RI: Small: Local and Forward-Oriented Deep Learning for Decentralized and Dynamic Environments

深度学习领域使计算机能够创建复杂的模型来执行以前仅限于人类的挑战性任务。它是现代人工智能（AI）取得成功的关键，包括图像中的自动物体识别、机器翻译和自动驾驶汽车。然而，目前的深度学习模型需要在一个集中的位置收集数据，并由强大的计算机进行全局优化。这极大地限制了深度学习在分散和动态环境中常见的未来人工智能应用，这些应用对国防和经济优势至关重要。这些未来的人工智能应用将部署在多个异构无线设备上，这些设备的计算和通信能力有限（例如，监控摄像头、天气传感器或空中无人机）。这项工作将开发新的深度学习方法，在这些分散的环境中执行，适应不断变化的条件，并从通信故障中恢复。为了扩大STEM的参与，该项目还将开发虚拟机器学习实验室，以吸引高中生。最终，这项工作将朝着下一代分散和动态人工智能系统迈出关键一步。该项目将开发优化一系列可逆函数的方法，这些函数迭代地解构数据模式，这种方法称为破坏性学习。具体地说，这项工作将促进本地和前瞻性破坏性学习的基础和实践方面的知识。第一个目标将从研究者之前的工作中概括迭代算法，并为仅向前算法提供基础。这些前向算法不需要集中计算环境，即使在设备之间通信不完善的情况下，也可以局部优化人工智能组件。第二个目标将开发更实用的破坏性学习算法，结合前向学习和全局集中学习的优势。最终目标将在模拟和真实设备网络上实现和评估前向算法，以证明这种新的深度学习方法的可行性。这些目标共同为跨设备端到端学习的新替代方案奠定了基础，而不需要全球同步。该奖项反映了美国国家科学基金会的法定使命，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。

The field of deep learning enables computers to create complex models to perform challenging tasks previously limited to humans. It has been the key to modern successes in artificial intelligence (AI) including automatic object identification in images, machine translation, and autonomous cars. However, current deep learning models require data to be collected in one centralized location and globally optimized by powerful computers. This significantly limits deep learning in the decentralized and dynamic environments common to future AI applications critical for national defense and economic pre-eminence. These future AI applications will be deployed over multiple heterogeneous wireless devices that have limited computational and communication capabilities (e.g., a surveillance camera, a weather sensor, or an aerial drone). This work will develop novel deep learning methods that execute in these decentralized environments, adapt to changing conditions, and recover from communication failures. To broaden STEM participation, this project will also develop virtual machine learning labs to engage high school students. Ultimately, this work will take a key step towards the next generation of decentralized and dynamic AI systems.This project will develop approaches to optimize a sequence of invertible functions that iteratively deconstruct data patterns, an approach called destructive learning. Specifically, this work will advance knowledge on both the foundational and practical aspects of local and forward-oriented destructive learning. The first objective will generalize the iterative algorithms from the investigator’s prior work and provide the foundation for forward-only algorithms. These forward-only algorithms do not require a centralized computational environment and can locally optimize AI components even under imperfect communication between devices. The second objective will develop more practical destructive learning algorithms that combine the strengths of forward-only and global centralized learning. The final objective will implement and evaluate forward-oriented algorithms on simulated and real device networks to demonstrate the feasibility of this new deep learning approach. These objectives work together in creating a foundation for a novel alternative to end-to-end learning across devices that does not require global synchronization.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.