Open models and the future of Physical AI with NVIDIA

SPEAKER_1 (0:02)

Welcome to the Practical AI Podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place.

Be sure to connect with us on LinkedIn, X, or Bluesky to stay up to date with episode drops, behind-the-scenes content, and AI insights. You can learn more at practicalai.fm. Now, on to the show.

Daniel Whitenack (0:41)

Welcome to another episode of the Practical AI podcast. This is Daniel Wightnack. I am CEO at Prediction Guard, and I'm joined as always by my co-host, Chris Benson, who is a principal AI and autonomy research engineer. How are you doing, Chris?

Chris Benson (0:56)

Hey, I'm doing great today, Daniel. Really excited about today's conversation.

Daniel Whitenack (1:00)

Yeah. Well, I think in the world that both of us inhabit, we are open to interesting discussions, and I think we'll talk about things open and things related to the world in today's episode because we have with us Ming-Yu Liu, who is vice president of Cosmos Lab at Nvidia. Welcome to the show, Ming-Yu.

Ming-Yu Liu (1:25)

Hello, it's good to have me here. Great to be here. Hi, Daniel and Chris.

Daniel Whitenack (1:30)

Yeah. Great to have you.

One of the things I alluded to is maybe something open, which I know is dear to Nvidia's heart, which is open models. I've seen in the news recently in what Nvidia has posted, obviously, they are promoting open models as a key piece of what is important in the AI ecosystem. I'm wondering, Ming-Yu, if you could help us understand, maybe from your perspective, from Nvidia's perspective, why in today's world is the idea of having open models and promoting open models? Why is that such a critical thing in the ecosystem?

Ming-Yu Liu (2:15)

Yes.

Okay. So when I was a student, I learned a lot about computer vision and machine learning. At that time, there are great researchers, students, open-sourced their code and model. And looking at their code and model actually helped me understand the concept and helped me to innovate.

And the Academy always worked like that. And it has been years, even before GBT, and GBT also had an open-source model in the past. And that has pushed the field forward, allowing innovation. It's just until recently, the model is getting bigger. The amount of resources required to build a model is a tremendous amount. And hence, the amount of open-model reduces. But open-model generally is there for the field.

And it has been very useful to push the field forward. And so, when this continues to happen, I think the world will be in a better place if we have an open-model to enable innovation.

So, API are great, they solve problems, but they don't give you the insight.

It doesn't allow you to tear apart and miss your idea inside. And also, the kind of thing you want to do may be suitable. You want to have a local computer and you don't have the internet connection. So, API may not always be the solution. And that's why I think open-model is important to enable people. And Nvidia is a serrated computing company. So, we build this computer, great computer, GPU, networking devices, and now we also build CPU, SoC. We build this computer and we want to enable the whole ecosystem. And we generally believe that to better build these computers, we need to know more about the applications, right? And in the old days, you have this computer and you have library and you have software applications, right? And in the modern world, you have computer and you have library still, but you now have models. Model is a new kind of libraries people can use and to build great, amazing applications. You know, maybe all kinds of agents to help you buy stuff or help you to code, all on top of models. And we, Nvidia, like to build models, like to build open models on top of Nvidia's computer.

There are a couple of reasons why we do that. First, we, through building a model, we better understand what kind of computer we should build, what kind of GPU architecture we should have, so that Nvidia can always be there to help the whole ecosystem. And second, we also want to measure how people are enabled to do amazing applications, right? So when we have an open model there, and people can test it out first, and combine them to do application good for their targeted business, and gain some familiarity, and maybe later they can build their own model. So there's a lot of reasons why open model is critical, both for Academy and also for the whole industry.