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.

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.

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

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

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

The machine learning model the team implemented overcame the challenge of limited data to incorporate the alpha conotoxins' amino acid sequences, secondary structure propensities and electrostatic ...

The big AI companies all seem to view agents as the next big thing. This week, Clune’s lab revealed its latest open-ended learning project: an AI program that invents and builds AI agents.