Collaborative Research: FW-HTF-RM: AI-Assisted Programming: Equipping Social and Natural Scientists for the Future of Research

合作研究：FW-HTF-RM：人工智能辅助编程：为社会和自然科学家的未来研究做好准备

• 批准号: 2326173
• 负责人: Arjun Guha
• 金额: $ 25.2万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326173&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; RM

Computer programming is essential for modern science. Scientists write programs to control instruments, run simulations, and analyze data. However, the programming tools and techniques that scientists use often lag behind those in the software engineering industry. This lag makes scientific discovery slower, more costly, and can lead to unreproducible results. In the past two years, artificial intelligence (AI) tools, such as ChatGPT, have revolutionized the software industry. They have been shown to make software engineers significantly more productive, but have not had the same impact on the sciences. The goal of this research project is to develop and test AI programming tools that work for scientists. The research team is developing AI models and tools that support the programming languages that scientists use. They are developing benchmarks to evaluate the effectiveness of AI tools for programming tasks that are unique to the sciences. They are investigating how AI programming tools can help college students study science more effectively. By harnessing AI to make programming easier for scientists, the project is helping to accelerate scientific discovery, lower its cost, and allow more people to participate in scientific work.The project is developing large language models of code and associated tools to support scientists. To understand scientists' needs, the team is running qualitative and quantitative studies of how scientists write programs. Based on these findings, they are developing deep neural network models for programming languages that are frequently used in the sciences, such as MATLAB and R, but are less commonly used in the software engineering industry. These models are particularly helpful for scientists who are not expert programmers; they can turn descriptions into computer programs, and also generate explanations of existing programs. The team is developing models that support the programming paradigms that scientists use, including computational notebooks and programs whose structure is determined by data formats. The team is developing code generation models that can be deployed on private, "air gapped" networks, making them suitable for scientists working in sensitive fields, including energy and defense.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机编程对于现代科学至关重要。科学家编写程序来控制仪器，运行模拟和分析数据。 但是，科学家使用的编程工具和技术经常落后于软件工程行业中的编程工具和技术。这一滞后使科学发现较慢，更昂贵，并可能导致不可再生的结果。在过去的两年中，人工智能（AI）工具（例如Chatgpt）彻底改变了软件行业。它们已被证明使软件工程师显着提高生产力，但对科学的影响没有相同的影响。该研究项目的目的是开发和测试适合科学家使用的AI编程工具。研究团队正在开发支持科学家使用的编程语言的AI模型和工具。他们正在开发基准，以评估AI工具在科学独有的编程任务中的有效性。他们正在研究AI编程工具如何帮助大学生更有效地学习科学。通过利用AI使科学家更容易编程，该项目正在帮助加速科学发现，降低其成本，并允许更多的人参与科学工作。该项目正在开发大型的代码和相关工具的语言模型来支持科学家。为了了解科学家的需求，该团队正在对科学家如何编写程序进行定性和定量研究。基于这些发现，他们正在开发针对科学中经常使用的编程语言的深层神经网络模型，例如Matlab和R，但在软件工程行业中不常用。 这些模型对不是专家程序员的科学家特别有用。他们可以将描述转变为计算机程序，还可以生成现有程序的解释。该团队正在开发支持科学家使用的编程范例的模型，包括计算笔记本和结构由数据格式确定的程序。该团队正在开发可以部署在私人“空气”网络上的代码生成模型，使其适合于在敏感领域工作的科学家，包括能源和防御。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。