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.

这个为期三年的研究经验，为本科生（REU）网站在北德克萨斯大学将支持10名学生10周，每年夏天，并训练他们建立现代人工智能（AI）系统，能够跨领域交流他们所获得的知识。最先进的人工智能系统用于具有挑战性的目标，如视觉对象识别，语音识别和理解自然语言，需要大量的训练，其中大部分学习到的知识被锁定到构建的人工智能系统的结构中。然而，该计划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，以便保留这些系统所学到的细微差别，并随时可用于其他应用程序。这个REU汇集了一个跨学科的团队，以支持展示人工智能系统的好处的项目，该系统旨在直接提供和接收推断的知识。在程序的早期，每个学生将确定一个应用程序域和一个外部顾问，在许多呈现的选项中进行工作。学生还将参加一个长期的，独特的人工智能暑期研究计划，该计划将现有的大学生和外部REU学生整合在一起，以促进跨部门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通过自我监督深度学习模型获得的知识。每年参与的学生将创建出现在各种应用程序中的常见术语或项目的矢量表示，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，学生将接触到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周过渡到他们个人研究工作的演示和故障排除。该REU将帮助培养一支不仅擅长使用深度学习模型，而且能够扩展其功能的学生队伍。此外，该项目将培训来自研究资源有限的大学合作伙伴的各种学生，与卡内基R1联邦指定的西班牙裔服务机构的成功团队中的不同学生进行交流。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。