The version numbers are a little confusing and deserve some explanation. Internally, we are working on version 9 of our new foundation model, which is 1.5T params. This is substantially better in every way than v8: data curation, training recipe, size, etc. It is also optimized
RESEARCH
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Solving Differential Equations With Neural Networks
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Solving Differential Equations With Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/DE-NNs -
AGI ALPHA: public implementation layer for AI self-improvement
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AI self-improvement is now a multi‑billion‑dollar category. AGI ALPHA is building the public implementation layer: proof‑bound agents, SecureRails, Open RSI Eval, Evidence Dockets & replayable enterprise machine labor. https://
github.com/MontrealAI/agi
alpha-first-real-loop
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Understanding Recursive Self-Improvement in AI
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If you want to learn how recursive self-improvement works, I break it all down in 14 minutes here: https://
youtu.be/ky1TkRWA65M?si
=pf9w9LQyzXv4VXOe
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LLMs Detect Cancer Early in Health Records
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We could pick up risk of cancer and detect it much earlier if we applied LLMs to electronic health records, as shown in this @AllofUsResearch study
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Grok V9 1.5T training run shows promising results
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Our recently completed Grok V9 1.5T run is looking great and that is before Cursor data is added in supplemental training
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Spontaneous symmetry breaking and Goldstone modes in deep information propagation
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Spontaneous symmetry breaking and Goldstone modes for deep information propagation Iqbal et al.: https://
arxiv.org/abs/2605.14685 #ArtificialIntelligence #DeepLearning #AIAgents -
Research on deep information propagation and symmetry breaking in neural networks
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Spontaneous symmetry breaking and Goldstone modes for deep information propagation Iqbal et al.: https://
arxiv.org/abs/2605.14685 #ArtificialIntelligence #DeepLearning #AIAgents -
Sparse Convolutional Autoencoders for Fine-Tuning
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It does work. With stacks of pre-trained sparse convolutional autoencoders, we can achieve a strong starting point for fine-tuning on small labeled datasets (like Caltech 101, which had 30 training samples per category) and reach near-state-of-the-art performance.
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AI as Scientist: Self-Driving Labs Accelerate Discovery
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AI Is Becoming A Scientist: How Self-Driving Labs Will Accelerate Discovery #AI is moving beyond assisting #scientists and taking an active role in #discovery, with self-driving labs that can design #experiments, run tests and learn from results. This article explores how