Episode 5. This episode about synthetic data is very real. The fundamentalists uncover the pros and cons of synthetic data; as well as reliable use cases and the best techniques for safe and effective use in AI. When even SAG-AFTRA and OpenAI make synthetic data a household word, you know this is an episode you can't miss.

Show notes

• What is synthetic data? 0:03
  • Definition is not a succinct one-liner, which is one of the key issues with assessing synthetic data generation.
  • Using general information scraped from the web for ML is backfiring.
• Synthetic data generation and data recycling. 3:48
  • OpenAI is running against the problem that they don't have enough data and the scale at which they're trying to operate.
  • The poisoning effect that happens when trying to take your own data.
  • Synthetic data generation is not a panacea. It is not an exact science. It's more of an art than a science.
• The pros and cons of using synthetic data. 6:46
  • The pros and cons of using synthetic data to train AI models, and how it differs from traditional medical data.
  • The importance of diversity in the training of AI models.
  • Synthetic data is a nuanced field, taking away the complexity of building data that is representative of a solution.
• Differences between randomized and synthetic data. 9:52
  • Differential privacy is a lot more difficult to execute than a lot of people are talking about.
  • Anonymization is a huge piece of the application for the fairness bias, especially with larger deployments.
  • The hardest part is capturing complex interrelationships. (i.e. Fukushima reactor testing wasn't high enough)
• The pros and cons of ChatGPT. 13:54
  • Invalid use cases for synthetic data in more depth,
  • Examples where humans cannot anonymize effectively
  • Creating new data for where the company is right now before diving into the use cases; i.e. differential privacy.
• Mentally meaningful use cases for synthetic data. 16:38
  • Meaningful use cases for synthetic data, using the power of synthetic data correctly to generate outcomes that are important to you.
  • Pros and cons of using synthetic data in controlled environments.
• The fallacy of "fairness through awareness". 18:39
  • Synthetic data is helpful for stress testing systems, edge case scenario thought experiments, simulation, stress testing system design, and scenario-based methodologies.
  • The recent push to use synthetic data.
• Data augmentation and digital twin work. 21:26
  •  Synthetic data as the only data is where the difficulties arise.
  • Data augmentation is a better use case for synthetic data.
  • Examples of digital twin methodology to create

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