Artificial Intelligence in the Air

智能正在走向边缘。自主系统和工业边缘是人工智能（AI）的下一个目标。然而，尽管硬件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。通信可以让边缘设备共享它们的数据和计算资源，并为它们的学习能力提供多方面的提高，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，我们当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，在机器学习应用中，我们感兴趣的是推断底层信号或消息的特征，而不是重建它们。增加的数据速率并不能转化为更快或更准确的学习算法，而且信息的内容、及时性和相关性往往比其数量更重要。AI-R挑战了目前将沟通和学习分开对待的框架，努力从基本理论原则出发，开发以ai为导向的沟通范式，弥合这一差距。这种新范式将超越传统的通信理论框架，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还将走出通信增量研究的周期，充分利用AI能力来“学习”最佳通信策略，以实现规定的目标。基于我们在信息论、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.