Large Language Models (LLMs) have showcased remarkable capabilities
surpassing conventional NLP challenges, creating opportunities for use in
production use cases. Towards this goal, there is a notable shift to building
compound AI systems, wherein LLMs are integrated into an expansive software
infrastructure with many components like models, retrievers, databases and
tools. In this paper, we introduce a blueprint architecture for compound AI
systems to operate in enterprise settings cost-effectively and feasibly. Our
proposed architecture aims for seamless integration with existing compute and
data infrastructure, with ``stream'' serving as the key orchestration concept
to coordinate data and instructions among agents and other components. Task and
data planners, respectively, break down, map, and optimize tasks and data to
available agents and data sources defined in respective registries, given
production constraints such as accuracy and latency.

大型语言模型已经展示出了超越传统自然语言处理挑战的卓越能力，为生产使用案例创造了机会。为了实现这一目标，出现了构建复合人工智能系统的显著转变，在其中将大型语言模型与包括模型、检索器、数据库和工具在内的广泛软件基础设施集成。本文介绍了一种针对复合人工智能系统在企业环境中进行成本效益和可行性操作的蓝图架构。我们提出的架构旨在与现有的计算和数据基础设施进行无缝集成，通过 “流” 作为关键的编排概念，在代理和其他组件之间协调数据和指令。任务和数据计划分别将任务和数据分解、映射并优化分配给在各自注册表中定义的可用代理和数据源，考虑生产约束条件，如准确度和延迟。

面向企业的复合人工智能系统的蓝图架构

A Blueprint Architecture of Compound AI Systems for Enterprise

Seamless integration of physical objects as interactive digital entities
remains a challenge for spatial computing. This paper introduces Augmented
Object Intelligence (AOI), a novel XR interaction paradigm designed to blur the
lines between digital and physical by endowing real-world objects with the
ability to interact as if they were digital, where every object has the
potential to serve as a portal to vast digital functionalities. Our approach
utilizes object segmentation and classification, combined with the power of
Multimodal Large Language Models (MLLMs), to facilitate these interactions. We
implement the AOI concept in the form of XR-Objects, an open-source prototype
system that provides a platform for users to engage with their physical
environment in rich and contextually relevant ways. This system enables analog
objects to not only convey information but also to initiate digital actions,
such as querying for details or executing tasks. Our contributions are
threefold: (1) we define the AOI concept and detail its advantages over
traditional AI assistants, (2) detail the XR-Objects system's open-source
design and implementation, and (3) show its versatility through a variety of
use cases and a user study.

通过引入增强物体智能（AOI）这一新颖的 XR 交互范式，结合物体分割和分类以及多模态大型语言模型的能力，提出了一种无缝融合物理对象与交互数字实体的方法，并通过 XR-Objects 系统的设计和实现以及一系列使用案例和用户研究的展示，展现了 AOI 的优势和多样化的应用。

增强对象智能：通过 XR-Objects 使模拟世界可交互

Augmented Object Intelligence: Making the Analog World Interactable with  XR-Objects

Artificial General Intelligence (AGI), possessing the capacity to comprehend,
learn, and execute tasks with human cognitive abilities, engenders significant
anticipation and intrigue across scientific, commercial, and societal arenas.
This fascination extends particularly to the Internet of Things (IoT), a
landscape characterized by the interconnection of countless devices, sensors,
and systems, collectively gathering and sharing data to enable intelligent
decision-making and automation. This research embarks on an exploration of the
opportunities and challenges towards achieving AGI in the context of the IoT.
Specifically, it starts by outlining the fundamental principles of IoT and the
critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it
delves into AGI fundamentals, culminating in the formulation of a conceptual
framework for AGI's seamless integration within IoT. The application spectrum
for AGI-infused IoT is broad, encompassing domains ranging from smart grids,
residential environments, manufacturing, and transportation to environmental
monitoring, agriculture, healthcare, and education. However, adapting AGI to
resource-constrained IoT settings necessitates dedicated research efforts.
Furthermore, the paper addresses constraints imposed by limited computing
resources, intricacies associated with large-scale IoT communication, as well
as the critical concerns pertaining to security and privacy.

人工智能在物联网系统中的关键作用以及在资源受限的物联网环境中实现人工智能的无缝集成所面临的机遇和挑战。

走向物联网中的人工通用智能 (AGI): 机遇与挑战

Towards Artificial General Intelligence (AGI) in the Internet of Things  (IoT): Opportunities and Challenges

Non-classical logics are used in a wide spectrum of disciplines, including
artificial intelligence, computer science, mathematics, and philosophy. The
de-facto standard infrastructure for automated theorem proving, the TPTP World,
currently supports only classical logics. Similar standards for non-classical
logic reasoning do not exist (yet). This hampers practical development of
reasoning systems, and limits their interoperability and application. This
paper describes the latest extension of the TPTP World, which provides
languages and infrastructure for reasoning in non-classical logics. The
extensions integrate seamlessly with the existing TPTP World.

本文介绍了 TPTP World 的最新扩展，提供用于非经典逻辑推理的语言和基础设施，并与现有 TPTP World 自由衔接