AI for Scientific Discovery

AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines.

AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence (AI) technologies in scientific research and discovery across the full breadth of scientific disciplines. AI technologies support discovery science in multiple ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research by underpinning the discovery process with AI-enabled standards and pipelines and supporting the management of large-scale data and knowledge resources so that they can be shared and integrated and serve as a background 'knowledge ecosystem' into which new discoveries can be embedded. However, there are limitations to what AI can achieve and its outputs can be biased and confounded and thus should not be blindly trusted. The latest generation of hybrid and 'human-in-the-loop' AI technologies have as their objective a balance between human inputs and insights and the power of number-crunching and statistical inference at a massive scale that AI technologies are best at.

Autorentext

Janna Hastings is a computer scientist with more than a decade of experience across the life, behavioural and social sciences. She is also a data scientist with a PhD in computational biology and extensive experience in bioinformatics, cheminformatics and psychoinformatics. She is currently Assistant Professor of Medical Knowledge and Decision Support at the University of Zurich, and Vice-Director of the School of Medicine at the University of St. Gallen. Her current research focuses on bridging the gaps between knowledge and learning to bring AI technologies in medicine closer to the needs and workflows of clinicians and to support truly interdisciplinary and integrative knowledge discovery.

Inhalt

Preface. Acknowledgements. About the Author. 1 Introduction: AI and the Digital Revolution in Science. 2 AI for Managing Scientific Literature and Evidence. 3 AI for Data Interpretation. 4 AI for Reproducible Research. 5 Limitations of AI and Strategies for Combating Bias. 6 Conclusion: AI and the Future of Scientific Discovery. Index.