Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...

Tsukuba, Japan—Machine learning systems for autonomous driving, diagnostic medical imaging, and other applications require reliable and safe output. One such system design is the N-version machine ...

Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

The massive datasets that power machine learning algorithms and systems are complex, noisy, and vulnerable to various kinds of errors, contamination, and adversarial corruptions. As data science and ...

Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from massive datasets and powering next-gen AI. That future might be closer than ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...