"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

Synthetic Data with Alex Watson, Founder of Gretel AI

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Nov 14, 2023

In this discussion, Alex Watson, the Founder and CPO of Gretel AI, delves into the transformative world of synthetic data. He outlines how synthetic datasets address real-world limitations in machine learning, particularly in sensitive sectors like healthcare and finance. Alex introduces Gretel’s innovative TabLM, enhancing natural language queries for tabular data. The conversation also touches on the ethical implications of AI and the need for regulatory frameworks to ensure privacy. It's an insightful exploration of how synthetic data can revolutionize AI training and deployment.

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ANECDOTE

Data Access Challenges at AWS

Alex Watson, prior to Gretel AI, co-founded Harvest AI, a security startup acquired by AWS.
At AWS, he observed difficulties in internal data access, even for large data-driven companies.

ANECDOTE

Synthetic Data for Rare Disease Detection

Gretel AI partnered with UCI researchers on a rare disease dataset.
They used synthetic data to boost the minority class and improve disease detection.

INSIGHT

Trustworthy Synthetic Data Generation

Gretel AI trains models to recreate real-world data distributions.
This allows for altering distributions for specific tasks and ensures trustworthiness.

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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.

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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