BOT or NOT? This special series explores the evolving relationship between humans and machines, examining the ways that robots, artificial intelligence and automation are impacting our work and lives.

Why is machine learning so hard to explain? Making it clear can help with stakeholder buy-in Your email has been sent Getty Images/iStockphoto More must-read AI coverage ‘Catastrophic’ Stakes: OpenAI ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

If you’re a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops.

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false ...

In “The Adventure of the Silver Blaze,” Sherlock Holmes famously solved a case not by discovering a clue–but by noting its absence. In that case, it was a dog that didn’t bark, and that lack of ...

Python libraries that can interpret and explain machine learning models provide valuable insights into their predictions and ensure transparency in AI applications. A Python library is a collection of ...

This year’s winner of Best use of machine learning/AI, ActiveViam stood out for delivering a practical, production-ready ...

Too often, AI vendors tell us - "Machine learning handles that." So what exactly does that mean? Vendors are making what I call Deux es Machina claims about AI - now it's time to back those claims up.

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...

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