Gradient Dissent: Conversations on AI

Daeil Kim — The Unreasonable Effectiveness of Synthetic Data


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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
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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!
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Gradient Dissent: Conversations on AIBy Lukas Biewald

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