Previous columns in this series introduced the problem of data protection in machine learning (ML), emphasizing the real challenge that operational query data pose. That is, when you use an ML system, ...

Forbes contributors publish independent expert analyses and insights. Rachel Wells is a writer who covers leadership, AI, and upskilling. If you're looking for a job that pays six figures, is always ...

Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train ...

What’s keeping leaders from adopting machine learning? Well, tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort. But five vectors of progress are ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

The key to developing flexible machine-learning models that are capable of reasoning like people do may not be feeding them oodles of training data. Instead, a new study suggests, it might come down ...

Machine learning is powering most of the recent advancements in AI, including computer vision, natural language processing, predictive analytics, autonomous systems, and a wide range of applications.