The One About Physical World Models (ft. Causal Labs & Ropedia)
What would it take for AI to understand the real world, not just describe it? Come explore the rise of physical world models: AI systems that can learn how environments behave, anticipate what happens next, and eventually support action in complex real-world settings.
More About The Sharings
Fangzhou Hong (Co-founder & CTO, Ropedia) will be sharing on "Building the Data Layer for World Models and Embodied AI"
Getting AI to understand and act in the physical world starts with a fundamental challenge: how do you capture, structure, and transform real-world human experience into machine-usable representations? Fangzhou will provide an overview of embodied AI and explore how large-scale multimodal experience data can be collected and structured for physical AI systems. Hear more about the challenges of real-world data collection, multimodal synchronisation, and action representation, and the role these datasets play in enabling world models, physical reasoning, and embodied intelligence. (Technical Level: 100 - 200)
Dar Mehta (CEO & Co-Founder, Causal Labs) will be sharing on "Predict, Act and Control with Large Physics Models"
Correlation has taken AI far. Causality is what takes it further. Dar will explore why causality is a missing key for AI tackling civilisation's most ambitious unsolved problems and share the technical blueprint behind Causal Labs' bet on a new pre-training paradigm grounded in physics rather than language. He'll discuss how they are training a large physics foundation model (LPM) to learn causality, and the case for weather as the proving ground of choice for AI that learns physics (Technical Level: 200)
*Do note that program might be subject to changes.
More About The Speakers
Fangzhou Hong is Co-founder and CTO of Ropedia, dedicating in building the encyclopedia of human experience for embodied AI — the data infrastructure layer that turns real-world experience into machine intelligence. He led the creation of Xperience-10m, Ropedia's flagship multimodal dataset with over 2.7M downloads on Hugging Face, ranking Top 3 on the platform's weekly trending list. Fangzhou holds a Ph.D. from NTU (MMLab@NTU / S-Lab) and a B.Eng. from Tsinghua University, and is a Google Ph.D. Fellow and China3DV Rising Star. He previously worked with Meta Reality Labs Research.
Dar Mehta is the CEO & Co-Founder of Causal Labs, a Series A research lab training a large physics foundation model to learn causality, by predicting and controlling the weather. Previously, Dar and his co-founder, Kelsie Zhao, pioneered technical breakthroughs at Waymo & Cruise for autonomous vehicles that now enable 100K+ safety-critical rides every week in the U.S. Dar was a department technical lead for Perception and Technical Safety at Cruise (General Motors), built 3D perception models at Waymo (Google), did RL research at Google Brain/DeepMind robotics, and founded a YC-backed robotics startup out of the University of Waterloo, where he studied robotics.
Causal Labs is a team of researchers and engineers from self-driving, drug discovery, and robotics - including Google DeepMind, Cruise, and Waymo. We're a Series A startup backed by Lachy Groom, Stefano Ermon, Jonathan Frankle, Kyle Vogt, and Nal Kalchbrenner.
More About The Series
AI Wednesdays is Lorong AI’s weekly gathering, bringing together practitioners, researchers and innovators for technical discussions on research insights, product development and engineering practices.
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