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Artificial Intelligence in Science

Challenges, Opportunities and the Future of Research

image of Artificial Intelligence in Science

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.

English

The importance of knowledge bases for artificial intelligence in science

For artificial intelligence (AI) systems to increase the productivity of science, they need to understand both the domains of science they are operating in, and the world in which that domain is embedded. In other words, they need knowledge bases that provide such information in explicit and verifiable forms to support reasoning that includes transparent explanations for their conclusions. This essay explains the idea of knowledge bases and knowledge graphs, summarising the state of the art and the improvements needed to support broader uses of AI in science. These improvements include commonsense knowledge to tie scientific concepts to the everyday world and to provide common ground for communication with human partners; expressive representations for encoding scientific knowledge; and robust reasoning techniques that go beyond simple retrieval. Research could work towards an open knowledge network to provide a community resource that supports re-use, replication and dissemination.

English

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