智能助理.
主要方法:
- 一个轻量级的对话助理,由OpenAI的GPT-4o提供动力.
- 通过Gradio接口与结构化的系统提示模拟教学推理的部署.
- 通过两个案例研究进行演示:图像分类和回归预测.
主要成果:
- 没有先前ML经验的科学家使用助手成功开发了工作模型.
- 助手有效地指导用户定义ML目标,评估数据,选择模型和评估指标.
- 生成的注释Python代码促进了模型实现.
结论:
- 交谈助理显著降低了在实验科学中采用ML的障碍.
- 该系统提供了一个可定制的框架,用于创建特定领域的AI导师.
- 这种方法提高了ML在数据丰富的分析工作流中的实用性.
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