Dwarkesh Podcast Will scaling work? [Narration]
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Jan 19, 2024 The podcast dives into the feasibility of scaling Large Language Models toward Artificial General Intelligence, featuring a lively debate between skeptics and believers. It addresses challenges like data limitations and compute requirements while discussing the tension between generalization and memorization. The connection between AI scaling and theoretical limitations is explored, suggesting that tech advancements may outstrip our understanding. Additionally, it examines the hurdles of achieving true insight learning, contrasting neural networks with human cognition.
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Scaling and AGI
- Scaling LLMs++ could lead to powerful AIs by 2040 or sooner, automating cognitive labor.
- If scaling doesn't work, the path to AGI becomes longer and more complex.
Data Bottleneck and Self-Play
- Five orders of magnitude more data than currently available are needed for reliable AI.
- Self-play synthetic data generation faces evaluation and compute challenges.
Potential of LLMs and Synthetic Data
- LLMs could achieve human-level intelligence with sufficient data, given their current progress.
- Synthetic data and self-play are promising for overcoming the data bottleneck.