In this episode, Matt Paige and Rowan Stone, CEO of Sapien, discuss the critical importance of data quality and provenance in AI.

Stone, who has experience with on-chain products at Coinbase, introduces Sapien's innovative approach to building a decentralized data protocol that emphasizes 'don't trust, verify' principles.

They explore avenues such as incentives, validation methods, and the peer review process used by Sapien to create high-quality datasets.

The discussion touches on the implications of bad data, the role of synthetic data, the complexities of achieving accurate AI outputs, and the parallels between the AI and crypto worlds.

Key insights are shared on how to ensure models perform safely, the hurdles in the industry, and the trajectory of AI development.

Additionally, Stone provides a glimpse into Sapien’s efforts to demystify data validation and enhance the transparency and trustworthiness of AI applications.

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Key Moments:

01:04 The Importance of Data Quality in AI
03:32 Challenges and Risks in AI Development
07:08 Sapien's Approach to Data Validation
08:35 Incentives and Trust in AI Systems
13:30 Building a Decentralized Data Protocol
23:22 Consensus and Collaboration in AI and Crypto
30:55 The Role of Synthetic Data
36:17 Future of AI Models and Open Source

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Key Links:

Mentioned in this episode:

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Don’t Trust—Verify: Building a Proof of Quality for AI Data

Talking AI