SBIR Phase I: Ikigai: One-Click-AI For Intelligent Decision Making

SBIR 第一阶段：Ikigai：用于智能决策的一键式 AI

• 批准号: 1914143
• 负责人: Vinayak Ramesh
• 金额: $ 22.43万
• 依托单位: Ikigai Labs Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914143&HistoricalAwards=false
• 关键词: SBIR; Phase; Ikigai; Click; AI

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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于将人工智能驱动的决策带到任何可以使用Excel的人的指尖。特别是，通过Excel宏，该产品实现了以下功能：预测分析，假设情景分析和优化决策建议。这使得各种规模和能力的团队都可以利用数据驱动的决策，而无需昂贵的基础设施或高技能的数据科学家。这个小型企业创新研究第一阶段项目旨在将图形模型驱动的预测和优化结合起来，以解决通用强化学习。这将是一项重要的创新，因为使用图形模型作为基础来解决大规模通用强化学习/ AI问题的决策框架是不存在的，并且非常具有挑战性。为此，该创新是双重的：在高效地将Excel数据表示为图形模型，以及在数据基础上以可扩展和无缝的方式在图形模型上进行计算，在单机（例如笔记本电脑）以及多台机器上。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。