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Chatbots, LLMs, and Conversational AI
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The early 2010s saw two distinct conversational interface paths: enterprise chatbots, which were rigid, task-oriented, and often failed outside predefined scripts (e.g., British Telecom's "Aimee"), and consumer voice assistants (e.g., Siri, Alexa), which were lifestyle-oriented and cloud-powered with an "App Store" model.
Parallel NLP research moved from N-gram models to neural networks (RNNs, LSTMs). The 2017 Transformer architecture, using self-attention, enabled parallelization and long-range dependency capture, overcoming RNN limitations.
This led to Large Language Models (LLMs), which evolved from the Transformer into encoder-only (BERT, for NLU) and decoder-only (GPT, for NLG) models. LLMs represent a disruptive pivot from deterministic, rule-based systems to probabilistic, learned intelligence, outperforming earlier chatbots.
Future conversational AI will use hybrid architectures, combining LLM generative power with the predictability of deterministic systems for task execution, and techniques like Retrieval-Augmented Generation (RAG) for factual grounding.
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By AdrianSend us a text
The early 2010s saw two distinct conversational interface paths: enterprise chatbots, which were rigid, task-oriented, and often failed outside predefined scripts (e.g., British Telecom's "Aimee"), and consumer voice assistants (e.g., Siri, Alexa), which were lifestyle-oriented and cloud-powered with an "App Store" model.
Parallel NLP research moved from N-gram models to neural networks (RNNs, LSTMs). The 2017 Transformer architecture, using self-attention, enabled parallelization and long-range dependency capture, overcoming RNN limitations.
This led to Large Language Models (LLMs), which evolved from the Transformer into encoder-only (BERT, for NLU) and decoder-only (GPT, for NLG) models. LLMs represent a disruptive pivot from deterministic, rule-based systems to probabilistic, learned intelligence, outperforming earlier chatbots.
Future conversational AI will use hybrid architectures, combining LLM generative power with the predictability of deterministic systems for task execution, and techniques like Retrieval-Augmented Generation (RAG) for factual grounding.