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 ...

Memristors consume extremely little power and behave similarly to brain cells. Researchers have now introduced novel memristive that offer significant advantages: they are more robust, function across ...

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 ...