Asian governments react to Nvidia CEO Jensen Huang’s exit
Some governments moved to confirm chip supply commitments, while others are monitoring the impact on local projects or addressing ongoing investigations.
Data and training
All topicsThe training side: building datasets, synthetic data, pre- and post-training methods, compute and training cost.
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1–20 of 61Nov 15
Nov 14
Export-banned Nvidia chips are behind China’s most powerful AI models — uh-oh, Jensen!
New research shows the most advanced AI models from China were trained on banned Nvidia chips.
Former OpenAI researcher says he found Nvidia Blackwell fingerprints hidden inside six Chinese AI models
Former OpenAI researcher Will DePue says he has produced the first technical evidence that DeepSeek, Moonshot AI, MiniMax, and Ant Group are using Nvidia's latest Blackwell GPUs despite US export controls. His forensic method reads characteristic chip signatures directly from published model weights. He estimates the hardware behind the five training runs cost more than half a billion dollars.
Nov 13
LLMON: An LLM-Native Markup Language, now in Granite 4.1 3B and 8B
LLMs rely on their context to generate the next set of tokens. To address this, we developed LLMON (LLM Object Notation, pronounced “lemon”), an LLM-native markup language for explicitly communicating structure and semantics to the models.
Jensen Huang is out. What did China want from him?
Today, we’re talking about the Jensen Huang scandal at Nvidia, which is developing so quickly that we had to do an emergency update of this intro minutes before publishing this episode.
Nov 11
AlphaGenome 2: Deciphering how cells read DNA to better understand disease
Predicting how DNA sequences and their variations impact specific human cell types and disease.
Nov 6
[AINews] Inkling 2 Small: 17A-594B trained in 68 hours on 12T tokens, beating Muse Spark
Inside Inkling 2 Small’s training run and METR’s study of AI research at Anthropic.
New NVIDIA Cosmos 3 Open Models for Interactive World Simulation
NVIDIA Cosmos world model development platform adds four new open-weight checkpoints to its Cosmos 3 model family for generating interactive world simulation and multi-sensor multi-view driving scenes. All four models are post-trained examples of Cosmos 3 Nano and have 16B trainable parameters. The models use OpenMDW1.1, permitting commercial and non-commercial use under its terms.
Detecting signs of hypertension using wearable sensors
In a new study published in Nature, we present a multimodal artificial intelligence (AI) system capable of passively detecting signs of hypertension using a consumer smartwatch, without requiring any cuff calibration.
Nov 5
GPT-6 Astra sees the matrix and reprograms other AI models directly through their weights
MIT researchers show that OpenAI's GPT-6 Astra can read and deliberately rewrite the weights of other AI models without traditional training. In one experiment, Astra boosted a Qwen model's math accuracy from 7.9 to 48.1 percent by changing just 0.016 percent of its weights. The approach could fundamentally simplify AI optimization but still hits clear limits.
Nov 4
Agentic cheminformatics with Google Antigravity
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Oct 28
NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt
One open model for multilingual transcription, speaker attribution, translation, and audio Q&A, with top leaderboard results, plus the open Granary v2 data pipeline and fine-tuning recipes to adapt it.
Oct 23
Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox
This is the follow up to our first blog post, The Agent Said It Was Done. The Database Disagreed. ThinkingBox-Training is now available on GitHub . We built ThinkingBox to evaluate an agent's work by comparing the final database state with the expected outcome.
Oct 22
NVIDIA Unveils Minerva to Bring End-to-End Observability for AI Factories
Launched today, NVIDIA Minerva is an AIOps platform built to help teams find problems earlier and keep AI factories productive. It brings together telemetry from infrastructure and AI workloads, then applies purpose-built AI models to turn that data into timely, actionable insight.
NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World Models
To help developers rapidly build world models, which help physical AI systems understand and simulate the real world, NVIDIA has expanded the Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints.
NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required
NVIDIA Kumo Relational is a pre-trained relational foundation model that makes predictions directly on enterprise databases. Given a predictive query, it gathers up to 10,000 historical examples as context and predicts outcomes such as churn, demand, or fraud in a single forward pass. It needs no task-specific training, no feature engineering, and no flattening of tables.
Oct 21
NVIDIA Cosmos3-H-Dreams: Bringing Clinical Surgery to Life Through Generative Simulation
Today, we take the next step and introduce Cosmos3-H-Dreams, bringing interactive generative simulation to the visual and procedural complexity of clinical surgery with our most convincing in-body imagery to date. To our knowledge, this is the first real-time generative surgical simulator trained on clinical cholecystectomy recordings to support live kinematic control.
Oct 20
TorchNeuron: running HF models on AWS Trainium is easier than ever
TorchNeuron provides a new native backend for PyTorch on AWS Trainium. Transformers, Diffusers and Kernels natively support it, making it easier than ever to run models on Trainium and to share them on the HF hub. Starting Oct. 20, 2026, TorchNeuron will be available as a public beta.
Oct 17
Grounding AI agents in biological reality: Building blocks for autonomous wet labs
Frontier AI systems have the potential to transform biology research and drug discovery by intelligently navigating vast combinatorial search spaces (e.g., a single 100-amino-acid protein spans at least 20 100 possible sequences) calling for dynamic multi-candidate exploration.
Oct 15
Introducing Timestral
State-of-the-art probabilistic forecasting in four sizes (5M–209M params), CPU-efficient, zero-shot ready.