AI For All Podcast
Synthetic Data for Machine Learning Models | Tonic.ai's Adam Kamor
Jul 27, 2023
Adam Kamor, Co-founder and Head of Engineering at Tonic.ai, discusses synthetic data for machine learning models. Topics include structured vs unstructured data, limits of synthetic data, use cases in different industries, data risks and privacy, prompt engineering, computer vision, and differential privacy.
28:46
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Quick takeaways
- Synthetic data can rebalance and improve the efficacy of machine learning models when the available data is insufficient or imbalanced.
- Synthetic data offers benefits such as data augmentation, privacy protection, and safe testing environments, but has limitations in capturing the complexity of real-world data and maintaining privacy.
Deep dives
Using AI to Generate Synthetic Data
AI can be used to generate synthetic data in order to rebalance consumer datasets and make them more reflective of the US population. This is particularly useful when the available data is insufficient or imbalanced, which can affect the efficacy of machine learning models. Tonic.ai is a company that specializes in generating fake data to address these challenges. By training a generative model on an existing dataset, synthetic data can be generated, including rare or minority cases. This synthetic data can be used to augment the original dataset and improve the classification scores of algorithms.
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