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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响是人工智能的实质性改进。尽管人工智能取得了进步，但目前最先进的平台在学习方面仍然很薄弱，需要大量的训练数据。该系统通过探索复杂数据集内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。这个小型企业创新研究（SBIR）第一阶段项目是开发一个新的面向关系的机器学习平台。当前的平台是面向对象的，其中对象（例如，字、序列数据等）表示为特征向量。特征向量表示在执行模式识别、分类和回归等任务时功能强大，但在详细学习对象的相互关系时效果不佳。对物体的深入理解在学习中至关重要。此外，对象的意义是定义的，只能通过它们与其他对象的相互关系来定义。该平台以数学和量子物理学中的范畴理论为基础，面向关系。它映射并保留所有维度中对象之间的相互关系。基于观察到的对象及其关系的并发性，该平台将它们融合以逐步创建更复杂的对象，并迭代地学习它们的相互关系，以形成与数据集相关联的分层结构。该平台自动学习对象的含义以及它们之间的管理规则。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。