Artificial Intelligence in Science: Challenges, Opportunities and the Future of ResearchBuy this book
- Disclaimers
- Artificial intelligence in science: Overview and policy proposalsby A. Nolan
- Introduction
- AI and the productivity of science: Why does this matter?
- Is science getting harder?
- Artificial intelligence in science today
- AI and science in the near future: Challenges and ways forward
- Artificial intelligence in science: Further implications for public policy
- Artificial intelligence, science and developing countries
- Conclusion
- References
- Part I. Is science getting harder?
- Are ideas getting harder to find? A short review of the evidenceby M. Clancy
- The end of Moore’s Law? Innovation in computer systems continues at a high paceby H. Kressel
- Is technological progress in US agriculture slowing?by M. Clancy
- Eroom’s Law and the decline in the productivity of biopharmaceutical R&Dby J.W. Scannell
- Is there a slowdown in research productivity? Evidence from China and Germanyby P. Boeing, P. Hünermund
- Declining R&D efficiency: Evidence from Japanby T. Miyagawa
- Quantifying the “cognitive extent” of science and how it has changed over time and across countriesby S. Milojević
- What can bibliometrics contribute to understanding research productivity?by G. Abramo, C.A. D’Angelo
- Are ideas getting harder to find? A short review of the evidenceby M. Clancy
- Part II. Artificial intelligence in science today
- How can artificial intelligence help scientists? A (non-exhaustive) overviewby A. Ghosh
- Introduction
- The most typical uses of AI in science
- The need for interpretability
- Treatment of uncertainties when using AI in science
- Beyond inference
- Simulation in science with generative models
- Using AI to compress data
- Indirect benefits of the deep-learning revolution to science
- AI-supporting scientific communication
- Robotics
- Dangers and weaknesses of AI in science
- Conclusion
- References
- A framework for evaluating the AI-driven automation of scienceby R. King, H. Zenil
- Using machine learning to verify scientific claimsby L.L. Wang
- Robot scientists: From Adam to Eve to Genesisby R. King, O. Peter, P. Courtney
- From knowledge discovery to knowledge creation: How can literature-based discovery accelerate progress in science?by N.R. Smalheiser, G. Hahn-Powell, D. Hristovski, Y. Sebastian
- Introduction
- What LBD tools are available?
- New and emerging models of LBD
- How can informatics scientists best collaborate with bench scientists, especially in biology and medicine?
- Extending LBD analyses beyond text
- Prospects for LBD accelerating scientific progress outside biomedicine
- Conclusion
- References
- Note
- Advancing the productivity of science with citizen science and artificial intelligenceby L. Ceccaroni, J.L. Oliver, E. Roger, J. Bibby, P. Flemons, K. Michael, A. Joly
- Introduction
- How citizen science coupled with artificial intelligence can increase the productivity of science
- Increasing the speed and scale of data processing
- Increasing projects’ temporal and geographical scope
- Improving the quality of data collected and processed
- Supporting learning between humans and machines
- Leveraging new data sources
- Diversifying engagement opportunities
- Future applications
- Policy considerations
- Conclusion
- References
- What can artificial intelligence do for physics?by S. Hossenfelder
- AI in drug discoveryby K.Z. Szalay
- Data-driven innovation in clinical pharmaceutical researchby J. New
- Applying AI to real-world health-care settings and the life sciences: Tackling data privacy, security and policy challenges with federated learningby M. Galtier, D. Meadon
- Introduction
- The data challenges
- Why is it hard to gather data in health care?
- How does federated learning work?
- Why is federated learning the future of health care?
- What is happening in the federated learning space?
- Life sciences
- Clinical research
- Federated learning and data privacy
- Solving policy challenges using federated learning
- Conclusion
- References
- How can artificial intelligence help scientists? A (non-exhaustive) overviewby A. Ghosh
- Part III. The near future: challenges and ways forward
- Artificial intelligence in scientific discovery: Challenges and opportunitiesby R. King, H. Zenil
- Machine reading: Successes, challenges and implications for scienceby J. Dunietz
- Interpretability: Should – and can – we understand the reasoning of machine-learning systems?by H.M. Cartwright
- Combining collective and machine intelligence at the knowledge frontierby E. Malliaraki, A. Berditchevskaia
- Elicit: Language models as research toolsby J. Byun, A. Stuhlmüller
- Democratising artificial intelligence to accelerate scientific discoveryby J. Vanschoren
- Is there a narrowing of AI research?by J. Mateos-Garcia, J. Klinger
- Lessons from shortcomings in machine learning for medical imagingby G. Varoquaux, V. Cheplygina
- Artificial intelligence in scientific discovery: Challenges and opportunitiesby R. King, H. Zenil
- Part IV. Artificial intelligence in science: Implications for public policy
- Artificial intelligence for science and engineering: A priority for public investment in research and developmentby T. Hey
- The importance of knowledge bases for artificial intelligence in scienceby K. Forbus
- High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystemby G. Tourassi, M. Shankar, F. Wang
- Improving reproducibility of artificial intelligence research to increase trust and productivityby O.E. Gundersen
- Introduction
- Criteria for a definition of reproducibility
- Defining reproducibility
- Documenting computational experiments
- The degree to which a result has been reproduced
- The sources of irreproducibility
- Implications of sources on irreproducibility
- Implications for the research ecosystem
- Conclusion
- References
- AI and scientific productivity: Considering policy and governance challengesby K. Flanagan, B. Ribeiro, P. Ferri
- Artificial intelligence for science and engineering: A priority for public investment in research and developmentby T. Hey
- Part V. Artificial intelligence, science and developing countries
- Artificial intelligence and development projects: A case study in funding mechanisms to optimise research excellence in sub-Saharan Africaby J. Shawe-Taylor, D. Orlič
- Artificial intelligence for science in Africaby G. Barrett
- Artificial intelligence, developing-country science and bilateral co-operationby P.M. Addo
- Artificial intelligence and development projects: A case study in funding mechanisms to optimise research excellence in sub-Saharan Africaby J. Shawe-Taylor, D. Orlič
- Artificial intelligence in science: Overview and policy proposalsby A. Nolan
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