Facilitating Interdisciplinary Teams to Build Better AI-Based Systems

促进跨学科团队构建更好的基于人工智能的系统

• 批准号: RGPIN-2021-03538
• 负责人: Zhou, Shurui
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751680
• 关键词: Facilitating; Interdisciplinary; Teams; Build; Better

Intelligent software systems assist in every area of our lives, such as e-commerce sites, social media, and web searching. When building intelligent software systems with AI components, interdisciplinary teams consisting of (but not limited to) data scientists and software engineers need to work together. These groups have different goals and experience, often leading to friction in the process: Data scientists mainly work in the data exploratory phase to train a high-performing machine learning model, a heavily exploratory and iterative process. Then they deliver the resulting learning code and models to software engineers in order to integrate the model into production code in the production phase. Evidence shows that it is common that the code from the exploratory phase often needs to be refactored in order to accommodate production concerns, such as latency, scalability, and robustness. Additionally, once development and production models drift apart, it is non-trivial to incorporate feedback from the production phase for additional experimentation, often requiring significant coordination. This introduces mistakes, slows down the development process, and increases the need for coordination overhead. Currently, both big tech corporations and small start-up teams struggle with transitioning machine learning ideas into AI components that can be integrated into the software system seamlessly. The main goal of this research program is to foster collaboration and reduce friction between data scientists and software engineers, and provide support to the AI-based software development lifecycle. In particular, we aim to achieve three objectives: (1) Identifying context-specific collaboration pain points and best practices; (2) improving code quality and coding environment in the data exploration phase; and (3) facilitating collaboration between data scientists and software engineers. The main outcome includes the design and development of analysis infrastructure and interventions that support collaboration and system building. This research program will train 10 HQP (2 Ph.D., 3 MASc, 5 USR) through hands-on research practices in large-scale software analyses. The research activities involve in-depth user studies of software practitioners and AI experts, empirical investigation of the problem space, and rigorous design and evaluation of the methods to solve the problem. In addition, all HQP will gain skills and knowledge in software engineering, machine learning, and software development, and will have the chance to work on real, highly impactful software systems and build strong hands-on skills. Given the wide range of application scenarios, our research results can be applied to support collaboration and system building for teams focused on machine learning from different backgrounds, including established companies, start-ups, non-tech corporations, nonprofit, and research institutions in Ontario, in Canada, and internationally.

智能软件系统协助我们生活的每个领域，例如电子商务网站，社交媒体和网络搜索。当构建具有AI组件的智能软件系统时，由（但不限于）数据科学家和软件工程师组成的跨学科团队需要共同努力。这些群体具有不同的目标和经验，通常会导致该过程中的摩擦：数据科学家主要在数据探索阶段工作，以训练高性能的机器学习模型，这是一个重大探索性且迭代的过程。然后，他们将所得的学习代码和模型交付给软件工程师，以便在生产阶段将模型集成到生产代码中。有证据表明，通常需要对探索阶段的代码进行重构以适应生产问题，例如延迟，可扩展性和鲁棒性。此外，一旦开发和生产模型散开，将生产阶段的反馈纳入以进行其他实验是不平凡的，通常需要显着协调。这引入了错误，减慢了发展过程，并增加了对开销的协调需求。目前，大型科技公司和小型初创团队都在将机器学习想法转变为可以将机器学习想法转变为可以无缝集成到软件系统中的AI组件。该研究计划的主要目标是促进数据科学家和软件工程师之间的摩擦，并为基于AI的软件开发生命周期提供支持。特别是，我们旨在实现三个目标：（1）确定特定于上下文的协作痛点和最佳实践； （2）在数据勘探阶段改善代码质量和编码环境； （3）支持数据科学家与软件工程师之间的合作。主要结果包括设计和开发分析基础架构以及支持协作和系统建设的干预措施。该研究计划将通过大规模软件分析中的动手研究实践培训10个HQP（2博士学位，3 MASC，5 USR）。研究活动涉及对软件实践者和AI专家的深入用户研究，问题空间的经验投资以及解决问题的方法的严格设计和评估。此外，所有HQP都将获得软件工程，机器学习和软件开发方面的技能和知识，并将有机会致力于真正的，高度影响力的软件系统并建立强大的动手技能。鉴于广泛的应用程序方案，我们的研究结果可用于支持来自不同背景的机器学习的团队的协作和系统建设，包括成熟的公司，初创企业，非技术公司，非营利组织，非营利组织和安大略省，加拿大，加拿大和国际化的研究机构。