Abstract
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. Such tools struggle to cope with the non-trivial challenges of the network environment and the growing demands of emerging use cases. In this paper, we revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. These systems acquire common sense by exploiting different cognitive abilities such as perception, analogy, and reasoning, that enable them to generalize and deal with unforeseen scenarios. Towards developing the components of such a system, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyper-dimensional (HD) computing, that aligns with intuitive physics and enables analogical reasoning, that define common sense. Then, we explain how methods such as integrated information theory play a role in the proposed intent-driven and objective-driven planning methods that maneuver the AGI-native network to take actions. Next, we discuss how an AGI-native network can enable use cases related to human and autonomous agents: a) analogical reasoning for next-generation DTs, b) synchronized and resilient experiences for cognitive avatars, and c) brain-level metaverse experiences like holographic teleportation. Finally, we conclude with a set of recommendations to build AGI-native systems. Ultimately, we envision this paper as a roadmap for the beyond 6G era.
Abstract (translated)
实现诸如数字孪生（DTs）等服务的未来无线系统具有挑战性,即使采用传统的技术如元表面也会如此。虽然人工智能(AI)原生网络承诺要克服无线技术的某些限制,但发展仍然依赖于像神经网络这样的AI工具。这些工具很难应对网络环境中的非简单挑战和新兴用例的增长需求。在本文中,我们重新回顾了AI原生无线系统的概念,并使它们具有将它们转化为具有人工通用智能(AGI)所需常识的设备。这些系统通过利用感知、类比和推理等不同认知能力来获取常识,并能够泛化处理未预料到的情况。为了开发这种系统,我们首先展示如何通过将现实世界的元素抽象成可通用表示来构建感知模块。然后,我们使用基于因果关系和超维度(HD)计算的原则创建了一个世界模型,符合直觉物理学,并能够进行类比推理,定义共同常识。接下来,我们解释了像集成信息理论这样的方法在拟定有意和目标驱动的计划方法中如何发挥作用,这些方法可以操纵AGI原生网络采取行动。然后,我们讨论了AGI原生网络如何实现与人类和自主代理相关的用例:a)下一代DT的类比推理;b)为认知角色实现同步和有弹性的体验;c)类似于全息传输的脑层虚拟现实体验。最后,我们得出了一系列建议,以构建AGI原生系统。我们最终将本文视为6G时代 beyond 的路线图。
URL
https://arxiv.org/abs/2405.02336
