How to Build AI Responsibly | David Adkins Meta AI

David Adkins is an experienced senior technology executive who leads engineering teams at Meta AI. David holds an MS in Computer Science from the University at Buffalo focused on machine learning. In this conversation we discuss

* How to think about AI bias and fairness

* Why AI Transparency, Explainability and Control are important

* How Meta de-risked LLaMA

* How to take AI Research to production and more.

Listen now on Apple, Spotify and Amazon Music and please subscribe to my YouTube channel.

⏰ 2:06 David’s Favorite Restaurant Sammy’s Fishbox

⏰ 2:40 Career Stories: David’s Journey

⏰ 4:37 What David is working on at Meta AI

⏰ 4:50 How to organize AI Teams

⏰ 6:56 What is AI Bias and Fairness

⏰ 8:52 How to build a Product with AI Fairness in mind

⏰ 11:47 Examples of Fairness Mitigations in Meta Products

⏰ 13:33 What’s Surprising about working on AI Fairness

⏰ 16:05 What causes Bias in AI Products

⏰ 19:19 How to Mitigate AI Problems when you’ve already launched

⏰ 21:38 What is AI Transparency and Control

⏰ 24:12 What is AI Explainability?

⏰ 26:10 Why YOU should care about AI Transparency

⏰ 31:00 What is surprising about working on AI Transparency

⏰ 33:00 AI System Cards

⏰ 37:30 Developing LLaMA Responsibly

⏰ 39:30 How to de-risk large language models

⏰ 43:06 How to do AI Research to production

⏰ 46:51 What’s next for David

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