Ikigai Labs Inc — National Science Foundation SBIR Phase I: IT

Description

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from bringing AI-driven decision making to the fingertips of anyone who can use Excel. In particular, through Excel Macros, this product enables the following capabilities: Predictive Analytics, What-If Scenario Analysis, and Optimized Decision Recommendations. This enables teams of all sizes and capabilities to utilize data-driven decision making without the need for expensive infrastructure or highly skilled data scientists. This Small Business Innovation Research Phase I project aims to bring together Graphical Model driven prediction and optimization to solve generic reinforcement learning. This will be an important innovation since a decision-making framework that uses Graphical Models as the substrate to solve generic reinforcement learning / AI problems at scale is non-existent and very challenging. To this end, the innovation is twofold: in efficiently representing Excel data as Graphical Models, and in the data substrate to compute over Graphical Models in a scalable and seamless manner, on a single machine (e.g. a laptop), as well as multiple machines. 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.