Peter Gray
@peteryugray.bsky.social
AI / ML comms person (formerly Meta, Linden Lab). Guitar in Butterfly Knives. Vespa enthusiast.
New Apple ML Research Highlight: "ParaRNN: Large-Scale Nonlinear RNNs, Trainable in Parallel" machinelearning.apple.com/research/lar... 1/7
ParaRNN: Large-Scale Nonlinear RNNs, Trainable in Parallel
Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based…
machinelearning.apple.com
Apple researchers are sharing new work advancing AI and ML at #ICLR 2026 - for highlights, see the link in the thread below 1/5
me, emotionally writing an essay on the use of force by federal agents: ok but what if i packaged this in the most insane way possible www.theverge.com/policy/86857...
Best gas masks
“How did these people go out and get gas masks?” AG Bondi asked.
theverge.com
Apple's researchers continue to focus on multimodal LLMs, with studies exploring their use for image generation, understanding, and multi-turn web searches with cropped images.
Apple AI research shows how MLLMs understand, generate, search for images
Apple's researchers continue to focus on multimodal LLMs, with studies exploring their use for image generation, understanding, and multi-turn web searches with cropped images.
appleinsider.com
A great primer - and ICYMI: a *single* parameter (among billions) - a "super weight" can determine if an LLM's output will be coherent or nonsense machinelearning.apple.com/research/the...
The “Super Weight:” How Even a Single Parameter can Determine a Large Language Model’s Behavior
A recent paper from Apple researchers,
machinelearning.apple.com
MIT Technology Review@technologyreview.com · 7mo agoParameters are the mysterious numbers that make your favorite AI models tick. But what exactly do they do?
Apple continues to focus on AI-powered image modification, with new studies detailing evaluation frameworks and an AI model that can turn 2D images into 3D scenes in a second.
Apple's AI & ML research papers show instant 3D image conversion, more
Apple continues to focus on AI-powered image modification, with new studies detailing evaluation frameworks and an AI model that can turn 2D images into 3D scenes in a second. Here's what the company's research has revealed.
appleinsider.com
Heading to #NeurIPS2025 next week? Check out this post for some highlights of the work Apple researchers will present: machinelearning.apple.com/research/neu... - across topics including... 1/7
Apple Machine Learning Research at NeurIPS 2025
Apple researchers advance AI and ML through fundamental research, and to support the broader research community and help accelerate progress…
machinelearning.apple.com
MLX + the Neural Accelerators in the M5 GPU = up to 4x faster LLM inference🚀 machinelearning.apple.com/research/exp...
Exploring LLMs with MLX and the Neural Accelerators in the M5 GPU
Mac with Apple silicon is increasingly popular among AI developers and researchers interested in using their Mac to experiment with the…
machinelearning.apple.com
Apple’s new language model can write long texts incredibly fast
Apple’s new language model can write long texts incredibly fast
In a new study, Apple researchers present a diffusion model that can write up to 128 times faster than its counterparts. Here’s how it works. more…
9to5mac.com
Apple researchers develop SimpleFold, a lightweight AI for protein folding prediction
Apple researchers develop SimpleFold, a lightweight AI for protein folding prediction
Google DeepMind’s work with AlphaFold has been nothing short of a miracle, but it is computationally expensive. With that in mind, Apple researchers set off to develop an alternative method to use AI to predict the 3D structure of proteins, and it shows promise. Here are the details. more…
9to5mac.com
New Apple #ML Research Highlight: The "Super Weight:" How Even a Single Parameter can Determine an #LLM's Behavior machinelearning.apple.com/research/the...
The
A recent paper from Apple researchers,
machinelearning.apple.com
New Apple #ML Research Highlight: "FastVLM: Efficient Vision Encoding for Vision Language Models" machinelearning.apple.com/research/fas...
FastVLM: Efficient Vision Encoding for Vision Language Models
Vision Language Models (VLMs) enable visual understanding alongside textual inputs. They are typically built by passing visual tokens from a…
machinelearning.apple.com
New paper: 'Apple Intelligence Foundation Language Models Tech Report 2025' provides technical details for two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: machinelearning.apple.com/research/app...
Apple Intelligence Foundation Language Models Tech Report 2025
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and…
machinelearning.apple.com
Next week at #ICML2025, Apple researchers will present many new papers across a range of topics in #AI & #ML - check out this post for some highlights: machinelearning.apple.com/research/icm...
New post: "Updates to Apple's On-Device and Server Foundation Language Models" - details the architectures, training data and recipes, inference optimization techniques, and evaluation results compared to comparable models: machinelearning.apple.com/research/app...
Updates to Apple's On-Device and Server Foundation Language Models
With Apple Intelligence, we're integrating powerful generative AI right into the apps and experiences people use every day, all while…
machinelearning.apple.com
New Apple #ML Research Highlight: "An LLM-Based Approach to Review Summarization on the App Store" - details the multi-step #LLM workflow for generating high quality summaries from diverse crowdsourced reviews in a dynamic environment: machinelearning.apple.com/research/app...
An LLM-Based Approach to Review Summarization on the App Store
Ratings and reviews are an invaluable resource for users exploring an app on the App Store, providing insights into how others have…
machinelearning.apple.com
New post: "Apple Machine Learning Research at #ICLR 2025" - highlighting a selection of the many Apple #ML research papers to be presented at @iclr-conf.bsky.social this week: machinelearning.apple.com/research/icl...
Apple Machine Learning Research at ICLR 2025
Apple researchers are advancing machine learning (ML) and AI through fundamental research that improves the world’s understanding of this…
machinelearning.apple.com
New Apple #ML Research Highlight: "Controlling Language and Diffusion Models by Transporting Activations” machinelearning.apple.com/research/tra...
Controlling Language and Diffusion Models by Transporting Activations
Large generative models are becoming increasingly capable and more widely deployed to power production applications, but getting these…
machinelearning.apple.com
Modern science wouldn’t exist without the online research repository known as arXiv. Three decades in, its creator still can’t let it go.
Inside arXiv—the Most Transformative Platform in All of Science
Modern science wouldn’t exist without the online research repository known as arXiv. Three decades in, its creator still can’t let it go.
wrd.cm
Very cool work from an international team of researchers including @www.helmholtz-munich.de and Apple: applying optimal transport to give scientists a new ability to observe millions of cells simultaneously, as they develop across time and in space
Nature@nature.com · 2y agoNature research paper: Mapping cells through time and space with moscot https://go.nature.com/40pqpvw
Today is a great day for optimal transport 🎉! Lots of gratitude 🙏 for all folks who contributed to ott-jax.readthedocs.io and pushed for the MOSCOT (now @ nature!) paper, from visionaries @dominik1klein.bsky.social, G. Palla, Z. Piran to the magician, Michal Klein! ❤️ www.nature.com/articles/s41...
Mapping cells through time and space with moscot - Nature
Moscot is an optimal transport approach that overcomes current limitations of similar methods to enable multimodal, scalable and consistent single-cell analyses of datasets across spatial and temporal...
nature.com
Helmholtz Munich@helmholtzmunich.bsky.social · 2y agoAI in Cell Research: Unveiling Organ Development #Moscot, a groundbreaking #AI tool developed by an international team led by #HelmholtzMunich, tracks cells in real time, revealing how organs like the #pancreas form. t1p.de/viil5 @fabiantheis.bsky.social @nature.com
New #Apple Machine Learning research highlight: “Accelerating LLM Inference on #NVIDIA GPUs with ReDrafter” - a story of impactful, production-relevant #ML research: machinelearning.apple.com/research/red... 1/5
Accelerating LLM Inference on NVIDIA GPUs with ReDrafter
Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and…
machinelearning.apple.com
Apple Machine Learning Research at NeurIPS 2024 machinelearning.apple.com/research/neu...
Apple Machine Learning Research at NeurIPS 2024
Apple researchers are advancing the field of ML through fundamental research that improves the world’s understanding of this technology and…
machinelearning.apple.com
New post: Apple Machine Learning Research at #NeurIPS2024 - highlighting a handful of the many papers #Apple’s #ML researchers will present next week, across a variety of topics: machinelearning.apple.com/research/neu... 1/3
Apple Machine Learning Research at NeurIPS 2024
Apple researchers are advancing the field of ML through fundamental research that improves the world’s understanding of this technology and…
machinelearning.apple.com
The multimodality space is now evolving in a much better way. The focus has shifted to finding the bottlenecks and fixing things on the fundamental level. This paper from **Apple** introduces **AIMv2**, and effort is in a similar direction, except that they only do it for the autoregressive models.
New from Apple ML researchers: AIMv2 - scalable and open autoregressive vision encoders: machinelearning.apple.com/research/mul...
