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Speaker
Rachel Thomas
Co-founder, fast.ai
Director, USF Center for Applied Data Ethics
Bio
Rachel Thomas is director of the USF Center for Applied Data Ethics and co-founder of fast.ai, where she helped create the most popular free online course on deep learning, bringing people from around the world with diverse and nontraditional backgrounds into AI. Rachel earned her PhD in mathematics at Duke University and previously worked as a data scientist and software engineer. She was selected by Forbes as one of 20 Incredible Women in AI and was profiled in the book Women Tech Founders on the Rise. She wrote chapters for the books 97 Things About Ethics Everyone in Data Science Should Know and Deep Learning for Coders with fastai and PyTorch.
Rachel’s writing has been read by nearly a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 9x. Some of her most popular articles include:
- The problem with metrics is a big problem for AI
- If you think women in tech is just a pipeline issue, you haven’t been paying attention
- The real reason women quit tech, and how to address it
- Google’s AutoML: Cutting Through the Hype
- An Introduction to Deep Learning for Tabular Data
Rachel’s talks include:
- AI, Medicine, and Bias: Diversifying Your Dataset is Not Enough (Stanford AI in Medicine & Imaging Symposium)
- Getting Specific About Algorithmic Bias (featured talk at PyBay)
- The Barriers to AI are Lower than You Think (MIT Technology Review conference)
- The New Era in NLP (keynote at SciPy)
- Some Healthy Principles About Ethics & Bias In AI (keynote at PyBay)
fast.ai: The Why, How, and Future of Democratizing ML
March 26, 2021
5:30 PM - 6:00 PM (30 minutes) - Coordinated Universal Time
For the first time ever, Jeremy Howard and Rachel Thomas, co-founders of fast.ai, sit down for a fireside chat explaining why they started fast.ai, how it progressed from classes to a software platform, the importance of community, and where they see the future direction of fast.ai. They also dive into the importance of first-principles problem solving and creativity, versus relying on throwing as much compute at the problem as possible.