Old style AI or bots - rule based - pattern matching -- simple Rnns

 Older bots relied on rule‑based scripts or recurrent networks, and some niche systems still use simpler pattern‑matching.


Before transformers, most language‑processing models relied on recurrent neural networks (RNNs) and their variants:
  • Simple RNNs – processed text word‑by‑word, keeping a hidden state that captured previous context.
  • LSTMs and GRUs – introduced gating mechanisms to better retain long‑range information and mitigate the vanishing‑gradient problem.
  • Seq2seq architectures – paired an encoder RNN with a decoder RNN for tasks like translation; attention was later added to let the decoder focus on specific encoder states.
  • Convolutional neural networks (CNNs) were also used for tasks such as sentence classification, applying filters over word embeddings.

These approaches were the backbone of NLP until the transformer’s self‑attention mechanism proved far more effective at handling long‑range dependencies.

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