Machine learning (ML) and computer vision (CV) technologies are vital branches of artificial intelligence (AI) that help automate tasks and increase efficiency across industries. Experts predict that ...

As discussed in Chapter 3, extending safety engineering to cyber-physical systems that include artificial intelligence (AI) components brings a new set of challenges that suggest changing how this ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Jinsong Yu shares deep architectural insights ...

The world of machine learning isn’t just for fresh-faced graduates or seasoned tech veterans. More and more people are discovering the fascinating realm of machine learning well into their 40s, 50s, ...

Applying Machine Learning (ML) to physiological data poses several challenges. While ML can be effectively used to model well-defined systems, applying it to a system as complex as the human body ...

Understanding and preventing drug side effects holds a profound influence on drug development and utilization, profoundly impacting patients’ physical and mental well-being. Traditional artificial ...

A new review highlights how machine learning is transforming the way scientists detect and measure organic pollutants in the ...

Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing significant challenges to agricultural productivity and global food security.

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 ...