Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily ...

Top eigenvector of AGOP of two separate models, MLPs and Laplace kernel machines, captured similar features (cosine similarity greater than .99) when trained on the same data from CelebA across ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

Unlike their more modern large language model counterparts, artificial neural networks require human input to learn and function. ANNs have been around since the 1950s. They started taking hold in ...

In 1943, a pair of neuroscientists were trying to describe how the human nervous system works when they accidentally laid the foundation for artificial intelligence. In their mathematical framework ...

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Sometimes in the rush to explore our interactions with neural nets (often in the form of LLMs) we forget to think about our own operating system and how it works. Of course, scientists did spend a lot ...

Computing power has increased exponentially over the past few decades. We now have cameras on smartphones with incredible computational photography, voice assistants that respond near instantaneously, ...

For the past decade, AI researcher Chris Olah has been obsessed with artificial neural networks. One question in particular engaged him, and has been the center of his work, first at Google Brain, ...