Before 2017, AI models like RNNs processed text sequentially, where each word passed information to the next in a chain, causing information to fade over long distances (e.g., word 1 forgotten by word 50). The 2017 paper 'Attention Is All You Need' by Vaswani et al. introduced Transformers, which allow every word to attend to every other word simultaneously through direct connections, eliminating the sequential chain and preventing information loss. This parallel processing approach enabled AI to capture long-range dependencies and contextual relationships more effectively, revolutionizing natural language processing.
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RNNs Had a Fatal Flaw — Why Transformers Replaced Sequential ProcessingIndiziert:
Before 2017, AI read words one at a time. Each word passed its information to the next in a long chain — but by the time it reached word fifty, word one was almost forgotten. Information fades over long distances. Then Transformers arrived. Every word can attend to every other word simultaneously. No chain. No forgetting. Direct connections everywhere. One paper changed everything: "Attention Is All You Need" — Vaswani et al., 2017. Full episode: The Math Behind AI EP12 — "Attention Is All You Need" 👉 https://youtu.be/vzgIdRDtbYk #Transformer #RNN #AttentionIsAllYouNeed #DeepLearning #MachineLearning #Math #AI #AxiomMotion #Manim #NeuralNetwork #Vaswani2017
Before 2017, AI read words one at a time. Each word passed its information to the next in a long chain.
But by the time it reached word 50, word one was almost forgotten. Information fades over long distances.
Then transformers arrived. Every word can talk to every other word simultaneously. No chain, no forgetting, direct connections everywhere.
One paper changed everything. Attention is all you need. Vaswani and colleagues, 2017.
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