In this episode of the AI Agents Podcast, we explore how Sapien.io is transforming the way fine-tuned large language models (LLMs) are built.
Demetri Panici interviews Rowan Stone from Sapien to uncover how the company is rethinking AI training data by decentralizing the data collection process.
Rather than relying on centralized data centers, Sapien empowers contributors worldwide to provide high-quality, structured knowledge, helping eliminate biases and enhance model performance.
Rowan details how Sapien’s innovative data-foundry framework and incentive structures enable scalable, high-quality input for LLMs — from autonomous vehicle data to nuanced human insight across vertical specialties.
With a growing network of 560,000+ contributors and clients like Midjourney and UN agencies, Sapien.io is setting a new standard for how enterprise-grade AI models are fine-tuned and optimized in a rapidly evolving tech landscape.
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⏰ TIMESTAMPS:
0:OO - Introduction
1:00 - Introducing Sapien And Rowan
5:19 - Understanding Decentralization In AI
9:20 - Real-World Applications Of Decentralized Data
14:05 - Advice For Startups On Funding Success
16:52 - The User Base of Sapien
18:58 - Building And Scaling An Early-Stage Team
21:44 - Improving Data Quality With Peer Review
25:58 - Agentic Models And Chain Of Reasoning
30:40 - Practical Use of Chain of Reasoning in the Workplace
34:08 - Why Work with Vertical Models
37:42 - The Next Leap In Humanoid AI Robotics
40:58 - The Intelligence Level of AI Models
45:08 - Final Thoughts
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