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OECD Science, Technology and Innovation Outlook 2018

Adapting to Technological and Societal Disruption

image of OECD Science, Technology and Innovation Outlook 2018

The OECD Science, Technology and Innovation Outlook 2018 is the twelfth edition in a series that biennially reviews key trends in science, technology and innovation (STI) policy in OECD countries and a number of major partner economies. The 14 chapters within this edition look at a range of topics, notably the opportunities and challenges related to enhanced data access, the impacts of artificial intelligence on science and manufacturing, and the influence of digitalisation on research and innovation. The report also discusses the shortcomings of current policy measures, how the Sustainable Development Goals are re-shaping STI policy agendas, and the need for new - more flexible and agile - approaches to technology governance and policy design. While these disruptive changes challenge policy makers in a number of ways, the digital revolution underway also provides solutions for better policy targeting, implementation and monitoring.

This report relies on the latest academic work in the field, research and innovation statistical data, as well as data on wider trends and issues. It makes extensive use of country responses to the 2017 EC OECD STI policy survey (https://stip.oecd.org) and features contributions by renowned experts and academics to broaden the debate and provide more personal, sometimes controversial, angles to it.

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Artificial intelligence and machine learning in science

Finding solutions to many of the world’s major challenges requires increasing scientific knowledge. Artificial intelligence (AI) has the potential to increase the productivity of science, at a time when some evidence suggests that research productivity may be falling. This chapter first outlines the three key technological developments driving the recent rise in AI: vastly improved computer hardware, vastly increased availability of data and vastly improved AI software. It then describes the promises of AI in science, illustrating its current uses across a range of scientific disciplines. Later sections raise the question of explainability of AI and the implications for science, highlighting gaps in education and training programmes that slow down the rollout of AI in science. The chapter finishes by envisioning a future in which increasingly intelligent AI systems, working with human scientists, help address society’s most pressing problems, while expanding scientific knowledge.

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