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
What did you think? Let us know.
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