Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.
Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.
It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.
If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
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Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.
Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.
It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.
If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
Spencer Huang: NVIDIA’s Big Plan for Physical AI: Simulation, World Models, and the 3 Computers
Inference by Turing Post
28 minutes
1 month ago
Spencer Huang: NVIDIA’s Big Plan for Physical AI: Simulation, World Models, and the 3 Computers
When robots move into the real world, speed and safety come from simulation!
In his first sit-down interview, Spencer Huang – NVIDIA’s product lead for robotics software – talks about his role at NVIDIA, a flat organization where “you have access to everything.” We discuss how open source shapes NVIDIA’s robotics ecosystem, how robots learn physics through simulation, and why neural simulators and world models may evolve alongside conventional physics. I also ask him what’s harder: working on robotics or being Jensen Huang’s son.
Watch to learn a lot about robotics, NVIDIA, and its big plans ahead. It was a real pleasure chatting with Spencer.
*We cover:*
- NVIDIA’s big picture
- The “three computers” of robotics – training, simulation, deployment
- Isaac Lab, Arena, and the path to policy evaluation at scale
- Physics engines, interop, and why OpenUSD can unify fragmented toolchains
- Neural simulators vs conventional simulators – a data flywheel, not a rivalry
- Safety as an architecture problem – graceful failure and functional safety
- Synthetic data for manipulation – soft bodies, contact forces, distributional realism
- Why the biggest bottleneck is robotics data, and how open ecosystems help reach baseline
- NVIDIA’s “Mission is Boss” culture – cross-pollinating research into robotics
This is a ground-level look at how robots learn to handle the messy world – and why simulation needs both fidelity and diversity to produce robust skills.
*Chapters*:
0:22 The future of Physical AI begins here
1:00 Inside NVIDIA’s secret blueprint for teaching robots
3:46 Why safety is the hardest part of robotics
4:11 Simulation: the new classroom for machines
8:55 Can robots really understand physics?
13:55 How NVIDIA builds robot brains without a PhD
16:47 The plan to unify a fragmented robotics world
20:31 Why open source is NVIDIA’s biggest power move
21:21 What’s harder – robotics or being Jensen Huang’s son?
24:31 The one thing holding robotics back
27:56 The sci-fi books that shaped Spencer's mind
*Did you like the episode? You know the drill:*
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*Guest:* Spencer Huang, NVIDIA – a product line manager at NVIDIA leading robotics software product. His work centers on open-source simulation frameworks for robot learning, synthetic data generation methodologies, and advancing robot autonomy – from industrial mobile manipulators to generalist humanoid robots.
https://www.linkedin.com/in/spencermhuang/
*📰 Want the transcript and edited version?*
Find it here: https://www.turingpost.com/spencer
*Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live.
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#robotics #simulation #NVIDIA #Omniverse #digitaltwins #worldmodels #physicalAI #reinforcementlearning #syntheticdata
Inference by Turing Post
Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.
Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.
It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.
If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
