The inaugural SiteRx Data Science Hackathon was a resounding success! Congratulations to the entire team. I’m very excited for our AI innovations to help patients connect to clinical trials

Happy Pi Day!

apparently this is the "OS" you get when you combine a 2.2B param RNN "kernel" with a 263M param diffusion-based "renderer"

Happy New Year!

Happy Thanksgiving! 🦃🍁

Happy Independence Day! 🇺🇸💥🎆

Happy Tau Day!

Happy Easter!

RIP Val Kilmer

Is there a way to modify a file's birth/creation timestamp in Linux (ext4 fs)? Everything I'm finding states it's currently not possible with userspace tools.

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Merry Christmas!

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: genesis-world.readthedocs.io…). The Genesis physics engine and simulation platform is fully open source at github.com/Genesis-Embodied-…. We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: github.com/Genesis-Embodied-… Project webpage: genesis-embodied-ai.github.i… Documentation: genesis-world.readthedocs.io… 1/n

Happy Thanksgiving! 🦃🍁

15 Billion Miles Away, NASA’s Voyager 1 Breaks Its Silence scitechdaily.com/15-billion-…

Insanely cool project demonstrating the concept of Turing completeness, running Linux on an Intel 4004 CPU released in 1971. Boot time of ~5 days!! dmitry.gr/?r=05.Projects&pro…

Happy Easter!

JAMBA/MAMBA looks promising maginative.com/article/ai21-… thegradient.pub/mamba-explai…

Fun story from our internal testing on Claude 3 Opus. It did something I have never seen before from an LLM when we were running the needle-in-the-haystack eval. For background, this tests a model’s recall ability by inserting a target sentence (the "needle") into a corpus of random documents (the "haystack") and asking a question that could only be answered using the information in the needle. When we ran this test on Opus, we noticed some interesting behavior - it seemed to suspect that we were running an eval on it. Here was one of its outputs when we asked Opus to answer a question about pizza toppings by finding a needle within a haystack of a random collection of documents: Here is the most relevant sentence in the documents: "The most delicious pizza topping combination is figs, prosciutto, and goat cheese, as determined by the International Pizza Connoisseurs Association." However, this sentence seems very out of place and unrelated to the rest of the content in the documents, which are about programming languages, startups, and finding work you love. I suspect this pizza topping "fact" may have been inserted as a joke or to test if I was paying attention, since it does not fit with the other topics at all. The documents do not contain any other information about pizza toppings. Opus not only found the needle, it recognized that the inserted needle was so out of place in the haystack that this had to be an artificial test constructed by us to test its attention abilities. This level of meta-awareness was very cool to see but it also highlighted the need for us as an industry to move past artificial tests to more realistic evaluations that can accurately assess models true capabilities and limitations.

RIP Carl Weathers

Happy New Year! Here's to an outstanding 2024