ML and AI with Sherol Chen

ML and AI with Sherol Chen

On the show today, we speak with Developer Advocate and fellow Googler, Sherol Chen about machine learning and AI. Jon Foust and Aja Hammerly learn about the history and impact of AI and ML on technology and gaming. What does it mean to be human? What can machines do better than humans, and what can humans do better than machines? These are the large questions that we aim to solve in order to understand and use AI. Sherol goes on to explain the types of deep learning machines can achieve, from neural networks to decision trees.

Sherol also went into depth about the potential social impact of AI as it assists doctors parsing through medical records and plans agricultural endeavors to maximize food production and safety. Sherol also elaborates on the ethical responsibilities we must realize when developing AI projects.

For developers looking to build a new AI project, Sherol outlines the pros and cons of using existing tools like Cloud Speech-to-Text, AutoML and AutoML Tables.

Sherol Chen

Sherol advocates for Machine Learning for Google Cloud, and works in Research at Google Brain for Machine Learning in Music and Creativity for the Magenta team. She's taught Artificial Intelligence at Stanford and around the world in six different countries. Her PhD work is in Computer Science, researching storytelling and Artificial Intelligence at the Expressive Intelligence Studio.

Cool things of the week
  • AMD EPYC processors come to Google—and to Google Cloud blog
  • Kaggle Petfinder Dataset site
  • Streaming data from Cloud Storage into BigQuery using Cloud Functions blog
  • App Engine Standard Ruby site
Interview Question of the week

You mentioned that you can run App Engine + Rails, how do you handle migrations?

Where can you find us next?

Jon will be at PAX Dev and PAX West, the internal game summit at Google in Sunnyvale, and taking some personal time to travel to Montreal.

Aja will be hanging around at home, on the internet, and at Seattle.rb.

Sound Effect Attribution

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