The Reasoning Show

The Reasoning Show AI moves fast. Thinking clearly matters more.

The Reasoning Show cuts through the hype to explore how the smartest people in enterprise AI actually make decisions — the strategy, the tradeoffs, and the hard lessons no press release mentions.

Every week, hosts Aaron Delp and Brian Gracely sit down with the founders building the tools, investors funding the shift, and operators running AI in the real world. Not hype. Not panic. Just clear-headed conversations with people who have to make actual decisions.

Because the AI revolution isn't just happening. It's being reasoned through.

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The Reasoning Show

Synthetic Data for AI

April 17, 2024 • Massive Studios

0:00 | 25:58

Kalyan Veeramachaneni (@kveeramac, CEO/Founder @DataCebo) discusses the generation and value proposition of synthetic data for GenAI.

SHOW: 813

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SHOW NOTES:

Topic 1 - Our topic for today is synthetic data. While the concept and need for synthetic data has been around for a long time, it isn’t a topic that typically comes to the forefront and something we haven’t talked about until today. Today is a bit of crossing the streams between developers and testing data and using GenAI to achieve this goal. For this, we’re joined by Kalyan, CEO and Co-Founder of DataCebo. Welcome to the show

Topic 2 - First, for those not familiar, what is synthetic data? What is the use case and need? What problem is it solving today?

Topic 2a - Hopefully, listeners out there are making the connection to the advantages of GenAI for synthetic data, but take us through your original concept at MIT and the history of Synthetic Data Vault (SDV).

Topic 3 - We recently did a show on the security and privacy of training LLMs where we covered the need to mask PII for the training of models for compliance. I can also see bias issues coming into play or maybe training data that doesn’t exist in the real world (weather models example). What are some of the use cases that you’ve seen require synthetic data sets. Are there certain industries (healthcare, financials, etc.) that benefit?

Topic 4 - You were designing this based on GenAI before GenAI was “cool”. How has the rise of LLMs impacted this space?

Topic 5 - If I understand this correctly, organizations would put generative AI on a problem to describe a need for a data set, the model would then evaluate the available data and create a quality synthetic or “fake” dataset. How would the organization verify the quality of the dataset? How would they validate that a synthetic data set is as good as the original data?

Topic 6 - Let’s talk about resources for a bit. When I think of GenAI and training, I think of large amounts of hardware and in particular GPU’s that might have limited availability. Is that true here? Also, is this on-prem or in the cloud, or both?

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