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Paige Bailey: Pioneering Generative AI in Product Management at Google DeepMindby@whatsai
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Paige Bailey: Pioneering Generative AI in Product Management at Google DeepMind

by Louis BouchardNovember 17th, 2023
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Paige Bailey is a trailblazer in AI product management and a visionary. She's been at the helm of transformative projects at Google DeepMind and GitHub. Paige shares her insights on the current state and the potential of generative AI. We touch upon Google’s PaLM 2 and its implications for the future of machine learning.
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Today, I received none other than Paige Bailey on my podcast, a trailblazer in AI product management and a visionary who’s been at the helm of transformative projects at Google DeepMind and GitHub , working on the most advanced LLM projects.


In our discussion, Paige shares her insights on the current state and the potential of generative AI. We touch upon Google’s PaLM 2 and its implications for the future of machine learning.


Here are some topics and insights we touched on in the discussion:


  • Paige emphasizes the transformative power of technology to make experiences once exclusive to the ultra-wealthy accessible to all, such as the services provided by Uber and Instacart.
  • Discusses the challenges and advancements in AI-assisted coding and the role of GitHub Copilot in shaping developer experiences.
  • Highlights the importance of careful product design around large language models to reduce errors and enhance performance, citing Google's approach with models like Bard.
  • Underscores the need for responsible AI development, focusing on safety checks and ethical considerations in technology deployment.
  • Paige points out the revolutionary impact of foundational models in inspiring businesses to embed research teams for innovation.
  • Notes the potential risks and benefits of using AI in educational settings, particularly regarding the reliance on AI for problem-solving in academic learning.
  • She also provides insight into the role of a product manager within AI research teams, focusing on prioritizing use cases and data for model training.


If these topics sound interesting, join us in this discussion and listen on Spotify, Apple podcast or now: