I-Corps: Creating data sets that are distilled into collections of information that may be used to answer questions in real-time

I-Corps：创建数据集，将其提炼成可用于实时回答问题的信息集合

• 批准号: 2038479
• 负责人: Anita Bell
• 金额: $ 5万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038479&HistoricalAwards=false
• 关键词: Corps; Creating; data; sets; distilled

The broader impact/commercial potential of this I-Corps project is the development of low-cost, industry agnostic, artificial intelligence resources. Presently, data analysts and data science teams face one primary problem: data cleanliness. Data cleanliness is a measure of corrupt or inaccurate records from a record set or table. To clean a database one must identify incomplete or incorrect parts of the data and then replace, modify, or delete the bad data. Machine learning models often perform well in lab conditions but fail in the real world due to dirty data. Artificial intelligence solutions are inhibited in real world applications due to too little signaling data amidst the noise. The proposed technology solves the problems that face many data scientists who dependent on the dirty data at hand. There is a demand for faster, better decision making across virtually every industry. The goal is to strategically solve the data cleanliness problem, with protectable intellectual property and processes, for various industries after refining internal processes, user interface, and value propositions for the initial technology target: financial institutions.This I-Corps project is based on the development of novel artificial intelligence resources to automate data preparation, augmentation, and governance processes. These resources include proprietary machine learning algorithms, unique data librarying processes, dynamic training sets, next generation database technology, and intelligent rules engines to reimagine the expensive, high-effort, and highly technical space of data science. The novel approach of this project will automate over 60% of a data scientists’ workload, reduce cost, minimize internal IT resource demands, and increase information access.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I-Corps项目的更广泛的影响/商业潜力是开发低成本，行业不可知的人工智能资源。目前，数据分析师和数据科学团队面临着一个主要问题：数据清洁。数据清洁度是对记录集或表中损坏或不准确记录的度量。 要清理数据库，必须识别数据中不完整或不正确的部分，然后替换、修改或删除坏数据。机器学习模型通常在实验室条件下表现良好，但由于脏数据而在真实的世界中失败。人工智能解决方案在真实的世界应用中受到抑制，这是因为在噪声中信令数据太少。这项技术解决了许多依赖手头脏数据的数据科学家面临的问题。几乎每个行业都需要更快、更好的决策。目标是在优化内部流程、用户界面和初始技术目标（金融机构）的价值主张后，通过可保护的知识产权和流程，战略性地解决各行业的数据清洁问题。该I-Corps项目基于开发新型人工智能资源，以自动化数据准备、增强和治理流程。这些资源包括专有的机器学习算法，独特的数据库流程，动态训练集，下一代数据库技术和智能规则引擎，以重新构想数据科学的昂贵，高工作量和高技术空间。该项目的创新方法将自动化数据科学家60%以上的工作量，降低成本，最大限度地减少内部IT资源需求，并增加信息访问。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。