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

University Of North Texas, Denton TX

Investigators

Abstract

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.