In this special #TWIMLfest episode of the podcast, we’re joined by Jeremy Howard, Founder of Fast.ai.
In our conversation with Jeremy, we discuss his career path, including his journey through the consulting world and how those experiences led him down the path to ML education, his thoughts on the current state of the machine learning adoption cycle, and if we’re at maximum capacity for deep learning use and capability.
Of course, we dig into the newest version of the fast.ai framework and course, the reception of Jeremy’s book ‘Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD,’ and what’s missing from the machine learning education landscape. If you’ve missed our previous conversations with Jeremy, I encourage you to check them out here and here.
The complete show notes for this episode can be found at https://twimlai.com/go/421.
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Modeling Human Cognition with RNNs and Curriculum Learning, w/ Kanaka Rajan - #524
Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523
Delivering Neural Speech Services at Scale with Li Jiang - #522
AI’s Legal and Ethical Implications with Sandra Wachter - #521
Compositional ML and the Future of Software Development with Dillon Erb - #520
Generating SQL Database Queries from Natural Language with Yanshuai Cao - #519
Social Commonsense Reasoning with Yejin Choi - #518
Deep Reinforcement Learning for Game Testing at EA with Konrad Tollmar - #517
Exploring AI 2041 with Kai-Fu Lee - #516
Advancing Robotic Brains and Bodies with Daniela Rus - #515
Neural Synthesis of Binaural Speech From Mono Audio with Alexander Richard - #514
Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513
Adaptivity in Machine Learning with Samory Kpotufe - #512
A Social Scientist’s Perspective on AI with Eric Rice - #511
Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510
Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509
Spatiotemporal Data Analysis with Rose Yu - #508
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