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.
Bringing you a special second episode this week with Matt Linton and Paul Turner sharing insights with Mark and Melanie about the CPU vulnerabilities, Spectre & Meltdown, and how Google coordinated and managed security with the broader community. We talked about how there has been minimal to no performance impact for GCP users and GCP’s Live Migration helped deploy patches and mitigations without requiring maintenance downtime.
Due to the special nature, no cool things or question included on this podcast.
Cloud AutoML is a suite of products enabling developers with limited ML expertise to build high quality models using transfer learning and Neural Architecture Search techniques. AutoML Vision is the first product out the gate with a focus on making it easy to train customized vision models.
Launchpad Studio, a product development acceleration program focused on helping machine learning startups iterate quickly, fail fast, and collaborate on best practices.
Malika Cantor and Peter Norvig talk with Mark and Melanie this week about how the Launchpad Studio program is helping startups overcome data, expertise and tooling barriers by providing access to talent and resources and building universal best practices.