Amidst the robust impetus from artificial intelligence (AI) and big data,
edge intelligence (EI) has emerged as a nascent computing paradigm,
synthesizing AI with edge computing (EC) to become an exemplary solution for
unleashing the full potential of AI services. Nonetheless, challenges in
communication costs, resource allocation, privacy, and security continue to
constrain its proficiency in supporting services with diverse requirements. In
response to these issues, this paper introduces socialized learning (SL) as a
promising solution, further propelling the advancement of EI. SL is a learning
paradigm predicated on social principles and behaviors, aimed at amplifying the
collaborative capacity and collective intelligence of agents within the EI
system. SL not only enhances the system's adaptability but also optimizes
communication, and networking processes, essential for distributed intelligence
across diverse devices and platforms. Therefore, a combination of SL and EI may
greatly facilitate the development of collaborative intelligence in the future
network. This paper presents the findings of a literature review on the
integration of EI and SL, summarizing the latest achievements in existing
research on EI and SL. Subsequently, we delve comprehensively into the
limitations of EI and how it could benefit from SL. Special emphasis is placed
on the communication challenges and networking strategies and other aspects
within these systems, underlining the role of optimized network solutions in
improving system efficacy. Based on these discussions, we elaborate in detail
on three integrated components: socialized architecture, socialized training,
and socialized inference, analyzing their strengths and weaknesses. Finally, we
identify some possible future applications of combining SL and EI, discuss open
problems and suggest some future research.

整合边缘智能和社交学习有助于发展协作智能网络，通过解决沟通挑战和网络策略等问题，优化系统效能，进而能够实现人工智能服务的充分潜力释放。

社会化学习：网络系统中边缘智能的范式转变综述

Socialized Learning: A Survey of the Paradigm Shift for Edge  Intelligence in Networked Systems

Miscommunication and communication challenges between instructors and
students represents one of the primary barriers to post-secondary learning.
Students often avoid or miss opportunities to ask questions during office hours
due to insecurities or scheduling conflicts. Moreover, students need to work at
their own pace to have the freedom and time for the self-contemplation needed
to build conceptual understanding and develop creative thinking skills. To
eliminate barriers to student engagement, academic institutions need to
redefine their fundamental approach to education by proposing flexible
educational pathways that recognize continuous learning. To this end, we
developed an AI-augmented intelligent educational assistance framework based on
a power language model (i.e., GPT-3) that automatically generates
course-specific intelligent assistants regardless of discipline or academic
level. The virtual intelligent teaching assistant (TA) system will serve as a
voice-enabled helper capable of answering course-specific questions concerning
curriculum, logistics and course policies. It is envisioned to improve access
to course-related information for the students and reduce logistical workload
for the instructors and TAs. Its GPT-3-based knowledge discovery component as
well as the generalized system architecture is presented accompanied by a
methodical evaluation of the system accuracy and performance.

通过提供弹性的教育模式建议，基于 GPT-3 的智能教育辅助框架被研发，能够自动生成课程助手，可以回答与教学相关的问题，降低教师和助教的工作量同时提高学生获得课程相关信息的机会。

面向高等教育的跨平台和课程导向的智能助手

Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide
range of fields, ranging from speech processing, image classification to drug
discovery. This is driven by the explosive growth of data, advances in machine
learning (especially deep learning), and easy access to vastly powerful
computing resources. Particularly, the wide scale deployment of edge devices
(e.g., IoT devices) generates an unprecedented scale of data, which provides
the opportunity to derive accurate models and develop various intelligent
applications at the network edge. However, such enormous data cannot all be
sent from end devices to the cloud for processing, due to the varying channel
quality, traffic congestion and/or privacy concerns. By pushing inference and
training processes of AI models to edge nodes, edge AI has emerged as a
promising alternative. AI at the edge requires close cooperation among edge
devices, such as smart phones and smart vehicles, and edge servers at the
wireless access points and base stations, which however result in heavy
communication overheads. In this paper, we present a comprehensive survey of
the recent developments in various techniques for overcoming these
communication challenges. Specifically, we first identify key communication
challenges in edge AI systems. We then introduce communication-efficient
techniques, from both algorithmic and system perspectives for training and
inference tasks at the network edge. Potential future research directions are
also highlighted.

本文综述了近期在人工智能边缘计算系统中与解决通信挑战相关的技术，着重介绍了从算法和系统两个方面提高在网络边缘的训练与推理的通信效率的方法，并突出了未来研究方向。