Name: Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda (en Inglés)
Price: 124884.00 COP
Availability: InStock
Author: National Academies of Sciences Engineeri; Division on Engineering and Physical Sci; Computer Science and Telecommunications
ISBN: 9780309726665

portada Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda (en Inglés)

Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda (en Inglés)

Formato

Libro Físico

Editorial

National Academies Press

Autor

National Academies of Sciences Engineeri; Division on Engineering and Physical Sci; Computer Science and Telecommunications

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9780309726665

Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda (en Inglés)

National Academies Of Sciences Engineeri; Division On Engineering And Physical Sci; Computer Science And Telecommunications (Autor) · National Academies Press · Tapa Blanda

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Reseña del libro "Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda (en Inglés)"

Advances in artificial intelligence, and specifically in machine learning, are enabling new capabilities across nearly every sector of the economy. Many of these applications - such as automated vehicles, the power grid, or surgical robots - are safety critical: where malfunctions can result in harm to people, the environment, or property. While machine learning is already being deployed to enhance the capabilities of some physical systems, extending the rigorous practices of safety engineering to include machine learning components brings significant challenges. Machine Learning for Safety-Critical Applications explores ways to safely integrate machine learning into physical systems and presents research priorities for improving safety, testing, and evaluation. This report finds that designing machine learning algorithms in a way that aligns with safety engineering standards will require changes in research, training, and engineering practice - as well as a shift away from focusing on algorithmic performance in isolation.