Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed.

学术挑战是推动现有技术发展、将特定主题和问题置于科学界关注的有效手段，同时也是缩小受限社群在访问和参与塑造研究领域方面的差距的重要方式。本文回顾了过去几年里在机器学习及相关领域内最具影响力的竞赛，并分析了各个学科领域的挑战。对科学挑战的目标、主要成就和未来几年的期望进行了审视。

学术竞赛

Academic competitions

Acknowledgments in scientific papers may give an insight into aspects of the
scientific community, such as reward systems, collaboration patterns, and
hidden research trends. The aim of the paper is to evaluate the performance of
different embedding models for the task of automatic extraction and
classification of acknowledged entities from the acknowledgment text in
scientific papers. We trained and implemented a named entity recognition (NER)
task using the Flair NLP-framework. The training was conducted using three
default Flair NER models with two differently-sized corpora. The Flair
Embeddings model trained on the larger training corpus showed the best accuracy
of 0.77. Our model is able to recognize six entity types: funding agency, grant
number, individuals, university, corporation and miscellaneous. The model works
more precise for some entity types than the others, thus, individuals and grant
numbers showed very good F1-Score over 0.9. Most of the previous works on
acknowledgement analysis were limited by the manual evaluation of data and
therefore by the amount of processed data. This model can be applied for the
comprehensive analysis of the acknowledgement texts and may potentially make a
great contribution to the field of automated acknowledgement analysis.

本论文旨在评估不同嵌入模型在科学论文中对已确认的实体进行自动提取和分类的性能，并提供了一种基于 Flair NLP 框架的命名实体识别（NER）任务的解决方案，以了解科学社区的奖励体系、合作模式和隐藏研究趋势。

科学文档中被承认实体的自动提取和分类的嵌入模型评估

Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents

There is growing concern over the potential misuse of artificial intelligence
(AI) research. Publishing scientific research can facilitate misuse of the
technology, but the research can also contribute to protections against misuse.
This paper addresses the balance between these two effects. Our theoretical
framework elucidates the factors governing whether the published research will
be more useful for attackers or defenders, such as the possibility for adequate
defensive measures, or the independent discovery of the knowledge outside of
the scientific community. The balance will vary across scientific fields.
However, we show that the existing conversation within AI has imported concepts
and conclusions from prior debates within computer security over the disclosure
of software vulnerabilities. While disclosure of software vulnerabilities often
favours defence, this cannot be assumed for AI research. The AI research
community should consider concepts and policies from a broad set of adjacent
fields, and ultimately needs to craft policy well-suited to its particular
challenges.

本研究针对人工智能 (AI) 研究的潜在滥用问题，探讨了科学研究对于保护和滥用 AI 技术之间的平衡关系，并论述了存在于 AI 领域内的软件漏洞披露问题和 AI 研究之间的差异，最终提出了需要寻求特定挑战的政策建议。