Details

Autor(en) / Beteiligte

• Organisation for Economic Co-operation and Development

Titel

Artificial Intelligence in Science : Challenges, Opportunities and the Future of Research [electronic resource]

Beschreibungen/Notizen

• Artificial intelligence and development projects: A case study in funding mechanisms to optimise research excellence in sub-Saharan Africa -- Applying AI to real-world health-care settings and the life sciences: Tackling data privacy, security and policy challenges with federated learning -- How can artificial intelligence help scientists? A (non-exhaustive) overview -- Artificial intelligence for science and engineering: A priority for public investment in research and development -- Declining R&D efficiency: Evidence from Japan -- Machine reading: Successes, challenges and implications for science -- Eroom's Law and the decline in the productivity of biopharmaceutical R&D -- Using machine learning to verify scientific claims -- Foreword -- Democratising artificial intelligence to accelerate scientific discovery -- Is there a narrowing of AI research? -- From knowledge discovery to knowledge creation: How can literature-based discovery accelerate progress in science? -- A framework for evaluating the AI-driven automation of science -- Interpretability: Should - and can - we understand the reasoning of machine-learning systems? -- Artificial intelligence for science in Africa -- Is there a slowdown in research productivity? Evidence from China and Germany -- High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem -- Artificial intelligence in scientific discovery: Challenges and opportunities -- Artificial intelligence in science: Overview and policy proposals -- Preface -- What can artificial intelligence do for physics? -- Are ideas getting harder to find? A short review of the evidence -- Lessons from shortcomings in machine learning for medical imaging -- AI and scientific productivity: Considering policy and governance challenges -- Is technological progress in US agriculture slowing? -- AI in drug discovery -- Combining collective and machine intelligence at the knowledge frontier -- Elicit: Language models as research tools -- Improving reproducibility of artificial intelligence research to increase trust and productivity -- What can bibliometrics contribute to understanding research productivity? -- Executive summary -- Quantifying the "cognitive extent" of science and how it has changed over time and across countries -- Robot scientists: From Adam to Eve to Genesis -- The end of Moore's Law? Innovation in computer systems continues at a high pace -- The importance of knowledge bases for artificial intelligence in science -- Data-driven innovation in clinical pharmaceutical research -- Artificial intelligence, developing-country science and bilateral co‑operation -- Advancing the productivity of science with citizen science and artificial intelligence.
• 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.