Chang Shi

🔥

Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇

Advanced Machine Intelligence (AMI) is building a new breed of AI systems that understand the world, have persistent memory, can reason and plan, and are controllable and safe. We’ve raised a $1.03B (~€890M) round from global investors who believe in our vision of universally intelligent systems centered on world models. This round is co-led by Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions, along with other investors and angels across the world. We are a growing team of researchers and builders, operating in Paris, New York, Montreal and Singapore from day one. Read more: amilabs.xyz AMI - Real world. Real intelligence.

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)

I've been working on a new LLM inference algorithm. It's called Speculative Speculative Decoding (SSD) and it's up to 2x faster than the strongest inference engines in the world. Collab w/ @tri_dao @avnermay. Details in thread.

Check out our work leveraging Entity-Centric Diffusion for Hierarchical RL🔥

“Video WMs are learnable physics simulators.” Can’t agree no more.

I am at #NeurIPS25 from Dec 2nd to 7th. I will present our work at the embodied-world-models.github.io workshop! Would love to catch up with old friends and connect with new friends on robot learning, world models, RL, VLM/VLA … and more! I am looking for 2026 internships and full time soon. Feel free to DM me and let’s chat!

As a robotics researcher, I believe accurately modeling complex interactions between agents would be a big step for scaling up robot learning from unlabeled video. Looking forward to some inspiring discussion with the Cohere Labs Embodied AI community!

⚠️ Reminder! Submissions for @RL_Conference's RL beyond Reward Workshop are due May 30 (AoE)! We are brewing an interesting program and seeking innovative research work in reward-free RL. All papers are welcome, from exploratory abstracts to complete research papers.

@harshit_sikchi @scottniekum @yayitsamyzhang @marcgbellemare @yukez @PeterStone_TX Big congrats Harshit! 🎉

Successfully defended my Ph.D. today 🎓🥳! @scottniekum and @yayitsamyzhang are the best advisors I could have ever asked for. A big thanks to my committee members @marcgbellemare @yukez @PeterStone_TX . The full presentation video will be uploaded soon... Excited about what's to come!

@bauzavillalonga Super interested! Applied!

I'm hosting a student researcher at Google DeepMind in 2024! If you or some you know is interested in robotic manipulation, multi-modal learning, and want to work at London GDM then apply by Dec 15 (note it is tight!). Link: shorturl.at/fivV6 and lmk if you applied!