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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.
John Giannandrea, Apple's senior VP for AI and Machine Learning, will retire in spring 2026 but remain as an advisor until then. Amar Subramanya has been appointed as the new VP of AI, tasked with leading significant projects in AI research and development.
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.
Apple has unveiled updates to its on-device and server foundation language models, enhancing generative AI capabilities while prioritizing user privacy. The new models, optimized for Apple silicon, support multiple languages and improved efficiency, incorporating advanced architectures and diverse training data, including image-text pairs, to power intelligent features across its platforms.
Dots.ocr, a new 3B parameter OCR model from RedNote, enables competitive on-device optical character recognition, leveraging Apple's Neural Engine for efficiency. The article outlines the challenges and processes involved in converting the model from PyTorch to Core ML, detailing the steps taken to optimize its performance for on-device use. Future parts of the series will focus on further integration and optimization strategies.