Apple has launched MLX, a machine learning framework optimized for their silicon chips. It supports various tasks including training transformer models, text and image generation, and speech recognition. The article also touches on a phenomenon called "grokking" related to neural network learning.

This article discusses Apple's MLX framework, designed for efficient use of M-series chips in protein folding tasks. It highlights the advantages of unified memory architecture and provides a detailed example of adapting OpenFold3 code to work with MLX. The author shares performance results showing significant speed improvements compared to traditional setups.

Rmlx is an R package that connects to Apple's MLX framework, allowing users to leverage GPU computing on Apple Silicon. It supports various backend configurations for efficient matrix operations and automatic differentiation. The package facilitates high-performance computations directly from R, making it suitable for data analysis and machine learning tasks.