Artificial general intelligence (AGI) has gained global recognition as a
future technology due to the emergence of breakthrough large language models
and chatbots such as GPT-4 and ChatGPT, respectively. AGI aims to replicate
human intelligence through computer systems, which is one of the critical
technologies having the potential to revolutionize the field of education.
Compared to conventional AI models, typically designed for a limited range of
tasks, demand significant amounts of domain-specific data for training and may
not always consider intricate interpersonal dynamics in education. AGI, driven
by the recent large pre-trained models, represents a significant leap in the
capability of machines to perform tasks that require human-level intelligence,
such as reasoning, problem-solving, decision-making, and even understanding
human emotions and social interactions. This work reviews AGI's key concepts,
capabilities, scope, and potential within future education, including setting
educational goals, designing pedagogy and curriculum, and performing
assessments. We also provide rich discussions over various ethical issues in
education faced by AGI and how AGI will affect human educators. The development
of AGI necessitates interdisciplinary collaborations between educators and AI
engineers to advance research and application efforts.

这篇论文综述了人工通用智能在教育领域的概念、能力、以及可能性，包括设定教育目标、设计教学法、课程以及评估。此外，也讨论了 AGI 在教育中所面临的各种伦理问题，以及人工通用智能如何影响人类教育工作者。该领域的进展需要教育工作者和 AI 工程师之间的跨学科合作。

人工通用智能（AGI）在教育中的应用

Artificial General Intelligence (AGI) for Education

Funding agencies are largely relied on a topic matching between domain
experts and research proposals to assign proposal reviewers. As proposals are
increasingly interdisciplinary, it is challenging to profile the
interdisciplinary nature of a proposal, and, thereafter, find expert reviewers
with an appropriate set of expertise. An essential step in solving this
challenge is to accurately model and classify the interdisciplinary labels of a
proposal. Existing methodological and application-related literature, such as
textual classification and proposal classification, are insufficient in jointly
addressing the three key unique issues introduced by interdisciplinary proposal
data: 1) the hierarchical structure of discipline labels of a proposal from
coarse-grain to fine-grain, e.g., from information science to AI to
fundamentals of AI. 2) the heterogeneous semantics of various main textual
parts that play different roles in a proposal; 3) the number of proposals is
imbalanced between non-interdisciplinary and interdisciplinary research. Can we
simultaneously address the three issues in understanding the proposal's
interdisciplinary nature? In response to this question, we propose a
hierarchical mixup multiple-label classification framework, which we called
H-MixUp. H-MixUp leverages a transformer-based semantic information extractor
and a GCN-based interdisciplinary knowledge extractor for the first and second
issues. H-MixUp develops a fused training method of Wold-level MixUp,
Word-level CutMix, Manifold MixUp, and Document-level MixUp to address the
third issue.

本文提出了一种名为 H-MixUp 的层次混合多标签分类框架，利用基于转换器的语义信息提取器和基于 GCN 的跨学科知识提取器来解决交叉学科提案的模型分类问题。

层次化 MixUp 多标签分类在不平衡的跨学科研究提议中的应用

Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals

With the widespread and pervasive use of Artificial Intelligence (AI) for
automated decision-making systems, AI bias is becoming more apparent and
problematic. One of its negative consequences is discrimination: the unfair, or
unequal treatment of individuals based on certain characteristics. However, the
relationship between bias and discrimination is not always clear. In this
paper, we survey relevant literature about bias and discrimination in AI from
an interdisciplinary perspective that embeds technical, legal, social and
ethical dimensions. We show that finding solutions to bias and discrimination
in AI requires robust cross-disciplinary collaborations.

本文从技术、法律、社会和伦理学的跨学科视角，对人工智能中的偏差和歧视进行了调查研究，并表明解决人工智能中的偏差和歧视需要进行强大的跨学科合作。