Supercharging computer vision model performance by generating years of training data in minutes. Daeil Kim is the co-founder and CEO of AI.Reverie(https://aireverie.com/), 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. https://twitter.com/daeil Topics covered: 0:00 Diversifying content 0:23 Intro+bio 1:00 From liberal arts to synthetic data 8:48 What is synthetic data? 11:24 Real world examples of synthetic data 16:16 Understanding performance gains using synthetic data 21:32 The future of Synthetic data and AI.Reverie 23:21 The composition of people at AI.reverie and ML 28:28 The evolution of ML tools and systems that Daeil uses 33:16 Most underrated aspect of ML and common misconceptions 34:42 Biggest challenge in making synthetic data work in the real world Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: bit.ly/2WdrUvI Spotify: bit.ly/2SqtadF Google:tiny.cc/GD_Google We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. app.wandb.ai/gallery