Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial intelligence for science and engineering: A priority for public investment in research and development
2
Zitationen
1
Autoren
2023
Jahr
Abstract
The rapid growth of scientific data generated both by scientific experiments at large national and international facilities and by model simulations on supercomputers epitomises Jim Gray’s “Fourth Paradigm” of data-intensive science. The use of artificial intelligence (AI) technologies to help automate the generation and analysis of such datasets is increasingly necessary. This essay describes the great potential for the use of AI and deep learning technologies to transform many fields of science. It draws particular attention to the conclusions of Town Hall meetings organised by the US Department of Energy (DOE). These meetings explored the potential for AI to accelerate science and the need for major public research and development (R&D) funding. Such funding could enable multidisciplinary teams of academic researchers to generate comparable breakthroughs to those of commercial companies such as Google DeepMind.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.428 Zit.