EFRI BRAID: Efficient Learning of Spatiotemporal Regularities in Humans and Machines through Temporal Scaffolding

EFRI BRAID：通过时间支架有效学习人类和机器的时空规律

• 批准号: 2317706
• 负责人: Dhireesha Kudithipudi
• 金额: $ 200万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317706&HistoricalAwards=false
• 关键词: EFRI; BRAID; Efficient; Learning; Spatiotemporal

Operating on a minimal energy budget, the human brain is able to efficiently process vast amounts of temporal information at different timescales as it quickly learns to act in new environments. By contrast, current AI models do not learn temporal information efficiently, struggle with lifelong learning - the ability to keep learning new tasks continuously throughout life - and also do not perform well in resource-constrained environments. This project aims to create new AI models that overcome these limitations by leveraging mechanisms inspired by theories for how the brain is able to efficiently learn temporal information. Particularly, the project is based on a recent theory, 'temporal scaffolding', which postulates that during sleep, the brain reactivates wake experiences in an accelerated manner to allow detecting important temporal patterns embedded in those experiences. The goal of this project is to develop autonomous machines, informed by the temporal scaffolding hypothesis, which can rapidly adapt, operate under uncertainty, and evolve throughout their lifespan despite resource constraints. This transformative approach has the potential to address major AI challenges and find applications in healthcare, energy, and national security. The team aims to promote broad access to the computational strategies through initiatives at multiple educational institutions, emphasizing cross-disciplinary training and outreach to underrepresented populations. The team will conduct value-sensitive workshops and regular ethics consultations throughout the project. Alongside the technical goals, the team aims to offer opportunities for underrepresented students in AI fields, fostering a competitive AI workforce to maintain US technological leadership in STEM. By emulating how the human brain learns, the team seeks to create efficient, lifelong learning AI systems capable of revolutionizing various industries and benefiting society as a whole.The Temporal Scaffolding Hypothesis provides a novel explanation for the brain’s superior ability to efficiently learn temporal information. According to this hypothesis, time-compressed memory replay during offline periods serves to extract temporal regularities within encoded experiences. Building on the temporal scaffolding hypothesis, in the present project the PIs propose a set of mechanisms underlying resource-efficient lifelong learning of spatiotemporal regularities employing online (“wake”) and offline (“sleep”) periods, which they intend to both verify in new human experiments and incorporate in machine learning algorithms. Advances in theory, models, and systems stemming from this grant will have applications in multiple domains. The two specific aims for this project are to: i) develop new AI algorithms and architectures, inspired by the temporal scaffolding hypothesis, for efficient learning of spatiotemporal patterns and ii) extend the temporal scaffolding hypothesis to include hierarchical representations that support lifelong learning and verify the predictions of the model through human experiments and computational investigations. Through these aims the PIs will develop optimization frameworks that support deployment in resource constrained environments. Moreover, this project will yield scalable deep neural network and spiking neural network models that incorporate temporally compressed replay mechanisms. This approach is expected to limit the catastrophic interference effects that hinder most current network models of memory and improve the system’s capacity for lifelong learning. Training and access to these transformative computational strategies will be broadened via multiple initiatives at the University of Texas at San Antonio, the University of Rochester, and the University of Tennessee, Knoxville, including successful K-12 partnerships and targeted experiential outreach strategies. The team will also engage in ethical design through value-sensitive workshops and regular ethics consultations. The project design efforts will also provide significant opportunities to underrepresented students in cross-cutting AI fields and promote a robust and competitive AI workforce that maintains US technological leadership in STEM.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.

人脑以最小的能源预算运行，能够在不同的时间范围内有效地处理大量临时信息，因为它很快学会了在新环境中行动。相比之下，当前的AI模型不会有效地学习临时信息，与终身学习斗争 - 能够在整个生命中不断学习新任务的能力 - 并且在资源受限的环境中也不能很好地表现。该项目旨在创建新的AI模型，通过利用受理论启发的机制来克服这些限制，以了解大脑如何有效地学习临时信息。特别是，该项目基于最近的理论“时间脚手架”，该理论假定在睡眠期间，大脑以加速的方式重新激活了唤醒体验，以允许检测到这些经验中嵌入的重要临时模式。该项目的目的是开发由临时脚手架假设所告知的自主机器，该假设可以迅速适应，在不确定性下运行并在其整个其寿命终身目的地资源限制中发展。这种变革性的方法有可能应对AI的主要挑战，并在医疗保健，能源和国家安全方面找到应用。该团队旨在通过多个教育机构的举措来促进广泛的计算策略，强调跨学科培训，并向人群不足的人群进行宣传。该团队将在整个项目中进行价值敏感的研讨会和定期道德咨询。除技术目标外，该团队的目标是为AI领域中代表性不足的学生提供机会，从而促进了竞争性的AI劳动力，以维持美国的技术领导力。通过模仿人脑的学习方式，团队试图创建高效，终生学习的AI系统，能够彻底改变各种行业并使社会整体受益。时间脚手架假设为大脑有效学习临时信息的卓越能力提供了一种新颖的解释。根据这一假设，在离线期间的时间压缩记忆重播是在编码体验中提取临时规律性的。基于临时脚手架假设的基础，在本项目中，PIS提出了一组资源效率高的终身学习的机制，该机制是对在线（“唤醒”）和离线（“睡眠”）期间的时空规律性学习，他们打算在新人实验中验证并将其纳入新的人类实验中。理论，模型和系统的进步将在多个领域中具有应用。该项目的两个具体目的是：i）开发新的AI算法和体系结构，灵感来自临时脚手架假设的启发，用于有效学习时空模式和ii）将临时脚手架假设扩展到临时脚手架的假设，以包括支持寿命学习并通过人类模型进行人体验证的层次结构表述，并验证人工经验和计算的模型和计算。通过这些目标，PI将开发优化框架，以支持资源约束环境中的部署。此外，该项目将产生可扩展的深神经网络和尖峰神经网络模型，并结合临时压缩的重播机制。预计这种方法将限制灾难性的干扰效应，从而阻碍大多数当前的内存网络模型，并提高系统的终身学习能力。培训和获取这些变革性计算策略将通过德克萨斯大学圣安东尼奥大学，罗切斯特大学和田纳西大学的诺克斯维尔大学的多项计划扩大，包括成功的K-12合作伙伴关系和目标专家外展策略。该团队还将通过价值敏感的研讨会和常规的道德咨询从事道德设计。项目设计工作还将为代表性不足的学生提供跨越AI领域的学生提供重要的机会，并促进稳健而有竞争力的AI劳动力，该劳动力在STEM中保持我们的技术领导力。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。