After years of dominance by the form of AI known as the transformer, the hunt is on for new architectures. Transformers aren’t especially efficient at processing and analyzing vast amounts of data, at ...

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...