Nyla Worker: prediction

Transcript context

…So I actually got to meet NVIDIA while I was working at eBay and they just went me over to their solutions architect program, which is. A place where you get to see all of the customers that NVIDIA had, uh, for artificial intelligence and you support them. So within that time, I started as a, in a rotational program where I supported retail customers, edge AI customers, retail customers, all trying to leverage AI in some kind of way. So for example, for retail, it was use cases like Amazon Go or retail theft protection Edge AI, it was robotics, manufacturing, deploying on the floors, uh, for autonomous vehicles, it was deploying in the vehicles, good computer vision networks, um, and things like that. So that was my first two years and it was hundreds of customers that were trying to leverage primarily computer vision. Some, uh, large language models, but the technology wasn't there yet. Primarily they were using it for recommender systems or search, but on the computer vision side, we saw that. And then I decided to join like the Edge AI team where I worked with customers such as Siemens and other big corporations and got to see how they were deploying this in like the manufacturing lines. Other items like that. However, one of my problems with every single customer was their data. They could use off the shelf models, right? There were ginormous image data sets and so on, but they didn't fit this particular niche use case. So for example, you have scratches in your cars in the manufacturing line. That is inspected manually. And it's a very long and arduous task to find all of those scratches. Right. And that dataset does not exist. And it was every time in retail, we didn't have enough data for like the items on the shelf or in retail. There is also high churn of packaging. So the packaging that was there like six months ago is changing this month. So because of that, there was always a deep need for data. So I started working on. Generating synthetic data that would immediately and automatically support that. So for example, I worked with Amazon in this project where we replaced tape synthetically in a 3d world. And that only was a big issue for Amazon because They needed to very quickly retrain those computer vision networks to detect packages that had a new Amazon tape. Yeah, and that was just the starting point. It grew to like robotics. So I worked with Festa on a 3D manipulator that needed to detect the pose of the object. And how do you get pose data? The way that people were doing it was by putting tags, like literally QR codes, onto the item such that they had some ground truth and then they would label it. e object. And how do you get pose data? The way that people were doing it was by putting tags, like literally QR codes, onto the item such that they had some ground truth and then they would label it. But that's impossible, like this is the case where synthetic data really becomes important because there is no way you're going to get the pose of the item in every single position. And on top of that, you're disturbing the item, right? In the real world, it would never have like a QR tag on it. So that is where I saw all of these things that needed synthetic data. And I worked with incredible researchers such as Jonatan Trembley that did a lot of research on like these 3D and synthetic data generation use cases. I like to think about it as we hit a data wall, like there was no way that we could progress with the existing data. And now what do you do? And I think we're going to see similar things with LLMs. We're going to hit a data wall. And then what do you do? And obviously there is synthetic data generation for LLMs too, but we'll see how it all comes together. And one of my realizations in the process of productizing synthetic data is that Training with synthetic data is an art, it's a skill on its own. How do you effectively generate, for example, do domain randomization on the items that you are generating in the 3D world. To effectively train networks is a complete art of its own. But yeah, so that, that goes, that glues it all together. Yeah, that's great. Um, and I think maybe as you think about LLMs, what we thought about optimizing before with Chinchilla and some of those scaling laws was finding the right middle ground that doesn't really optimize for anything. And now it's like, okay, we're just focusing on optimizing inference. And we're doing all this work at the, you know, algorithm layer, so to speak, or even at the GPU layer, you know, with some of the new math and like the metrics multiplication things with cutlass and the likes, but data, we haven't quite gotten to the point where we need to generate a ton of synthetic data versus it seems like in more robotics and kind of like 3d environments. There's really not that much. Synthetic data. So is most of the work there still getting more like, we haven't really seen, you know, Sora was maybe like the most impressive, kind of like somewhat 3d related thing, you know, it's not, I guess it's not really 3d because the output is flat, but it has its own kind of like 3d engine that it runs any thoughts on. Maybe what you've seen in synthetic data in 3d and how you think how far we are in the LLM side, like how soon we're going to need to really scale synthetic data to make some of these models like break the next barrier of performance. And also, yeah, thoughts on Sora. I don't know if you have any, I know the model is very private and, you know, not a lot of people have hands on experience on it.…

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