INSPIRE Track 1: Gradient Symbolic Computation

INSPIRE Track 1：梯度符号计算

• 批准号: 1344269
• 负责人: Paul Smolensky
• 金额: $ 100万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344269&HistoricalAwards=false
• 关键词: INSPIRE; Track; Gradient; Symbolic; Computation

This INSPIRE award is partially funded by the Linguistics Program and the Perception, Action & Cognition Program in the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral & Economic Sciences; by the Robust Intelligence Program in the Division of Information & Intelligent Systems in the Directorate for Computer & Information Science & Engineering; and by the Algorithmic Foundations Program in the Division of Computer and Network Systems in the Directorate for Computer & Information Science & Engineering.Discrete, combinatorial systems of structured symbols permeate human cognition in domains such as language, motor control, complex action planning, learning, and higher-level vision. Nonetheless, the computational apparatus that the brain exploits is based on continuous, activation-based propagation of information through complex networks of neurons. A fundamental problem of the cognitive sciences is how to integrate gradient, continuous neural computation with the discrete combinatorial dimension of cognition. The solution to this puzzle will provide a deeper understanding of the mind and may also serve as the basis of a new generation of computing systems capable of authentically brain-like behavior.Under the direction of Dr. Smolensky, the research team will develop an approach to this puzzle by exploring and testing the predictions of their theory of Gradient Symbolic Computation (GSC) in the domain of language. Their efforts will include the development of the formal, mathematical foundations of GSC. In parallel, the PIs will develop a framework for modeling Gradient Symbolic Processing. To that end, the PIs will use computational modeling and experimental psycholinguistic studies of phenomena that typify the morpho-phonological, syntactic, and semantic characteristics of language and language processing. The broader impacts of the work include the potential to transform general computing for future approaches to computer design, to provide innovations in computer language processing, and to empower major advances in our understanding of human language, its impairment in disease, and its learning and remediation. The project also strongly engages STEM education. Undergraduate, graduate, and post-doctoral researchers will all play key roles in highly interdisciplinary STEM research integrating experimental, theoretical, and computational methods. The new type of computation created will provide an integrative framework for developing courses bridging computation theory, psychology, and linguistics. Pedagogical materials developed in these courses will be made publicly available to facilitate undergraduate and graduate program development at other institutions.

INSPIRE奖的部分资金来自社会和行为经济科学理事会行为和认知科学部门的语言学项目和感知、行动认知项目;计算机信息科学工程理事会信息智能系统部门的稳健智能项目;以及计算机信息科学工程理事会计算机和网络系统部门的数学基础项目。结构化符号的离散组合系统渗透到语言、运动控制、复杂行动规划、学习和高级视觉等领域的人类认知中。 尽管如此，大脑利用的计算装置是基于通过复杂的神经元网络进行的连续的、基于激活的信息传播。认知科学的一个基本问题是如何将梯度、连续的神经计算与认知的离散组合维度相结合。这个难题的解决方案将提供对思维的更深入的理解，也可能成为新一代能够真正实现类脑行为的计算系统的基础。在Smolensky博士的指导下，研究团队将通过探索和测试他们的梯度符号计算（GSC）理论在语言领域的预测来开发解决这个难题的方法。他们的努力将包括GSC的正式数学基础的发展。与此同时，PI将开发一个用于建模梯度符号处理的框架。为此，PI将使用计算建模和实验心理语言学研究的现象，代表了语言和语言处理的形态语音，句法和语义特征。这项工作的更广泛影响包括有可能将通用计算转化为未来的计算机设计方法，提供计算机语言处理方面的创新，并使我们对人类语言的理解取得重大进展，疾病的损害，以及学习和补救。该项目还大力开展STEM教育。本科生，研究生和博士后研究人员都将在高度跨学科的STEM研究中发挥关键作用，整合实验，理论和计算方法。创建的新型计算将为开发连接计算理论、心理学和语言学的课程提供一个综合框架。在这些课程中开发的教学材料将公开提供，以促进其他机构的本科生和研究生课程的发展。