Is Class in Balance a Problem?

Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai.

In this conversation, we’ll be talking about the history of data science, machine learning, and AI, where we’ve come from and where we’re going, how new techniques can be applied to real-world problems, whether it be deep learning to medicine or porting techniques from computer vision to NLP. We’ll also talk about what’s present and what’s missing in the ML skills revolution, what software engineering skills data scientists need to learn, how to cope in a space of such fragmented tooling, and paths for emerging out of the shadow of FAANG. If that’s not enough, we’ll jump into how spreading DS skills around the globe involves serious investments in education, building software, communities, and research, along with diving into the social challenges that the information age and the AI revolution (so to speak) bring with it.

But to get to all of this, you’ll need to listen to a few minutes of us chatting about chocolate biscuits in Australia!

Links

• fast.ai · making neural nets uncool again
• nbdev: create delightful python projects using Jupyter Notebooks
• The fastai book, published as Jupyter Notebooks
• Deep Learning for Coders with fastai and PyTorch
• The wonderful and terrifying implications of computers that can learn -- Jeremy' awesome TED talk!
• Manna by Marshall Brain
• Ghost Work by Mary L. Gray and Siddharth Suri
• Uberland by Alex Rosenblat