Go offline with the Player FM app!
Synthetic Data in AI
Manage episode 373554692 series 3475282
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.
Good AI Needs Great GovernanceDefine, manage, and automate your AI model governance lifecycle from policy to proof.
Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.
Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:
- LinkedIn - Episode summaries, shares of cited articles, and more.
- YouTube - Was it something that we said? Good. Share your favorite quotes.
- Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.
21 episodes
Manage episode 373554692 series 3475282
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.
Good AI Needs Great GovernanceDefine, manage, and automate your AI model governance lifecycle from policy to proof.
Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.
Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:
- LinkedIn - Episode summaries, shares of cited articles, and more.
- YouTube - Was it something that we said? Good. Share your favorite quotes.
- Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.
21 episodes
All episodes
×Welcome to Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.