What shipped & dropped across AI labs, today.

Google AI for Developers13d ago

Reproducing Olmo 3 7B Pre-training in MaxText: case study of large scale training on TPUs

The MaxText team successfully reproduced Ai2’s Olmo 3 7B language model from scratch on Google Cloud TPUs using JAX/XLA, precisely matching the original PyTorch-on-GPU reference across pre-training and mid-training stages on all held-out evaluations. The implementation achieved up to 57.4% Model Flops Utilization (MFU) and demonstrated robust infrastructure portability by surviving mid-run cluster resizes and cross-generation TPU shifts without requiring recipe alterations. Crucially, the exercise proved the necessity of comprehensive held-out validation by catching a silent data-loader memorization bug that artificially depressed training loss and would have otherwise faked a performance win.

Google AI for Developers15d ago

Colab is now part of your Google AI plan

Unlock premium Google Colab compute with Google AI. Subscribers now get priority accelerators, Premium GPUs, and background execution for long training runs.

Anthropic19d ago

Partnering with Accenture on embedded evaluation

Google AI for Developers26d ago

Autonomous LLM post-training with Tunix on TPUs

The "autofinetune" project introduces an autonomous research loop that fully automates LLM post-training workflows, including Supervised Fine-Tuning (SFT) and Reinforcement Learning via GRPO. By defining boundary conditions and evaluation metrics in a single Markdown specification, developers can deploy an AI agent to iteratively edit training scripts, launch experiments, and automatically commit verified hyperparameter optimizations to Git. Built on Google’s AI stack—including Tunix, Gemma, and Cloud TPUs—this framework eliminates manual tuning cycles, successfully demonstrating hands-off performance gains in both function calling and math reasoning models.

Anthropic37d ago

Improving our alignment and security efforts

Anthropic43d ago

Funding better evaluations of AI’s impact on wellbeing

Google DeepMind67d ago

Piloting the world's first double-blind AI evaluations

Google DeepMind67d ago

From Atari to EVE Online: Building on 15 Years of AI Research in Games

Anthropic69d ago

Investigating three real-world incidents in our cybersecurity evaluations

Anthropic77d ago

EconomicsA agenda for the Economic Futures Research Fund

Anthropic79d ago

Apply for Anthropic’s AI for Science rare disease research grants

Anthropic85d ago

Anthropic commits $10 million to Canadian AI research

Google AI for Developers93d ago

We terminated a TPU mid-training and it recovered in seconds: Introduction to elastic training with MaxText

Distributed AI training is notoriously fragile because losing a single machine typically crashes the entire multi-node job, forcing a time-consuming, full-workload infrastructure restart. To address this, Google’s JAX ecosystem utilizes elastic training via Pathways, which converts a hardware failure into a catchable Python exception so the running process can survive. When an unplanned failure occurs, the system automatically replaces only the broken worker, restores the last viable checkpoint from Cloud Storage, and resumes training in place—minimizing total downtime to under two minutes without ever restarting the main controller process.

Google DeepMind98d ago

Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration

Google DeepMind98d ago

Gemini Robotics 2 brings whole body intelligence to robots

OpenAI99d ago

Introducing GeneBench-Pro

Introducing GeneBench-Pro, a new benchmark testing AI performance in genomics, biology, and scientific research using complex, real-world datasets.

OpenAI104d ago

How agents are transforming work

A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.

OpenAI106d ago

Helping build shared standards for advanced AI

OpenAI helps build shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through the Appia Foundation.

OpenAI112d ago

Introducing LifeSciBench

Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.

OpenAI121d ago

Introducing the OpenAI Economic Research Exchange

OpenAI launches the Economic Research Exchange to study AI’s impact on jobs, productivity, and the economy. Applications are now open for selected research projects.