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? 0:03Definition 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. 3:48OpenAI 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. 6:46The 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. 9:52Differential 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. 13:54Invalid use cases for synthetic data in more depth,Examples where humans cannot anonymize effectivelyCreating 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. 16:38Meaningful 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". 18:39Synthetic 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 a virtual twin of a physical system.How to get synthetic data through intelligently sampling the original dataset.The importance of knowing the history of data. 27:16Need to re-familiarize ourselves with these techniques in the context of the financial crisisOne of the key areas where synthetic data can be very powerful is when looking at past tabular data and the difference between use cases.Do you have a question or a discussion topic for the Fundamentalists? Let them know at [email protected]