ChinaTalk Scale's Alex Wang on the US-China AI Race
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Jun 25, 2024 
Alex Wang, CEO of Scale AI and former DoD collaborator, dives into the high-stakes AI race between the US and China. He highlights three critical barriers to achieving AGI and discusses how they might be overcome. The conversation reveals China's strengths and weaknesses in this tech showdown. Wang emphasizes the national security implications of AI advancements and argues for a unified approach to innovation without stifling creativity. Additionally, he proposes methods to safeguard AI development from espionage while fostering growth.
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Three Pillars of AI Progress
- AI progress depends on three exponential curves: compute, data, and algorithms.
- Current large language models are trained in two phases: pre-training on massive datasets and post-training with expert data.
Importance of Post-Training Data
- Post-training AI models significantly improves performance and relies heavily on expert or "frontier" data.
- This data captures expert reasoning, which is often not readily available.
The Data Wall
- The "data wall" limits AI progress because internet data is finite and grows slowly.
- Overcoming this requires data abundance, synthetic data, human expert input, and reinforcement learning environments.