In this episode, Nathan interviews Alex Watson, founder and CPO of Gretel AI, about the company's work in synthetic data. They discuss why we need synthetic data, Gretel’s new pre trained tabular LLM that creates synthetic data on a zero shot basis, privacy techniques to prevent LLM memorization, and more. If you need an ecommerce platform, check out our sponsor Shopify: https://shopify.com/cognitive for a $1/month trial period.

We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.

SPONSORS:

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X/SOCIAL:

@labenz (Nathan)

@AlexWatson405 (Alex)

@Gretel_AI

@CogRev_Podcast

TIMESTAMPS:

(00:00:00) - Intro

(00:03:02) - Origins of the company name Gretel AI and initial vision around enabling data sharing while protecting privacy

(00:05:16) - Alex's background in data privacy and compliance from his previous startup Harvest AI, acquired by AWS

(00:07:24) - Using synthetic data to create additional examples and improve detection of rare disease

(00:12:50) - Why use synthetic data?

(00:17:02) - Sponsors: Shopify | Omneky

(00:19:00) - Training models to recreate real-world data distributions and using validators to detect unrealistic outputs

(00:24:40) - Pre-training the Gretel tabular LLM on diverse internet data sets

(00:30:40) - Sponsors: Oracle | Netsuite

(00:34:00) - Using an agent planning architecture

(00:39:41) - Example use case of adapting models with synthetic data samples for long-tail cases

(00:43:00) - Using reinforcement learning to intentionally generate more diverse and representative synthetic data

(00:51:16) - The potential of efficient, lightweight models compared to massive LLMs like GPT-4

(00:56:00) - Analogizing model specialization to specialized parts of the brain

(01:06:04) - Using differential privacy techniques to prevent memorization and exposure of private data

(01:14:37) - Adding noise during training to blur memorization

(01:18:42) - Optimism that synthetic data quality issues reflect details not fully understood yet vs inherent problems

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