Hopfield networks to create

"Krotov and his colleagues recently developed a new deep learning architecture called the energy transformer.

"Typical AI architectures are usually found by trial and error. But Krotov thinks energy transformers could be designed more intentionally with a specific energy landscape in mind, like a more complex take on a Hopfield network.

"Though Hopfield networks were originally designed to remember, researchers are now exploring how they can be used to create. 

"Image generators such as Midjourney are powered by diffusion models, which are themselves inspired by the physics of diffusion. To train them, researchers add noise to the training data —say, pictures of cats —and then teach the model to remove the noise. That’s a lot like what a Hopfield network does, except instead of always landing on the same cat picture, a diffusion model removes non-cat noise from a noisy, random starting state to produce a new cat.

"It turns out that diffusion models can be understood as a particular kind of modern Hopfield network, according to Krotov and his colleagues, including Benjamin Hoover, Yuchen Liang, and Bao Pham. And that approach can be used to predict aspects of these networks’ behavior. 

"Their work suggests that feeding a modern Hopfield network more and more data doesn’t just saturate its memory. Instead, the model’s energy landscape gets so rugged that it is more likely to settle on a made-up memory than a real one. It becomes a diffusion model."