Deepak Pathak

So if you think about robotics, right, it's one of the oldest areas in technology.

Like in AI, AI was built for robotics.

We have seen amazing demos in robotics for the last 70 years, not four or five years, 70 years.

But there are robots around us.

And this is what people often get wrong.

When you think of robots in your mind, you imagine hardware.

Like that comes to your mind first because that's how Hollywood has primed us.

But the main thing behind not having robots around us today is the brain is missing.

And if you have the brain for robots, you can really enable them around in a variety of areas, variety of applications.

And that's what exactly SCALE is doing.

We are building what we call omnibodied brain.

Any robot, any task, one brain.

I'm interested in how you're building it.

The limitation that a lot of people are talking about right now is real-world data and training.

There are different approaches.

Digital twins trying to simulate physics in digital worlds.

This is very much what NVIDIA is focused on, right?

Is that the approach you've taken, simulated or synthetic data?

So I think I will draw analogy to LLMs, like ChatGPT-like models, like if you see open-ahead ChatGPT.

And soon after, many companies had their own LLM models.