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AI for Scientific Discovery

Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
AI for Scientific Discovery offers an accessible introduction to the diverse applications of artificial intelligence across scientific disciplines. It explores how AI assists in literature management, data analysis, and addressing reproducibility challenges by creating standards and pipelines. The book highlights the creation of integrated knowledge ecosystems and emphasises the importance of combining human insight with AI capabilities through hybrid and 'human-in-the-loop' approaches.
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Format: Hardback
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

This book is ideal for researchers, scientists, and students in engineering and technology interested in leveraging AI to enhance scientific research and discovery practices.

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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.

Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

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 โ€˜data scienceโ€™.

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 they can be shared and integrated, serving as a background โ€˜knowledge ecosystemโ€™ into which new discoveries can be embedded.

However, there are limitations to what AI can achieve, and its outputs can be biased and confounded; thus, they should not be blindly trusted. The latest generation of hybrid and โ€˜human-in-the-loopโ€™ AI technologies aim to balance human inputs and insights with the power of number-crunching and statistical inference at a massive scale, which AI technologies are best at.

Series: AI for Everything

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  • AI for Peace
    The role of artificial intelligence in war is widely recognised, but is there also a role for AI in fostering peace and preventing conflict? AI for Peace provides a new perspective on AI as a potential force for good in conflict-affected countries through its uses for early warning, combating hate speech, human rights investigations, and analysing the effects of climate...
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  • AI for Scientific Discovery
    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...
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    Sold Out

Book Details

INFORMATION

ISBN: 9781032128771

Publisher: Taylor & Francis Ltd

Format: Hardback

Date Published: 06 June 2023

Country: United Kingdom

Imprint: CRC Press

Illustration: 1 Line drawings, black and white; 4 Halftones, black and white; 5 Illustrations, black and white

Audience: Tertiary education, Professional and scholarly

DIMENSIONS

Width: 129.0mm

Height: 198.0mm

Weight: 20g

Pages: 120

About the Author

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.

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