Categories: AI/ML News

AI agents help explain other AI systems

Explaining the behavior of trained neural networks remains a compelling puzzle, especially as these models grow in size and sophistication. Like other scientific challenges throughout history, reverse-engineering how artificial intelligence systems work requires a substantial amount of experimentation: making hypotheses, intervening on behavior, and even dissecting large networks to examine individual neurons.

New method for comparing neural networks exposes how artificial intelligence works

A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the "black box" of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants,…

September 14, 2022

In "AI/ML News"

New method for comparing neural networks exposes how artificial intelligence works

A team has developed a novel approach for comparing neural networks that looks within the 'black box' of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving…

September 15, 2022

In "AI/ML News"

Understand Model Behavior During Training by Visualizing Metrics

You can learn a lot about neural networks and deep learning models by observing their performance over time during training. For example, if you see the training accuracy went worse with training epochs, you know you have issue with the optimization. Probably your learning rate is too fast. In this…

February 27, 2023

In "AI/ML Research"

AI Generated Robotic Content