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Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312
May 6, 2025
Alon Bochman, CEO of RagMetrics and AI veteran, dives into the complexities of making AI reliable. He emphasizes empirical evaluation over influencer advice, advocating for collaboration between technical and domain experts. Alon discusses the importance of tailoring AI solutions and involving subject matter experts in development. The conversation also covers fine-tuning language models through expert feedback and the challenges of AI in finance, highlighting the need for effective knowledge-sharing to enhance accuracy in decision-making.
01:01:37
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Quick takeaways
- Empirical evaluation is critical in machine learning, allowing engineers to discover effective configurations tailored to their specific tasks.
- Establishing clear evaluation criteria helps developers generate meaningful benchmarks, enabling them to discern optimal model configurations more effectively.
Deep dives
The Importance of Testing in AI Systems
Testing each component of an AI system is vital, especially as complexity increases with multiple agents involved. Engineers should not simply follow established guidelines; instead, they should actively engage with the data to discover what configurations work best for their specific tasks. An experimental approach allows engineers to benchmark different models and setups, ultimately leading to better performance tailored to user needs. This process enhances the overall quality of the project and encourages innovation through trial and error.
