人工智能是定义超越边界的新问题的加速器。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Tayo Obafemi-Ajayi, Steven F Jennings, Yu Zhang, Kara Li Liu, Joan Peckham, Jason H Moore
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引用次数: 0

摘要

跨学科、跨学科、融合和无边界思维(NBT)研究是解决问题的方法和技术不可知的方法。重点是通过访问不同领域的多个知识来源和专家观点来定义问题,目标是访问所有可用的知识来源和观点。虽然访问所有可用的知识来源和观点可能被视为难以实现的目标,但随着最近人工智能的兴起,我们可能更接近这一目标。我们回顾了几个用于将这些策略付诸行动的方法和技术的例子,但本文的主要重点是人工智能的最新进展如何在定义新问题方面实现巨大飞跃。通过利用人工智能综合多个领域知识的能力,这些工具可以用来提出多个候选问题定义。人工智能具有独特的能力,能够利用比任何个人——甚至是一个非常大的团队——更多的知识来源。加上人类的智慧,可以定义更好的问题来解决复杂的学术或社会挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI as an accelerator for defining new problems that transcends boundaries.

Interdisciplinary, transdisciplinary, convergence, and No-Boundary Thinking (NBT) research are methodology and technology-agnostic approaches to problem solving. The focus is on defining problems informed by access to multiple knowledge sources and expert perspectives across different domains, with the goal of accessing all available knowledge sources and perspectives. While access to all available knowledge sources and perspectives could be seen as a difficult to attain objective, with the recent rise of AI we might be closer to approaching this goal. We review several examples of methodologies and technologies that have been used to put these strategies into action, but the primary focus of this paper is on how recent advances in AI now enable a quantum leap forward in defining new problems. By leveraging the capacity of AI to synthesize knowledge from multiple domains, these tools can be used to propose multiple candidate problem definitions. AI is uniquely able to draw upon many more knowledge sources than any individual-or even a very large team-could. Coupled with human intelligence, better problems can be defined to address complex scholarly or societal challenges.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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