This paper explores the changes that pervasive AI is having on the nature of
combat. We look beyond the substitution of AI for experts to an approach where
complementary human and machine abilities are blended. Using historical and
modern examples, we show how autonomous weapons systems can be effectively
managed by teams of human "AI Operators" combined with AI/ML "Proxy Operators."
By basing our approach on the principles of complementation, we provide for a
flexible and dynamic approach to managing lethal autonomous systems. We
conclude by presenting a path to achieving an integrated vision of
machine-speed combat where the battlefield AI is operated by AI Operators that
watch for patterns of behavior within battlefield to assess the performance of
lethal autonomous systems. This approach enables the development of combat
systems that are likely to be more ethical, operate at machine speed, and are
capable of responding to a broader range of dynamic battlefield conditions than
any purely autonomous AI system could support.

该论文探讨了普遍人工智能对战斗性质的变化。在超越将人工智能替代专家的方式之外，我们提出了一种将人类和机器的能力相互结合的方法。通过历史和现代的例子，我们展示了如何通过人类 “AI 操作员” 与 AI/ML “代理操作员” 的团队来有效管理自主武器系统。通过将我们的解决方案基于补充原则，我们为管理致命自主系统提供了一种灵活和动态的方法。最后，我们提出了实现机器化作战整体愿景的路径，即由 AI 操作员操作战场 AI 以观察战场的行为模式以评估致命自主系统的性能。这种方法使得开发出的战斗系统可能更加道德化、运行速度更快，并能够应对更广泛的动态战场条件，超越任何纯粹自主人工智能系统能够支持的范围。

战争大象：重新思考战斗人工智能与人类监管

War Elephants: Rethinking Combat AI and Human Oversight

The vision of the upcoming 6G technologies, characterized by ultra-dense
network, low latency, and fast data rate is to support Pervasive AI (PAI) using
zero-touch solutions enabling self-X (e.g., self-configuration,
self-monitoring, and self-healing) services. However, the research on 6G is
still in its infancy, and only the first steps have been taken to conceptualize
its design, investigate its implementation, and plan for use cases. Toward this
end, academia and industry communities have gradually shifted from theoretical
studies of AI distribution to real-world deployment and standardization. Still,
designing an end-to-end framework that systematizes the AI distribution by
allowing easier access to the service using a third-party application assisted
by a zero-touch service provisioning has not been well explored. In this
context, we introduce a novel platform architecture to deploy a zero-touch
PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart
system. This platform aims to standardize the pervasive AI at all levels of the
architecture and unify the interfaces in order to facilitate the service
deployment across application and infrastructure domains, relieve the users
worries about cost, security, and resource allocation, and at the same time,
respect the 6G stringent performance requirements. As a proof of concept, we
present a Federated Learning-as-a-service use case where we evaluate the
ability of our proposed system to self-optimize and self-adapt to the dynamics
of 6G networks in addition to minimizing the users' perceived costs.

在 6G 网络中，我们引入了一种新颖的平台架构，通过基于区块链的智能系统部署零触碰的 PAI 作为服务（PAIaaS），旨在在架构的各个层面上标准化普适的人工智能，并统一接口，以便跨应用程序和基础设施领域轻松部署服务，解决用户在成本、安全和资源分配方面的担忧，并同时遵守 6G 严格的性能要求。作为概念验证，我们展示了联邦学习作为服务的用例，通过评估我们所提出的系统自我优化和自适应于 6G 网络的动态，并最大程度地减少用户的感知成本。

6G 网络中全面实现零触点的普适人工智能即服务

Zero-touch realization of Pervasive Artificial Intelligence-as-a-service  in 6G networks

Artificial intelligence (AI) has witnessed a substantial breakthrough in a
variety of Internet of Things (IoT) applications and services, spanning from
recommendation systems to robotics control and military surveillance. This is
driven by the easier access to sensory data and the enormous scale of
pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data
streams. Designing accurate models using such data streams, to predict future
insights and revolutionize the decision-taking process, inaugurates pervasive
systems as a worthy paradigm for a better quality-of-life. The confluence of
pervasive computing and artificial intelligence, Pervasive AI, expanded the
role of ubiquitous IoT systems from mainly data collection to executing
distributed computations with a promising alternative to centralized learning,
presenting various challenges. In this context, a wise cooperation and resource
scheduling should be envisaged among IoT devices (e.g., smartphones, smart
vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid
communication and computation overheads and ensure maximum performance. In this
paper, we conduct a comprehensive survey of the recent techniques developed to
overcome these resource challenges in pervasive AI systems. Specifically, we
first present an overview of the pervasive computing, its architecture, and its
intersection with artificial intelligence. We then review the background,
applications and performance metrics of AI, particularly Deep Learning (DL) and
online learning, running in a ubiquitous system. Next, we provide a deep
literature review of communication-efficient techniques, from both algorithmic
and system perspectives, of distributed inference, training and online learning
tasks across the combination of IoT devices, edge devices and cloud servers.
Finally, we discuss our future vision and research challenges.

本文综述了面向普适 AI 系统中资源调度的挑战，重点概述了分布式推断、训练和在线学习任务中的通信高效技术。