Google’s PaLM-2 with Paige Bailey

In this episode, Nathan sits down with Paige Bailey, Lead Product Manager of Generative Models at Google Deepmind. In this conversation, they discuss what it's like to be a PM for an LLM as opposed to an app, defining ideal LLM behaviour, and reasoning - how do you distinguish real abilities vs pattern matching?

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TIMESTAMPS:
(00:00) Episode Preview
(00:01:15) Introducing Paige Bailey
(00:04:21) Paige’s background at Google Brain and the Deepmind merger
(00:07:00) PM for a LLM vs being a PM for an app
(00:11:21) The development timeline and compute budget of PaLM-2
(00:14:30) Paige’s role in the PaLM 2 project
(00:15:30) Sponsors: Netsuite | Omneky
(00:17:26) Defining desired capabilities for PaLM-2
(00:19:17) The amount of work that went into elevating PaLM 2 from PaLM 1
(00:20:28) Has Google lost its ability to ship?
(00:24:240) Paige's "eureka" moment seeing GitHub Copilot capabilities
(00:27:47) Competing PaLM 2 with other models
(00:32:20) Grokking and the predictability of emergent capabilities
(00:37:30) Citizen scientists and the multilingual capabilities of PaLM 2
(00:39:29) Distinguishing real reasoning vs pattern matching
(00:45:51) Products using PaLM-2 that people should try
(00:50:35) Most exciting AI projects that you can try out
(00:52:29) Curriculum learning and successor to the transformer

LINKS:
PaLM 2: g.co/palm2
Duet AI for developers: https://cloud.google.com/blog/products/application-development/introducing-duet-ai-for-developers
Avenging Polayni’s Revenge: https://www.youtube.com/watch?v=BmyB-4S9QuY

X/TWITTER:
@DynamicWebPaige (Paige)
@labenz (Nathan)
@eriktorenberg
@CogRev_Podcast


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Music Credit: GoogleLM
Music license:
HD0JLKNORIFN9NJX