Artificial Intelligence in Science
Challenges, Opportunities and the Future of Research
The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions.
This publication is aimed at a broad readership, including policy makers, the public, and stakeholders in all areas of science. It is written in non-technical language and gathers the perspectives of prominent researchers and practitioners. The book examines various topics, including the current, emerging, and potential future uses of AI in science, where progress is needed to better serve scientific advancements, and changes in scientific productivity.
Additionally, it explores measures to expedite the integration of AI into research in developing countries.
A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance.
Combining collective and machine intelligence at the knowledge frontier
In the past decade, machine learning and deep learning have advanced significantly. They can now assist in the process of discovery, for instance, by analysing large volumes of data. On the other hand, humans have unique abilities such as creativity, intuition, contextualisation and abstraction. Moving forward, the best of both worlds must be combined. Instead of using only artificial intelligence (AI) to navigate scientific knowledge, novel AI and human collaborations could explore complexity and advance the frontiers of scientific understanding in new ways. This essay describes emerging tools and initiatives for discovering, encoding and synthesising knowledge that could help guide the way. The recommendations outline a pathway for changing scientific infrastructures, incentives and institutions to help hybrid human-AI science to flourish.
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