Jessica Forde, Yuvi Panda and Chris Holdgraf join Melanie and Mark to discuss Project Jupyter from it’s interactive notebook origin story to the various open source modular projects it’s grown into supporting data research and applications. We dive specifically into JupyterHub using Kubernetes to enable a multi-user server. We also talk about Binder, an interactive development environment that makes work easily reproducible.
Bryan Catanzaro, the VP Applied Deep Learning Research at NVIDIA, joins Mark and Melanie this week to discuss how his team uses applied deep learning to make NVIDIA products and processes better. We talk about parallel processing and compute with GPUs as well as his team’s research in graphics, text and audio to change how these forms of communication are created and rendered by using deep learning.
This week we are also joined by a special co-host, Sherol Chen who is a developer advocate on GCP and machine learning researcher on Magenta at Google. Listen at the end of the podcast where Mark and Sherol chat about all things GDC.
Dr. Fei-Fei Li, the Chief Scientist of AI/ML at Google joins Melanie and Mark this week to talk about how Google enables businesses to solve critical problems through AI solutions. We talk about the work she is doing at Google to help reduce AI barriers to entry for enterprise, her research with Stanford combining AI and health care, where AI research is going, and her efforts to overcome one of the key challenges in AI by driving for more diversity in the field.
In this episode, Google Play Marketing is the customer of Google Cloud Platform. Melanie and Mark chat with Dom Elliott (Google Play) and Stewart Bryson (Red Pill Analytics) about how they use our big data processing and visualisation tools to introspect what is happening in the Google play ecosystem.
This week, we dive into machine learning bias and fairness from a social and technical perspective with machine learning research scientists Timnit Gebru from Microsoft and Margaret Mitchell (aka Meg, aka M.) from Google.
They share with Melanie and Mark about ongoing efforts and resources to address bias and fairness including diversifying datasets, applying algorithmic techniques and expanding research team expertise and perspectives. There is not a simple solution to the challenge, and they give insights on what work in the broader community is in progress and where it is going.
Yifei Feng talks with Mark and Melanie about working on the open source TensorFlow platform, the recent 1.5 release, and how her team engages and supports the growing community. She provides a great overview of what its like to work on an open source project and ways to get involved especially for anyone new to contributing.