Skip to main content
NVIDIA AI Podcast

AI, Spatial Intelligence, and 3D Content Creation With Sanja Fidler of NVIDIA - Ep. 269

39 min episode · 2 min read
·
Sanja Fidler

Episode

39 min

Read time

2 min

Topics

Artificial Intelligence, Science & Discovery

AI-Generated Summary

Key Takeaways

  • Simulation-Based Robot Training: Physical AI requires virtual playgrounds that accurately mimic real-world physics to train robots safely and cost-effectively, avoiding expensive real-world trial and error that would damage equipment and environments during the learning process.
  • Differentiable Rendering Pipeline: NVIDIA doubled down on making graphics pipelines compatible with AI by lifting images and videos to three-dimensional representations, enabling phone-based room scanning that instantly creates training environments in Isaac simulator for immediate robot deployment.
  • World Models Evolution: Video-based world models learn physics from real-world recordings without human editing, progressing from GAN-based systems like GameGAN and DriveGAN to latent diffusion models that now power physically accurate simulations at scale through platforms like Cosmos.
  • Visual Language Model Integration: VLMs represent the breakthrough for handling long-tail scenarios in robotics by bringing language-based reasoning into physical environments, allowing systems to navigate completely new situations never encountered during training through semantic understanding and reasoning capabilities.

What It Covers

Sanja Fidler, VP of AI Research at NVIDIA, explains spatial intelligence and physical AI development, covering how her Toronto lab creates three-dimensional world models, simulation platforms, and robotics training environments through Omniverse.

Key Questions Answered

  • Simulation-Based Robot Training: Physical AI requires virtual playgrounds that accurately mimic real-world physics to train robots safely and cost-effectively, avoiding expensive real-world trial and error that would damage equipment and environments during the learning process.
  • Differentiable Rendering Pipeline: NVIDIA doubled down on making graphics pipelines compatible with AI by lifting images and videos to three-dimensional representations, enabling phone-based room scanning that instantly creates training environments in Isaac simulator for immediate robot deployment.
  • World Models Evolution: Video-based world models learn physics from real-world recordings without human editing, progressing from GAN-based systems like GameGAN and DriveGAN to latent diffusion models that now power physically accurate simulations at scale through platforms like Cosmos.
  • Visual Language Model Integration: VLMs represent the breakthrough for handling long-tail scenarios in robotics by bringing language-based reasoning into physical environments, allowing systems to navigate completely new situations never encountered during training through semantic understanding and reasoning capabilities.

Notable Moment

Fidler traces her career path from childhood inventor dreams inspired by her father's scientist bedtime stories to overcoming fear of traveling alone after her grandmother, a pioneering female plastic surgeon, encouraged her to accept a Berkeley research position.

Know someone who'd find this useful?

Episode Transcript

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. This past August, three of NVIDIA's research leaders gave a special address at SIGGRAPH, the annual international computer graphics and interactive techniques conference that's been running since 1974. One of those people is here with us today. Sonya Fiddler is VP of AI research at NVIDIA, where she leads the NVIDIA Spatial Intelligence Lab in Toronto, Ontario, Canada. Sonya is here to tell us about the lab, to talk about the research she's most excited about right now, including what was presented at SIGGRAPH, and to share a little bit about her own journey through the worlds of research and artificial intelligence. So without further ado, let's get to it. Sonya Fidler, welcome, and thanks for joining the AI podcast. Hi, Noah, and hi, audience. I'm very excited to be on this AI podcast. We are very excited to have you. Thanks for taking the time. There's a lot going on, obviously, and congratulations on the special address and everything else at SIGGRAPH. So we wanted to start with a little bit about your own journey. You followed your passion for computer vision and artificial intelligence across Europe and into North America. Can you tell us a little bit about what first got you interested in the field and how your journey took you to Toronto? So, maybe I started with with my youth, and there were actually three important break points, that that led led me to where I am. And the first one actually starts with my dad. So my dad would sit on a chair next to my sister and me and tell us bedtime stories. And surprisingly, she was very good at it. He was a scientist, and so he would tell us stories about scientists. For example, he would tell us about Nikola Tesla, who was born in Croatia, and my mom was also born in Croatia. I was born in Slovenia. And was this in Slovenia? Where did you grow? I grew up in Slovenia. So my dad were in Slovenia. My dad was was my my mom was born in Croatia. My my dad was born in Slovenia, and I was born in Slovenia. Okay. So he was telling us stories about, you know, how at the young age, Nikola jumped from the roof from of their house holding an open umbrella thinking he would fly. You know? And every night, there will be a new episode about his inventions that led to, you know, creation of radio, alternating current. Obviously, we didn't understand what he made it sounds fun. And the competition with Thomas Edison, right, it will be almost like a Netflix series. And for a challenge, it was very exciting. So I just That's amazing. Couldn't have to wait to hear more to the next, next day. So kinda like my childhood with heroes were not, you know, movie stars or music stars. They were scientists. Awesome. …

Get the full transcript (7,045 words) + summary by email — free

One-time email with the complete transcript and AI summary of this episode. No account needed.

One email, no spam. We’ll also show you what SignalCast does.

Browse all NVIDIA AI Podcast transcripts →

You just read a 3-minute summary of a 36-minute episode.

Get NVIDIA AI Podcast summarized like this every Monday — plus up to 2 more podcasts, free.

Pick Your Podcasts — Free

Keep Reading

Books, tools, and gear mentioned in this episode

SignalCast may earn commission on purchases via these links.

Tools

  • by NVIDIA

    latent diffusion models that now power physically accurate simulations at scale through platforms like Cosmos
  • by NVIDIA

    her Toronto lab creates three-dimensional world models, simulation platforms, and robotics training environments through Omniverse
  • by NVIDIA

    lifting images and videos to three-dimensional representations, enabling phone-based room scanning that instantly creates training environments in Isaac simulator for immediate robot deployment

More from NVIDIA AI Podcast

We summarize every new episode. Want them in your inbox?

Similar Episodes

Related episodes from other podcasts

Explore Related Topics

This podcast is featured in Best AI Podcasts (2026) — ranked and reviewed with AI summaries.

Read this week's AI & Machine Learning Podcast Insights — cross-podcast analysis updated weekly.

You're clearly into NVIDIA AI Podcast.

Every Monday, we deliver AI summaries of the latest episodes from NVIDIA AI Podcast and 192+ other podcasts. Free for one show.

Start My Monday Digest

No credit card · Unsubscribe anytime