论文标题
科学研究的人工智能：真实的研究教育框架
Artificial Intelligence for Scientific Research: Authentic Research Education Framework
论文作者
论文摘要
我们报告了一个框架，该框架能够通过解决共同的障碍来广泛采用真实的研究教育方法。我们提出的指导原则用于实施一项计划，在该计划中，具有互补技能的学生团队为自然科学研究人员开发有用的人工智能（AI）解决方案。为此，我们与揭示/指定其需求的研究实验室合作，然后我们的学生团队通过使用类似咨询的安排来发现，设计和开发AI解决方案，以解决独特的问题。迄今为止，我们的小组连续七个学期在纽约大学（纽约大学）工作，已经聘请了一百多名学生，从一年级的大学生到硕士候选人，并且与20多个项目和合作者合作。在为学生创造教育福利的同时，我们的方法还直接使科学家受益，他们有机会评估机器学习的特定需求的实用性。
We report a framework that enables the wide adoption of authentic research educational methodology at various schools by addressing common barriers. The guiding principles we present were applied to implement a program in which teams of students with complementary skills develop useful artificial intelligence (AI) solutions for researchers in natural sciences. To accomplish this, we work with research laboratories that reveal/specify their needs, and then our student teams work on the discovery, design, and development of an AI solution for unique problems using a consulting-like arrangement. To date, our group has been operating at New York University (NYU) for seven consecutive semesters, has engaged more than a hundred students, ranging from first-year college students to master's candidates, and has worked with more than twenty projects and collaborators. While creating education benefits for students, our approach also directly benefits scientists, who get an opportunity to evaluate the usefulness of machine learning for their specific needs.
