REU Site: Beyond Language: Training to Create and Share Vector Embeddings across Applications

REU 网站：超越语言：跨应用程序创建和共享向量嵌入的培训

• 批准号: 2244259
• 负责人: Ting Xiao
• 金额: $ 40.35万
• 依托单位: University of North Texas
• 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: 2023-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244259&HistoricalAwards=false
• 关键词: REU; Site; Beyond; Language; Training

This three-year Research Experiences for Undergraduates (REU) site at the University of North Texas will support 10 students for 10 weeks each summer and train them to build modern artificial intelligence (AI) systems capable of communicating their acquired knowledge across domains. State of the art AI systems for challenging goals such as visual object recognition, speech recognition, and understanding natural language require extensive training with much of the learned knowledge locked into the structure of the built AI system. However, students in this program will focus on creating and leveraging highly-trained AI systems to re-represent information so the nuanced understanding learned by these systems is preserved and readily accessible for other applications. This REU brings together an interdisciplinary team to support projects that showcase the benefits of AI systems built to provide and receive inferred knowledge directly. Early in the program each student will identify an application domain and an external advisor to work with among a number of presented options. Students will also participate in a long-standing, unique AI summer research program integrating current university students and external REU students to facilitate collaboration across departments and student expertise.Specifically, the training in this REU will allow students to more efficiently communicate the knowledge acquired by self-supervised deep learning models. The participating students each year will create vector representations of common terms or items that appear across various applications, utilize those embeddings to improve prediction models, and properly validate and document the benefit of this approach. For the first 5 weeks, the students will be exposed to different embedding strategies and machine learning applications utilizing them, then transition to demonstrations and trouble-shooting of their individual research efforts in the last 5 weeks. This REU will help prepare a workforce of students not only adept at using deep learning models but extending their functionality. Additionally, this project will train a diverse range of students from college partners with limited research resources to engage with diverse students in successful teams with a Carnegie R1 federally designated Hispanic-serving institution.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.

北德克萨斯大学的这个为期三年的本科生研究体验 (REU) 网站将在每年夏天为 10 名学生提供为期 10 周的支持，并培训他们构建能够跨领域交流所获得的知识的现代人工智能 (AI) 系统。为了实现诸如视觉对象识别、语音识别和理解自然语言等具有挑战性的目标，最先进的人工智能系统需要进行大量的训练，并将大部分学到的知识锁定在构建的人工智能系统的结构中。然而，该项目的学生将专注于创建和利用训练有素的人工智能系统来重新表示信息，以便保留这些系统学到的细致入微的理解，并易于其他应用程序访问。该 REU 汇集了一个跨学科团队来支持项目，这些项目展示了为直接提供和接收推断知识而构建的人工智能系统的优势。在课程的早期，每个学生将在提供的许多选项中确定一个应用领域和一个可以合作的外部顾问。学生还将参加一个长期的、独特的人工智能夏季研究项目，该项目将在校大学生和外部 REU 学生结合起来，以促进跨院系和学生专业知识的合作。具体来说，该 REU 的培训将使学生能够更有效地交流所获得的知识。自监督深度学习模型。每年参与的学生将创建出现在各种应用程序中的常用术语或项目的向量表示，利用这些嵌入来改进预测模型，并正确验证和记录这种方法的好处。在前 5 周，学生将接触不同的嵌入策略和利用它们的机器学习应用程序，然后在最后 5 周过渡到演示和解决他们个人研究工作的问题。该 REU 将帮助培养一批学生，不仅擅长使用深度学习模型，而且能够扩展其功能。此外，该项目还将培训来自研究资源有限的大学合作伙伴的各类学生，以便与卡内基 R1 联邦指定的拉美裔服务机构的成功团队中的不同学生进行互动。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。