SBIR Phase I: Categorical Representation Learning in Artificial Intelligence

SBIR 第一阶段：人工智能中的分类表示学习

• 批准号: 2109928
• 负责人: Artan Sheshmani
• 金额: $ 25.55万
• 依托单位: QGNAI, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2109928&HistoricalAwards=false
• 关键词: SBIR; Phase; Categorical; Representation; Learning

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is substantial improvement in artificial intelligence. Despite advances in AI, the current state-of-the-art platforms are still weak in learning and require large amounts of training data. The proposed system creates a better AI system by exploring more relationships within complex data sets. The initial application is cancer detection in genomic data. This Small Business Innovation Research (SBIR) Phase I project is to develop a novel relation-oriented machine learning platform. Current platforms are object-oriented, where objects (e.g., words, sequence data, etc.) are represented as feature vectors. The feature vector representation is powerful in performing tasks such as pattern recognition, classification, and regression but ineffective in learning the interrelationships of objects in great detail. An in-depth understanding of objects is paramount in learning. Also, the meaning of objects is defined and can only be defined through their interrelationship with other objects. Based on category theory in mathematics and quantum physics, the proposed platform is relation-oriented. It maps and preserves the interrelationship between objects in all dimensions. Based on the observed concurrences of objects and their relations, the platform fuses them to create more complex objects progressively and learns their interrelations iteratively to form a hierarchical structure associated with the dataset. This platform automatically learns the meaning of objects and the governing rules between them.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响在人工智能方面有很大改善。尽管AI取得了进步，但当前的最新平台仍然很弱，并且需要大量的培训数据。提出的系统通过探索复杂数据集中的更多关系来创建更好的AI系统。 最初的应用是基因组数据中的癌症检测。这项小型企业创新研究（SBIR）I阶段项目是开发一个以关系为导向的机器学习平台。当前平台是面向对象的，其中对象（例如单词，序列数据等）表示为特征向量。功能向量表示在执行诸如模式识别，分类和回归之类的任务方面非常有力，但在学习对象的相互关系方面无效。对对象的深入了解对于学习至关重要。同样，对象的含义是定义的，只能通过与其他对象的相互关系来定义。基于数学和量子物理学的类别理论，提出的平台面向关系。它映射并保留所有维度对象之间的相互关系。基于观察到的对象及其关系的并发，平台使他们融合了更复杂的对象，并在迭代地学习它们的相互关系，以形成与数据集关联的层次结构。该平台自动了解对象的含义和它们之间的管理规则。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估值得支持的。