The unreasonable effectiveness of synthetic data with Daeil Kim

The hard part is diversifying the content. So if we just have the same character in an environment doing everything, it's not going to work, right? So how do you actually create hundreds or thousands of variations of that character model with different behavior and things like that? That's been really the core focus of how we're thinking about our technology. You're listening to Gradient Dissent, a show where we learn about making machine learning models work in the real world. Daeil Kim is the co-founder and CEO of AI.Reverie. A startup that specializes in creating high quality synthetic training data for computer vision algorithms. Before that he was a senior data scientist at the New York Times. And before that he got his PhD in computer science from Brown university, focusing on machine learning and Bayesian statistics. He's going to talk about tools that will advance machine learning progress, and he's going to talk about synthetic data. I'm super excited for this. I was looking at your LinkedIn and you have a little bit of an unusual path, right? You did a liberal arts undergrad. Can you say a little bit about... I feel like I come across people quite a lot that want to make career transitions into machine learning and related fields. What was that for you? What prompted you to do it?