We Are Witnessing the Self-Immolation of a Superpower

"Accessing and remediating any of these issues can only be performed in the VAB."

Researchers at Google DeepMind developed GraphCast, a graph neural network that predicts global weather up to 10 days ahead at 0.25° resolution in under one minute. Trained on ERA5, it outperforms European Centre for Medium-Range Weather Forecasts’s HRES system on 90% of 1,380 targets. GraphCast also improves forecasts of hurricanes, atmospheric rivers, and extreme heat, marking a major leap for AI-driven weather prediction.

"It also takes a lot of energy to train a human."

Researchers at DeepMind built Gato, a 1.2B-parameter transformer trained on 604 tasks spanning Atari games, image captioning, dialogue, and real-world robotics. Using one shared set of weights, Gato processes text, images, and actions as tokens in a single sequence model. It achieves strong multi-task performance—including real robot block stacking—showing that one generalist AI can operate across vastly different domains.

Researchers at Google introduce a method to distill complex machine learning models into concise, human-readable piecewise-linear curves. Using their open-source pwlfit library, they convert decision forest and additive models into transparent source code without sacrificing accuracy. Tested on datasets like COMPAS, FICO, MSLR-WEB30K, and Chrome Web Store data, the approach matches black-box performance while improving interpretability, auditability, and deployment simplicity.

AutoDev is a Microsoft-built AI framework that turns GPT-4 into an autonomous software engineer. Unlike tools like GitHub Copilot, it doesn’t just suggest code — it edits files, runs builds, executes tests, retrieves context, and fixes its own bugs inside a secure Docker environment. On the HumanEval benchmark, it achieved 91.5% Pass@1 for code generation and 87.8% for test generation, rivaling top research systems without extra training.