[Market Trends] AI can't cross this line and we don't know why. | Welch Labs
📌 The AI Barrier: What’s Holding Us Back?
This video explores the phenomenon that despite advancements in AI, models hit a performance limit beyond which their error rates cannot decrease, a point known as the compute-efficient frontier. As AI models are trained with more compute, data, and size, their error rates improve but eventually plateau. OpenAI's studies, especially with models like GPT-3 and GPT-4, demonstrate that scaling larger models leads to consistent gains, but no model has surpassed this boundary. The video delves into neural scaling laws, which explain how error rates scale with model size, data, and compute, revealing that certain trends hold true across diverse problems. The video raises the question of whether these limits represent a fundamental law of AI, like the laws of physics, or are a result of our current approaches. It highlights that some uncertainty, especially in language models, prevents AI from achieving zero error rates.
