Sonia has been a visionary in laying the foundations of multimodal interpretability and directing scientific attention toward it through various initiatives, at a time when much of the community was focused primarily on probing language. Her ideas about interpreting alien modalities sent me down a rabbit hole and have played a major role in shaping my own views, and how I work. Excited to see World Mechanics shaping the mechanics of the world! 💥
Sonia Joseph (@soniajoseph_)
Earlier this month, I left Meta to build a frontier neolab for the physical world, focused on interpretable foundation models and scientific simulation. We’ve been assembling a world-class founding team of researchers and engineers from leading frontier AI and academic labs.
Meta was the most formative two years of my career so far. I had the privilege of building and leading the company’s internal interpretability community while also conducting research on the JEPA team. The interpretability community was a unique place within the AI industry because it offered a snapshot of the full interpretability life cycle: from mathematical insights from the neuroscientists, to mechanistic work on frontier models, to integrating those ideas into ad recommenders. Meanwhile, on the JEPA team, we published the first mechanistic interpretability work on physical reasoning in video world models. I loved everyone I worked with and will miss you all.
Those two experiences shaped our conviction that interpretability and the physical world belong together. Interpretability won’t merely help us debug physical foundation models. More deeply, it becomes part of how we train, verify, and trust them throughout the entire model lifecycle. Alongside this, we’re developing new approaches that treat foundation models as learned latent space scientific simulators capable of advancing problems in climate science, astrophysics, supply-chains, power grids, and other physical systems.
We intentionally founded the company as a Public Benefit Corporation. Physical AI demands a higher standard of safety and verification because failures have real-world consequences. Malfunctioning manufacturing pipelines are expensive, and no one wants hallucinating robots in their home. We’re building on a decade of progress in interpretability and AI safety while extending those ideas into physical reasoning, robotics, and scientific simulation.
We also believe the next era of physical AI will be by default an unusually closed ecosystem because of the strategic value of physical-world data. We’re pushing in the opposite direction where we can: supporting open research, strengthening ties with academia, investing in scientific literacy, and building a broader ecosystem that keeps the public connected to how this technology evolves.
The bar for joining the founding team is very high (for researchers, multiple first-author conference papers, and for engineers, experience at a tier 1 startup). We’re also investing in the broader Ecosystem: developing mentorship for later-stage PhD students working on video, world-model, and physical-world interpretability, collaborating closely with researchers across academia and other companies, and researching in the open where possible.
We’re currently in stealth. If you’re interested in advancing the frontier of interpretable physical AI and latent space scientific simulation, we’d love to hear from you. More coming soon.
— https://nitter.net/soniajoseph_/status/2082184426784805359#m