Ruchir puri

@pulkitology @willknight @WIRED Congratulations on the awesome tech and the launch @pulkitology ! Have been a robotics enthusiast since childhood - looking forward to your baby “Eka robotics” to scale new heights! 👏

Thanks @willknight and @WIRED for covering us: wired.com/story/when-rob…

Eka means unity -- “one,” in Sanskrit and “first” in Finnish. We’re building intelligence for the physical world in its native language: forces. Until now, robotics faced a tradeoff — generality or speed. The real world requires both. Robotics also faced a data problem. Our Vision–Force–Action (VFA) model — the first of its kind — breaks the generality-speed tradeoff and the data barrier. It's a new foundation uniting performance, generality, and safety for putting capable robots in everyone's hands. Today, I am excited to share our journey of pushing robots beyond human limits. Today, dexterity becomes scalable. Today, I welcome you to the Era of Eka. Co-founded with @haarnoja, and so thrilled and grateful to be working with a dream team at @EkaRobotics. Learn more: ekarobotics.com

@annadgoldie Way to go! Congratulations @annadgoldie and @Azaliamirh 🎉

Thrilled to announce our $300M Series A at a $4B valuation! Chips are the fuel for AI. At Ricursive Intelligence, we are using AI to design better chips faster, closing the recursive self-improving loop between AI and hardware.

@annadgoldie @Azaliamirh @RicursiveAI Way to go @annadgoldie and @Azaliamirh !!!

Excited to announce that @Azaliamirh and I are launching @RicursiveAI, a frontier AI lab creating a recursive self-improving loop between AI and the hardware that fuels it. Today, chip design takes 2-3 years and requires thousands of human experts. We will reduce that to weeks. This will be incredibly hard. For context: Go has a search space of 10^360. A simplified version of chip placement—only one part of the design process—has a search space of 10^9000. But we are the right team to solve it. We co-founded the Machine Learning for Systems team at Google Brain. There, we built AlphaChip—an RL agent for chip placement. AlphaChip has been used to design four generations of TPUs, data center CPUs, autonomous vehicle chips, and mobile phone chips. These chips are running in data centers and devices all over the world. Our immediate goal is to dramatically accelerate chip design. Next, we plan to design chips end-to-end given an ML workload, unlocking a Cambrian explosion of custom silicon. Finally, we will close the recursive loop. We will build our own chips, train our own models, and co-evolve them on the path to superintelligence. AI designs better chips 🔄chips train better AI We sat down with @WSJ’s @Berber_Jin1 to discuss Ricursive: wsj.com/tech/this-ai-s…

our new work w awesome @RedHat collaborators on novel inference scaling techniques - check out bit.ly/3CHs1Zz for more on how to scale small LMs to o1 performance! 🚀🚀🚀

Can we use classical probabilistic inference methods to scale small LMs to o1 level? 🤔 @MIT_CSAIL and Red Hat AI Innovation teams explore: bit.ly/3CHs1Zz

.@RedHat AI Innovation team just dropped a new research paper on inference-time scaling! 🚨 All built on @vllm_project. Paper and code here: …abilistic-inference-scaling.github.io Cheers to paper authors @variational_i, @xukai92, @GX_NLP, Shivchander Sudalairaj, and @ishapuri101!

[1/x] can we scale small, open LMs to o1 level? Using classical probabilistic inference methods, YES! Joint @MIT_CSAIL / @RedHat AI Innovation Team work introduces a particle filtering approach to scaling inference w/o any training! check out …abilistic-inference-scaling.github.io

🚀 Exciting News! 🚀 In a joint effort between IBM Research, Princeton, CMU, and UIUC, we are thrilled to announce the release of our high-performing hybrid Mamba2 model! This model is trained entirely on open datasets, and we’re releasing intermediate and final checkpoints to enable community experimentation. 🔗 Read more: huggingface.co/blog/bamba Key Takeaways ⚡ Inference Efficiency The Bamba-9B model delivers significant improvements in throughput and latency, enhancing real-time application performance. Benchmarking with vLLM against Llama 3.1 8B for long contexts shows: 🔹 2.5x throughput improvement 🔹 2x lower latency And this is just the beginning – further optimizations are on the way! 🏆 Competitive Benchmarks Bamba-9B performs competitively with state-of-the-art transformer models like Meta Llama 3.1 8B. It matches average benchmark performance (excluding math and MMLU tasks), with clear opportunities to close gaps through extended training and math-focused datasets. 🤝 Open Collaboration Developed entirely with open data, this effort emphasizes transparency and reproducibility, strengthening the foundations of the open-source AI community. 📂 For details, access to the model, and resources, check out the Bamba GitHub repository: github.com/foundation-mod… Let’s collaborate, experiment, and innovate together! 🔍✨ @tri_dao @_albertgu @MinjiaZhang -- it is a great collaboration and look forward to continuing to work with you.