Explainable AI: From the peak of inflated expectations to the pitfalls of interpreting machine learning models We have reached peak hype for explainable AI.

Explainable AI: A guide for making black box machine learning models explainable In the future, AI will explain itself, and interpretability could boost machine intelligence research.

Chain Of Thought Models Machine learning models are nothing more than incredibly complex functions with billions, and now even trillions of learned parameters.

Researchers have developed a new explainable artificial intelligence (AI) model to reduce bias and enhance trust and accuracy in machine learning-generated decision-making and knowledge organization.

The growing trend of AI means that it’s business-critical to understand how AI-enabled systems arrive at specific outputs.

As machine learning techniques become increasingly used in the sciences, a team of researchers in Lawrence Livermore National Laboratory's Computing and - Read more from Inside HPC & AI News.

Explainable AI, abbreviated "XAI," is an emerging set of techniques to peel back the curtains on complex AI systems.

The purpose of the challenge was to canvas the ATEC workforce for opportunities to solve real-world problems with AI and machine learning solutions that had a high potential return on investment.

In an era where AI adoption frequently outpaces regulatory readiness, Archana Pattabhi, Senior Vice President at a leading global bank, led a forward-looking transformation that redefined how ...