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Transformers Explained: The Discovery That Changed AI Forever
4 segments available
Nearly every modern AI model, from ChatGPT and Claude to Gemini and Grok, is built on the same foundation: the Transformer. In this video, YC's Ankit Gupta traces how AI learned to understand language — from early RNNs and LSTMs to attention mechanisms and the breakthrough 2017 paper Attention Is All You Need — the discovery that unlocked the modern AI era.
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The Rise of Transformers Explained
This segment explores the origins of the transformer architecture and the key developments that paved the way for its success in AI.
"Nearly every state-of-the-art AI system, whether it's ChatCBT, Claude, Gemini, or Grock, is built on the same underlying model architecture, the transformer. But where did the transformer architecture..."
Reviving LSTMs: From Limits to Leaders
Explore how LSTMs evolved to address the vanishing gradient problem and dominated NLP applications.
"signals used to adjust weights during training would fade to near zero as they were passed backwards through time. In the 1990s, Hawk Rider and Schmidh Huber proposed a solution of this. It was called..."
Attention: The Game-Changer in Sequence Models
This segment highlights the breakthrough of incorporating attention mechanisms into sequence models, revolutionizing tasks like machine translation.
"performance issue. It pointed to a deeper architectural problem. Allowing the decoder to only see one static summary of the input was a fundamental limitation. Why not give it access to all of the int..."
Transformers: Revolutionizing AI Speed and Accuracy
This segment discusses how transformers replaced traditional RNNs, enhancing speed and accuracy in AI systems.
"models on large data sets, the kinds we knew would be necessary to achieve broadly useful AI, intractably slow. In an attempt to speed up RNN's, researchers developed techniques like factorizing LSTM ..."