聊天GP-我吗? 没有
1Vanderbilt University, Nashville, Tennessee, United States.
Journal of medical imaging (Bellingham, Wash.)
|October 30, 2024
概括
2024年的生成人工智能 (GenAI) 工具可以在没有编码的情况下简化科学过程. 本研究探讨了定制大型语言模型 (LLM) 对五个研究实验室工作流的影响.
科学领域:
- 科学研究中的人工智能
- 实验室工作流程优化工作流程优化
- 生成型人工智能应用程序
背景情况:
- 生成性AI (GenAI) 技术正在迅速发展.
- 人工智能融入科学研究是一个日益增长的趋势.
- 无代码GenAI对实验室工作流程的影响仍未得到充分研究.
研究的目的:
- 评估2024年GenAI工具对科学过程的潜在影响.
- 在研究实验室环境中展示定制大型语言模型 (LLM) 的效果.
- 评估人工智能在五个不同的实验室工作流程中的实际应用.
主要方法:
- 探索两个不同的AI工具.
- 使用自定义的大型语言模型 (LLM).
- 在五个不同的研究实验室工作流程中应用.
- 在不需要特定编码专业知识的情况下进行评估.
主要成果:
- 基因人工智能工具为增强科学过程提供了巨大的潜力.
- 定制的LLM可以有效地集成到现有的实验室工作流程中.
- 影响在不同的研究任务中有所不同,显示了好处和需要改进的领域.
结论:
- 没有代码的GenAI为研究人员提供了一个可行的和可访问的选择.
- 对特定科学领域的LLM定制进行进一步的研究是有必要的.
- 人工智能集成,特别是LLMs,准备重塑实验室操作和研究效率.
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