On this episode of the AI For All Podcast, Adam Kamor, co-founder and Head of Engineering at Tonic.ai, joins Ryan Chacon and Neil Sahota to discuss synthetic data for machine learning models. They talk about structured vs unstructured data, the limits of synthetic data, synthetic data examples and use cases, when not to use synthetic data, data risks and privacy, prompt engineering with synthetic data, industries using synthetic data, differential privacy, computer vision, and digital twins.
Adam Kamor, PhD, is Co-Founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of data generation solutions that are transforming the work of fellow developers, analysts, and data engineers alike.
Tonic.ai is the fake data company. They mimic your production data to create de-identified, realistic, and safe data for your test environments.
More about Tonic: https://www.tonic.ai
Connect with Adam: https://www.linkedin.com/in/adam-kamor-85720b48/
Key Questions and Topics from This Episode:
(00:00) Intro to the AI For All Podcast
(00:54) Intro to Adam Kamor and Tonic.ai
(01:11) What is synthetic data?
(03:45) Structured vs unstructured data
(06:54) Synthetic data examples
(09:58) Limits of synthetic data
(11:47) When not to use synthetic data
(13:05) Synthetic data use cases
(15:22) Potential of synthetic data
(17:39) Data risks and privacy
(19:30) Prompt engineering with synthetic data
(21:11) What industries use synthetic data?
(23:08) What is differential privacy?
(24:22) Synthetic data, computer vision, and digital twins
(27:13) Learn more about Tonic.ai
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