The TWIML AI Podcast · Monday, July 27, 2026
Damian Borth, a professor of AI and Machine Learning, suggests a novel approach where the weights of already trained neural networks can serve as input data for training new models. This 'weight space learning' could accelerate and refine the process of creating new AI models.
“So we basically thought about this very simple idea. What happens actually if we take the weights of trained neural networks as the input to train a neural network to understand these weights that we have out there, much, much better.”
“Thinking about that, that you can treat the weights as an input modality, gives you suddenly this opportunity of, of, can we be much, much faster in creating new weights for particular tasks, or can we be much more precise in analyzing weights when somebody gives me a new neural network that I'm not knowledgeable about, and I never saw before?”