What if artificial intelligence could evolve as seamlessly as humans, learning from every interaction without forgetting what it already knows? Prompt Engineering takes a closer look at how the ...

An RIT scientist has been tapped by the National Science Foundation to solve a fundamental problem that plagues artificial neural networks. Christopher Kanan, an assistant professor in the Chester F.

A new study from the University of Illinois Urbana-Champaign suggests that the loss of skills often seen when fine-tuning large AI models may not be true forgetting but a temporary bias in their ...

The model consists of multiple experts with lateral connections. For each new task, a new expert is initialized and trained on the current task dataset. Then the new expert is compared with previous ...

When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill. Researchers at MIT, the ...