Ali Golshan, Co-founder and CEO of Gretel.ai, dives into the fascinating world of synthetic data and its pivotal role in advancing AI applications. He discusses how synthetic data can enhance privacy while improving the quality and structural stability of datasets. The conversation highlights the shift from traditional data methods to the use of language models and the challenges of scaling synthetic data in production. Ali also explores its transformative applications in sectors like healthcare and finance, underscoring the importance of governance and ethical considerations.
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insights INSIGHT
Synthetic Data for AI
Synthetic data is purpose-built for AI, focusing on quality, privacy, and structural stability.
It addresses bottlenecks of using sensitive data or lacking specific data for model training.
insights INSIGHT
LLMs Revolutionize Synthetic Data
Pre-LLM synthetic data generation used GANs and statistical models, focusing on data shape and distribution.
LLMs understand deep structural stability, enabling what-if scenarios and augmenting real data.
insights INSIGHT
Purpose of Synthetic Data
Differential privacy with synthetic data teaches models about insights, not specific individuals (e.g., diseases, not patients).
Synthetic data allows training smaller, specialized models, improving efficiency and cost.
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Summary In this episode of the AI Engineering Podcast Ali Golshan, co-founder and CEO of Gretel.ai, talks about the transformative role of synthetic data in AI systems. Ali explains how synthetic data can be purpose-built for AI use cases, emphasizing privacy, quality, and structural stability. He highlights the shift from traditional methods to using language models, which offer enhanced capabilities in understanding data's deep structure and generating high-quality datasets. The conversation explores the challenges and techniques of integrating synthetic data into AI systems, particularly in production environments, and concludes with insights into the future of synthetic data, including its application in various industries, the importance of privacy regulations, and the ongoing evolution of AI systems.
Announcements
Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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Your host is Tobias Macey and today I'm interviewing Ali Golshan about the role of synthetic data in building, scaling, and improving AI systems
Interview
Introduction
How did you get involved in machine learning?
Can you start by summarizing what you mean by synthetic data in the context of this conversation?
How have the capabilities around the generation and integration of synthetic data changed across the pre- and post-LLM timelines?
What are the motivating factors that would lead a team or organization to invest in synthetic data generation capacity?
What are the main methods used for generation of synthetic data sets?
How does that differ across open-source and commercial offerings?
From a surface level it seems like synthetic data generation is a straight-forward exercise that can be owned by an engineering team. What are the main "gotchas" that crop up as you move along the adoption curve?
What are the scaling characteristics of synthetic data generation as you go from prototype to production scale?
domains/data types that are inappropriate for synthetic use cases (e.g. scientific or educational content)
managing appropriate distribution of values in the generation process
Beyond just producing large volumes of semi-random data (structured or otherwise), what are the other processes involved in the workflow of synthetic data and its integration into the different systems that consume it?
What are the most interesting, innovative, or unexpected ways that you have seen synthetic data generation used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on synthetic data generation?
When is synthetic data the wrong choice?
What do you have planned for the future of synthetic data capabilities at Gretel?
From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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