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Why AI Models Fail: The Critical Need for Real Human Data Over Synthetic Training Sets
Manage episode 449396125 series 2911874
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TARTLE's Alexander McCaig and Jason Rigby discuss why AI companies are struggling with model accuracy and how real-time human feedback (RTHF) provides the solution to synthetic data limitations. Key insights include why AI models collapse at 50% synthetic data usage, the importance of demographic diversity in training sets, and how TARTLE's zero-party data approach ensures both compliance and quality across global markets.
The episode explores:
- Why public and synthetic data lead to increasing error rates
- The competitive advantage of real human behavioral data
- How TARTLE's API enables 45-minute integration for accelerated AI development
- Global compliance across GDPR, CCPA, and HIPAA through zero-party data
- The methodology for reducing algorithmic bias through diverse demographic sampling
TCAST: A Tech and Data Podcast
Hosted by: Alexander McCaig and Jason Rigby
About:
TCAST is a tech and data podcast exploring the most exciting trends in Big Data, Artificial Intelligence, and Humanity. Join Alexander and Jason as they fearlessly examine the latest developments in digital transformation and innovation.
Each episode features insightful interviews with data scientists, thought leaders, and industry pioneers who are shaping the skills and technologies we need for human progress.
Explore TCAST:
Tune in on your favorite podcast platform and explore our extensive selection of episodes at your own pace.
Connect with TCAST:
- Website: https://tartle.co/
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- Twitter: https://go.tartle.co/tweet
What's your data worth? Find out at https://tartle.co/
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