François Chollet says autoregressive sampling alone cannot solve real-world physical tasks, because the physical world punishes hallucination and approximate pattern matching. He says autonomous robotics requires engineering causality and discrete reasoning back into the loop, and praises Aether AI's Biwei and team for this work.
You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomous robotics we have to engineer causality and discrete reasoning back into the loop.
Biwei and the team at Aether AI is doing exactly the kind of foundational, non-consensus work the field actually needs right now.
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting. Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task. When we started Aether AI, robotics was the obvious place to begin. CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room. Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more. The technical write-up is linked in the comments. https://x.com/AetherLab_AI/status/2108263657251418596/video/1
Source: François Chollet · x.comPublished · added here
