I-Corps: System, Apparatus, and Method for Providing Behavior-Based Human-Centered Artificial Intelligence Augmentation to Enhance Performance

George Washington University, Washington DC

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is to reduce the cost of machine learning (ML) development by up to 50%, while simultaneously increasing intelligence production to address national priorities. The advancements of Big Data and storage technology have led to increasing levels of data production but without enough qualified analysts to transform data into actionable insights. The federal government invests nearly $5 billion in research related to artificial intelligence (AI) and ML in fiscal 2020, and public sector forecasts suggest that AI decision support/augmentation will deliver $2.9 trillion in value. This project will introduce revolutionary methods enabling analysts to auto-visualize patterns to support public sector and private market applications. This I-Corps project provides a framework for artificial intelligence (AI) augmentation that is tool-agnostic and can be used across any application, including national security. The innovation aims to integrate artificial intelligence (AI) augmentation and user behavior patterns through existing applications, collectively enhancing the performance of analysts and citizen data scientists. To address these challenges, the goal is to provide recommendations that are overlaid and integrated directly into the web application to help analysts increase intelligence production and dissemination across various public sector entities. 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.