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Fei-Fei Li on what simulation can do for robotics that real-world data can't: "You play out events that haven't happened, or cannot happen... And while you play it out, you learn how to act in it." "That's really important in robotics, because we cannot possibly have enough real-world data... show more

a16z

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35,794 Aufrufe • vor 3 Monaten •via X (Twitter)

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9 Kommentare

Profilbild von Oleg Bilozor

Oleg Bilozorvor 3 Monaten

The thing that gets me is 'events that cannot happen.' You're not just filling data gaps, you're training on physics edge cases that would take decades to encounter in the real world, if they ever happened at all.

Profilbild von sindikitil

sindikitilvor 3 Monaten

Simulation lets you stress-edge cases that'd take decades to hit in meatspace. The long-tail distribution problem.

Profilbild von Winston B.

Winston B.vor 3 Monaten

Billions of hours of simulation only matter if they're the scenarios that almost never happen on real roads, the ones that decide whether a policy holds up under pressure. Same case for robotics generally. The data you need most is the data you'd otherwise wait years to collect naturally, so simulation ends up being the only practical way to get it.

Profilbild von 踏空 Sidelined Capital

踏空 Sidelined Capitalvor 3 Monaten

The expensive part is deciding which impossible events teach robustness and which teach the robot to exploit a simulator bug.

Profilbild von Bobby

Bobbyvor 3 Monaten

Semper Fidelis

Profilbild von Fred Almeida

Fred Almeidavor 3 Monaten

I was doing this in 2016. Nothing new. We were even coding at the kernal level to improve the number of simulations. in 2017 we were at 12x the speed of 2016. We thought everyone was doing this (imposter syndrome sucks) but they weren't. Today we can train in an hour (constrained RL + SP + CL) what would take 1000 years in real world data. Even at the edge of the technology. People still don't understand this. Almost everyone that thinks they understand AI but haven't worked in it for the last 15 years will have a hard time understanding the foundations.

Profilbild von Ted | unfair.so

Ted | unfair.sovor 3 Monaten

training robots by building worlds = minecraft

Profilbild von PromptKing | Evidence & Integrity for Agentic AI

PromptKing | Evidence & Integrity for Agentic AIvor 3 Monaten

The same logic applies to agent governance. You cannot wait for the model swap, the budget breach, or the unauthorized escalation to discover whether the boundary held. You have to play the trajectory out first — under the actual policy — before the work is allowed to run. That’s why simulate-before-enforce exists. The events that would break the proof are the ones you must never let happen for real. One outcome. Many models. One law. One proof.

Profilbild von Our Ascent Live

Our Ascent Livevor 3 Monaten

The future of robotics should not only be technically intelligent @drfeifei. It should be publicly shaped. Citizen-led. AI-enhanced. 📡🗽

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