SBIR Phase I: Technology Translation of a Universal Summarization System

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响是通过商业化“通用数据汇总”的过程，使机器学习（ML）和人工智能（AI）成本更低、偏见更少、更准确、更可扩展、更容易使用。这种汇总过程适用于任何类型的数据，并在不丢失信息的情况下显著减少数据集大小。降低的成本包括计算、核心内存、存储、劳动力和能源，同时减少人工智能对环境的影响，提供“绿色人工智能”。通用总结也可用于测量和消除用于训练AI/ML系统的数据中的偏差。由于数据中概念的代表性过高而其他概念的代表性不足而引起的偏见将会减少，因为要使一个小摘要具有代表性，它必须是多样化和包容性的。此外，通过改进人类分析，准确性将得到提高。许多数据科学任务都涉及人类的分析，他们必须检查数据以发现洞察力。这些都是艰巨、昂贵、耗时且容易出错的任务，由于冗余和重复导致的人类警觉和决策疲劳而变得更糟。通过减小尺寸和消除冗余，减少了人的疲劳，提高了人的准确性和效率，并降低了注释成本。这个小企业创新研究（SBIR）第一阶段项目将开发和商业化简单和经济地执行大规模数据集的通用摘要的能力。摘要是一个从数据集中选择一小部分数据项的过程——少数选择的摘要项准确地表示了许多未选择项中包含的信息。这项创新被称为“校准的子模块总结”，这项技术涉及到快速、经济有效、准确地测量数据子集中的信息，然后通过算法选择具有数学信息内容保证的小子集。该技术消除了冗余，在数据集中留下了核心信息的有效表示。拟议的活动将创建一个商业服务，可以快速，轻松地总结大量数据（任何类型），并且不需要用户在机器学习，数据科学或子模块化方面的专业知识。这将大大降低任何数据丰富的行业的成本、上市时间、环境影响和数据偏差。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to make machine-learning (ML) and artificial intelligence (AI) less costly, less biased, more accurate, more scalable, and easier to use through the process of commercializing “universal data summarization.” This summarization process works on any kind of data and significantly reduces dataset size without loss of information. Cost reductions include computational, core memory, storage, labor, and energy, all while reducing AI's environmental impact to provide "green AI." Universal summarization can also be used to measure and remove bias in data used to train AI/ML systems. Biases, caused by concepts in the data that are vastly over-represented while others are under-represented, will be reduced since for a small summary to be representative, it must be diverse and inclusive. In addition, accuracy will be increased by improving human analytics. Many data science tasks involve analysis by humans who must examine data to discover insight. These are arduous, expensive, time-consuming, and error-prone tasks made worse by human alert and decision fatigue caused by redundancy and repetitiveness. By reducing size and eliminating redundancy, human fatigue is reduced, human accuracy and efficiency are increased, and annotation costs are mitigated. This Small Business Innovation Research (SBIR) Phase I project will develop and commercialize the ability to simply and affordably perform universal summarization of massive datasets. A summarization is a process that selects from a dataset a small subset of data items – the few selected summary items accurately represent the information contained in the many remaining unselected items. The innovation is called “calibrated submodular summarization,” a technology that involves quickly, cost-effectively, and accurately measuring information in subsets of data, and then algorithmically selecting small subsets that have mathematical information content guarantees. This technology strips away redundancy, leaving behind an efficient representation of the core information in the dataset. The proposed activities will create a commercial service that can summarize massive amounts of data (of any kind) quickly, easily, and without requiring user expertise in machine learning, data science, or submodularity. This will greatly reduce costs, time-to-market, environmental impact and data bias for any data-rich industry.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.