Alex Watson, Founder and CPO of Gretel AI, discusses the need for synthetic data, privacy techniques, and Gretel's pre trained tabular LLM. The podcast explores the benefits of using synthetic data, challenges of training AI models, and the potential of synthetic data for AI training. They also touch on the importance of control over AI models and the challenges of training AI models for privacy protection.

Guest Alex Watson, Product Director at Spotify's Core Experience, discusses career progression in product management, including self-definition, managing team members, and balancing creative and managerial aspects. They explore challenges in prioritizing and articulating product decisions and the value of keeping track of achievements. They also touch on the 5050 rule for time management and effective calendar management.

There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, photorealistic AI-generated picture of a human face. As the website’s name suggests, there’s no human being on the face of the earth who looks quite like the person staring back at you on the page. Each of those generated pictures are a piece of data that captures so much of the essence of what it means to look like a human being. And yet they do so without telling you anything whatsoever about any particular person. In that sense, it’s fully anonymous human face data. That’s impressive enough, and it speaks to how far generative image models have come over the last decade. But what if we could do the same for any kind of data? What if I could generate an anonymized set of medical records or financial transaction data that captures all of the latent relationships buried  in a private dataset, without the risk of leaking sensitive information about real people? That’s the mission of Alex Watson, the Chief Product Officer and co-founder of Gretel AI, where he works on unlocking value hidden in sensitive datasets in ways that preserve privacy. What I realized talking to Alex was that synthetic data is about much more than ensuring privacy. As you’ll see over the course of the conversation, we may well be heading for a world where most data can benefit from augmentation via data synthesis — where synthetic data brings privacy value almost as a side-effect of enriching ground truth data with context imported from the wider world. Alex joined me to talk about data privacy, data synthesis, and what could be the very strange future of the data lifecycle on this episode of the TDS podcast. *** Intro music: - Artist: Ron Gelinas - Track Title: Daybreak Chill Blend (original mix) - Link to Track: https://youtu.be/d8Y2sKIgFWc *** Chapters: 2:40 What is synthetic data? 6:45 Large language models 11:30 Preventing data leakage 18:00 Generative versus downstream models 24:10 De-biasing and fairness 30:45 Using synthetic data 35:00 People consuming the data 41:00 Spotting correlations in the data 47:45 Generalization of different ML algorithms 51:15 Wrap-up

Highlights from this week’s conversation include:Alex’s background working for NSA and starting a company (1:51)The Gretel.ai journey (9:30)Defining synthetic data (13:26)The evolution of AI in deep learning data and language learning (16:28)The properties of synthetic data (21:31)Boundaries between synthetic data and prediction models (25:52)The developer experience in Gretel.ai (36:44)Stewardship and expansion of deep learning models in the future (45:36) Final thoughts and takeaways (52:17)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Alex Watson, Founder & CEO of OpenForests, discusses nature-based solutions and the integration of technology in forest projects. They explore challenges in forest investments, the interdisciplinary nature of forestry, the importance of geo transparency, and the creative problem-solving needed in starting a company. They also discuss the development of the Explored Land Platform, a map-based project presentation platform.