<ul><li>The blog series explores the evolution of LLMs and generative AI, starting from encoder-decoder models to advanced GPT frameworks and autonomous AI agents.</li><li>Automated translation progressed from rule-based systems to neural networks like RNNs, designed for processing sequential data with a hidden state for carrying forward information from previous steps.</li><li>The RNN encoder-decoder model aimed to address the challenge of language translation but had limitations with long and complex sentences due to its sequential nature.</li><li>The evolution from rule-based systems to RNN-based encoder-decoder models is discussed, highlighting their power and limitations, leading to innovations like LSTM and GRU for addressing challenges.</li></ul>

How We Met AI: Episode 1 : RNN Encoder–Decoder Revolution

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