Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.

Google today made available Neural Tangents, an open source software library written in JAX, a system for high-performance machine learning research. It's intended to help build AI models of variable width simultaneously, which Google says could allow "unprecedented" insight into the models' behavior and "help … open the black box" of machine learning. As Google senior research scientist Samuel S. Schoenholz and research engineer Roman Novak explain in a blog post, one of the key insights enabling progress in AI research is that increasing the width of models results in more regular behavior and makes them easier to understand. By way of refresher, all neural network models contain neurons (mathematical functions) arranged in interconnected layers that transmit signals from input data and slowly adjust the synaptic strength (weights) of each connection. That's how they extract features and learn to make predictions.