The Train That Taught Itself to Drive: AI Takes Control of High-Speed Rail Power Systems
A single AI agent trained via deep reinforcement learning outperformed carefully tuned traditional controllers on high-speed train rectifiers, reducing voltage
Read the full articleYou might also wanna read
Revamping AI Learning: Why New Metrics Matter
Traditional reinforcement learning for AI falls short on complex tasks. New methods focus on trajectory awareness to boost performance.

The Paradox of Limitations: How Constraints Drive Innovation in AI-Driven Energy Systems
AI offers a world of potential for optimizing power generation and delivery even as it demands in unprecedented amounts.
Using Curriculum Learning and PufferLib to Train Superhuman AI Agents for 2048 and Tetris
Training gaming agents is an addictive game. A game of sleepless nights, grinds, explorations, sweeps, and prayers. PufferLib allows anyone
Transforming Multi-Robot Control with Modular AI
A transformer-based architecture shows promise in improving control across diverse robot morphologies. The innovation lies in using modular

The AI Surge Driving Transportation's Future
AI is revolutionizing transportation, integrating deeply into industry models. The question isn't if it will change the game but how fast.
How tuning the harness around an open model can match frontier AI performance at a fraction of the cost
We tuned an Nemotron 3 Ultra's harness to match Opus 4.8's best agent run at ~8x lower cost, changing only the scaffolding around it.

Comments
Sign in to join the conversation.
No comments yet. Be the first.