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Beschreibung
Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers’ awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
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Technische Daten


Erscheinungsdatum
17.05.2024
Sprache
Englisch
EAN
9789811968167
Herausgeber
Springer Singapore
Serien- oder Bandtitel
Artificial Intelligence: Foundations, Theory, and Algorithms
Sonderedition
Nein
Autor
Xiaowei Huang, Gaojie Jin, Wenjie Ruan
Seitenanzahl
321
Einbandart
Broschiert
Autorenporträt
Xiaowei Huang is currently a Reader of Computer Science and Director of the Autonomous Cyber-Physics Systems lab at the University of Liverpool (UoL). His research is concerned with the development of automated verification techniques that ensure the correctness and reliability of intelligent systems. He has published more than 80 papers, primarily in leading conference proceedings and journals in the fields of Artificial Intelligence (e.g. Artificial Intelligence Journal, ACM Transactions on Computational Logics, NeurIPS, AAAI, IJCAI, ECCV), Formal Verification (e.g. CAV, TACAS, and Theoretical Computer Science) and Software Engineering (e.g. IEEE Transactions on Reliability, ICSE and ASE). He has been invited to give talks at several leading conferences, discussing topics related to the safety and security of applying machine learning algorithms to critical applications. He has co-chaired the AAAI and IJCAI workshop series on Artificial Intelligence Safety and been the PI or co-PI ofseveral Dstl (Ministry of Defence, UK), EPSRC and EU H2020 projects. He is the Director of the Autonomous Cyber Physical Systems Lab at Liverpool.
Schlagwörter
Deep Learning, Machine Learning, Safety, Reliability, Robustness
Thema-Inhalt
UYQM - Maschinelles Lernen UR - Computersicherheit UTN - Netzwerksicherheit UYQ - Künstliche Intelligenz
Höhe
235 mm
Breite
15.5 cm

Hersteller: Springer Nature Singapore, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH

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