Collaborative Research: IIS: RI: Medium: Lifelong learning with hyper dimensional computing

协作研究：IIS：RI：中：超维计算的终身学习

• 批准号: 2211387
• 负责人: Friedrich Sommer
• 金额: $ 39.98万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211387&HistoricalAwards=false
• 关键词: Collaborative; Research; IIS; RI; Medium

The use of artificial intelligence (AI) has enabled computers to solve some problems that were out of reach just a decade ago, such as recognizing familiar objects in images, or translating between languages with reasonable accuracy. In each case, a specific task (such as "translate spoken Mandarin into spoken Spanish") is defined, data is collected (consisting, say, of utterances in the two languages), and an AI system is trained to achieve this functionality. To further expand the scope of AI, it is important to build systems that are not just geared towards highly-specific and static predefined tasks, but are able to take on new tasks as they arise (new words, new accents, and new dialects, for instance). This is often called "lifelong learning", and it means, basically, that the systems are adaptive to change. This project develops an approach to lifelong learning using a brain-inspired framework for distributed computing, yielding machines that potentially can solve tasks more flexibly and consume significantly less power than traditional AI systems. It will: (1) advance the ability of AI systems to handle changing environments, (2) enable a host of new low-power AI systems with applications such as environmental sensing, (3) strengthen mathematical connections between computer science and neuroscience, and (4) serve as the basis for educational and outreach activities.This project will develop lifelong learning within the framework of "hyperdimensional computing", a neurally-inspired model of computation in which information is encoded using randomized distributed high-dimensional representations, often with limited precision (e.g., with binary components), and processing consists of a few elementary operations such as vector summation. We will build HD algorithms for some fundamental statistical primitives -- similarity search, density estimation, and clustering -- and then use these as building blocks for various forms of lifelong learning. These will rest on mathematical advances in (1) the analysis of sparse codes produced by expansive random maps and (2) algorithmic exploitation of kernel properties of high-dimensional randomized representations. Our algorithms will be implemented in hardware, deployed on a network of low-power sensors, and evaluated experimentally in a lifelong learning task involving air quality sensing.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.

人工智能(AI)的使用使计算机能够解决一些十年前还遥不可及的问题，比如识别图像中熟悉的对象，或者以相当准确的速度在语言之间进行翻译。在每种情况下，都定义了一个特定的任务(例如，将普通话口语翻译成西班牙语口语)，收集数据(例如，由两种语言的话语组成)，并训练人工智能系统来实现这一功能。为了进一步扩大人工智能的范围，重要的是要建立不仅面向高度特定和静态的预定义任务的系统，而且能够在新任务出现时承担新任务(例如，新词、新口音和新方言)。这通常被称为“终身学习”，这意味着，从根本上说，系统能够适应变化。该项目开发了一种使用大脑启发的分布式计算框架进行终身学习的方法，产生的机器可能可以更灵活地解决任务，并且比传统的人工智能系统消耗的电力要少得多。它将：(1)提高人工智能系统处理不断变化的环境的能力，(2)使一系列新的低功耗人工智能系统能够应用于环境传感，(3)加强计算机科学和神经科学之间的数学联系，以及(4)作为教育和外展活动的基础。该项目将在“超维计算”的框架内发展终身学习。“超维计算”是一种受神经启发的计算模型，其中信息使用随机分布的高维表示进行编码，通常具有有限的精度(例如，使用二进制分量)，处理包括一些基本操作，如向量求和。我们将为一些基本的统计基元--相似性搜索、密度估计和聚类--构建HD算法，然后将它们用作各种形式的终身学习的构建块。这些将依赖于(1)由扩展随机映射产生的稀疏码的分析和(2)高维随机表示的核特性的算法开发方面的数学进展。我们的算法将在硬件中实现，部署在低功率传感器网络上，并在涉及空气质量感知的终身学习任务中进行实验评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。