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Manifold
Letter from Reykjavik: Genomics, Chess, Hyperscaling genAI, and Quantum Black Holes — #67
Aug 29, 2024
Bobby Fischer, the legendary chess player, leaves a lasting legacy as the podcast explores his influence on the game and its connection to scientific pursuits. Reflect on the innovative work at deCODE Genetics in Reykjavik and how it reshapes big data genomics. Delve into the intersection of chess and AI, discussing the challenges of scaling models and the limits of current architectures. The conversation also touches on the quest for artificial general intelligence, highlighting the intriguing interplay between science and intellectual disciplines.
35:30
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Quick takeaways
- Decode Genetics' innovative approach to genomics highlights the potential of genealogical data to infer the genetic makeup of an entire population.
- Concerns over the effectiveness of synthetic data for training AI models raise questions about the depth needed for complex understanding in various fields.
Deep dives
Challenges of Synthetic Data in AI
There are concerns regarding the effectiveness of synthetic data for training large language models, especially in complex areas like finance, law, and history. The risk is that synthetic data may not reflect the depth and nuances captured in the first few trillion tokens generated by human writers. Thus, as the reliance on synthetic data increases for future AI training, its potential shortcomings could hinder the models' understanding of intricate concepts tied to these fields. Without robust human-generated data, the risk grows that the synthetic models may lack the depth and accuracy needed for meaningful analysis.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode