Noise in Training Data
Alex explores the concepts of static and dynamic noise in training data, highlighting how even with significant corruption—like 70% of digits being wrong—transformer models can still achieve remarkable accuracy. The discussion reveals the surprising resilience of these models, as they maintain performance despite high levels of noise, only faltering when corruption reaches 90%. This insight underscores the robustness of modern AI architectures in handling imperfect data.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - 680
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