AI, famously, is a black-box solution. While neural networks are designed for specific applications, the training process produces millions or billions of parameters without us having much ...
Intelligent control has become a cornerstone of modern robotics and autonomous systems, where agents must operate safely and reliably despite uncertainty in ...
When it comes to adopting artificial intelligence in high-stakes settings like hospitals and airplanes, good AI performance and brief worker training on the technology is not sufficient to ensure ...
Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
In recent years, the rapid advancement of machine learning (ML) has led to their early integration into safety-critical systems. As noted in Chapter 2, these technologies offer significant potential ...
A recent experience with managing the tenth anniversary version of a technical conference (www.iccve2022.org) triggered a process of reflection on the last decade of artificial intelligence. The first ...
A technical paper titled “Envisioning a Safety Island to Enable HPC Devices in Safety-Critical Domains” was published by researchers at Barcelona Supercomputing Center and Intel. “HPC (High ...
As safety-critical systems become increasingly complex, the choice of processor architecture plays an important role in ensuring functional safety and system reliability. Consider an automotive ...
Ensuring reliable design and verification of the ICs used in safety-critical systems means compliance with industry standards such as ISO 26262. June 22nd, 2022 - By: Siemens EDA Automotive ...
Why testing alone cannot assure correctness in complex safety-critical software, and how edge cases and undefined behavior are able to evade validation efforts. How formal verification is used to ...
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