In this episode, Nathan sits down with Vivek Natarajan and Tao Tu of Google’s Med-PaLM, diving into how they used one of the world’s largest medical datasets ever compiled to develop Med-PaLM M, an AI agent specialized in medical tasks. In this episode, they discuss: Med-PaLM M's “clinically superhuman” abilities and limitations, the rigorous testing and validation that went into the model, and their vision for AI to take over repetitive clerical tasks and allow doctors to focus on patients.

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TIMESTAMPS:

(00:00 ) Episode Preview

(00:00:56 ) Introducing Vivek Natarajan and Tao Tu

(00:04:18 ) The story of Google’s Medical AI research progress

(00:07:11 ) Multi-modal Med-PaLM

(00:10:32 ) Genomic data - how do you represent it?

(00:11:13 ) Google’s Deep Variant

(00:14:44 ) The successes and failures behind the incredible pace of progress

(00:15:02 ) Sponsors: Netsuite | Omneky

(00:21:54 ) Google’s research culture and assembling an interdisciplinary team

(00:31:36 ) Google’s Pathways

(00:33:40 ) Med-PaLM M's architecture

(00:37:28 ) Working with 3 different model sizes and what you learn

(00:46:56 ) Data and compute required for Med-PaLM M

(00:49:38 ) Med-PaLM M's cycle time

(00:54:56 ) Is a bridge or adapter structure worth implementing?

(01:00:09 ) Can we create an AI doctor?

(01:02:39 ) Emergent capabilities like identifying tuberculosis

(01:09:37 ) Reactions to these emergent capabilities

(01:11:13 ) Moving towards clinical trials and real-world testing

(01:13:01 ) Regulatory and safety considerations

(01:15:03 ) AI safety in the healthcare domain

(01:17:00 ) Potential to transform healthcare access worldwide

LINKS:

Med-PaLM: https://sites.research.google/med-palm/

Med-PaLM M paper: https://arxiv.org/abs/2307.14334

Our earlier conversation with Vivek Natarajan on Med-PaLM: https://www.youtube.com/watch?v=nPBd7i5tnEE

X/TWITTER:

@vivnat (Vivek)

@taotu831 (Tao)

@labenz (Nathan)

@eriktorenberg

@CogRev_Podcast

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