Artificial Intelligence in the Air

智能正在走向边缘。自治系统和工业边缘是人工智能（AI）的下一个目标。然而，尽管在硬件方面取得了令人印象深刻的进步，但边缘设备没有足够的计算能力和数据来训练和部署最先进的机器学习（ML）算法。通信可以允许边缘设备共享它们的数据和计算资源，并提供多方面的学习能力，类似于语言对人类智力的影响。然而，受香农开创性工作的影响，我们目前的通信架构被设计为在节点之间建立可靠的比特管道，忽略了所传递比特的相关性或实用性。然而，在ML应用中，我们感兴趣的是推断潜在信号或消息的特征，而不是重建它们。数据速率的提高并不能转化为更快或更准确的学习算法，信息的内容、及时性和相关性往往比其数量更重要。AI-R挑战了当前将沟通和学习分开对待的框架，努力通过从基本理论原则开发面向AI的沟通范式来弥合这一差距。这种新的范式将超越经典的通信理论框架，考虑到信息传输的最终目标，例如检测无人机镜头中的异常或远程控制工业机器人。AI-R还将通过充分利用人工智能的能力来“学习”最佳的通信策略，以实现规定的目标，从而走出通信增量研究的周期。基于我们在信息理论、编码、通信和机器学习方面的专业知识和最新贡献，该项目将平衡基础研究与面向应用的算法设计和实现，以开发面向环境边缘智能的新工程见解和产品。

Intelligence is coming to the edge. Autonomous systems and industrial edge are the next targets of artificial intelligence (AI). However, despite impressive progress in hardware, edge devices do not have sufficient computing power and data to train and deploy state-of-the-art machine learning (ML) algorithms. Communication can allow edge devices to share their data and computational resources, and provide manifold increase in their learning capabilities, similarly to the impact language had on human intelligence. However, influenced by Shannon's seminal work, our current communication architectures are designed to establish reliable bit pipes between nodes, dismissing the relevance or utility of delivered bits. Yet, in ML applications, we are interested in inferring features of the underlying signals or messages, rather than reconstructing them. Increased data rates do not translate into faster or more accurate learning algorithms, and the content, timeliness, and the relevance of information are often more important than its quantity. AI-R challenges the current framework that treats communication and learning separately, striving to bridge this gap by developing an AI-oriented communication paradigm from fundamental theoretical principles. This new paradigm will go beyond the classical communication-theoretic framework by taking into account the ultimate goals of information transmission, for example, detecting anomalies in drone footage or remote controlling an industrial robot. AI-R will also step out of the cycles of incremental research in communications by fully exploiting AI capabilities to `learn' the best communication strategies to achieve the prescribed objectives. Building upon our expertise and recent contributions in information theory, coding, communications and ML, the project will balance fundamental research with application-oriented algorithm design and implementation to develop new engineering insights and products towards ambient edge intelligence.