Holzinger Andreas, Keiblinger Katharina, Holub Petr, Zatloukal Kurt, Müller Heimo
University of Natural Resources and Life Sciences Vienna, Austria; Medical University Graz, Austria; Alberta Machine Intelligence Institute Edmonton, Canada.
University of Natural Resources and Life Sciences Vienna, Austria.
N Biotechnol. 2023 May 25;74:16-24. doi: 10.1016/j.nbt.2023.02.001. Epub 2023 Feb 6.
Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.
由于(例如ChatGPT这样的)成功范例,如今人工智能(AI)已成为众人热议的话题。当生物技术的进步与人工智能的进步相结合时,前所未有的新潜在解决方案就出现了。这有助于解决许多全球性问题,并为重要的可持续发展目标做出贡献。当前的例子包括粮食安全、健康与福祉、清洁水、清洁能源、负责任的消费与生产、气候行动、水下生物,以及保护、恢复和促进陆地生态系统的可持续利用、可持续管理森林、防治荒漠化、制止和扭转土地退化以及遏制生物多样性丧失。如今人工智能在生命科学中无处不在。主题范围广泛,涵盖机器学习和大数据分析、知识发现和数据挖掘、生物医学本体、基于知识的推理、自然语言处理、不确定性下的决策支持和推理、时空表示与推理,以及具有生物技术应用的可解释人工智能(XAI)的方法学方面。在这篇社论前言中,我们概述了本期特刊所涉及的每个主题的开放研究问题和挑战。潜在作者可以直接将此作为撰写论文的指导方针。