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The past, present and future of chatbots
- Chatbots have been around for decades, with the earliest version, ELIZA, released in 1967 as a simple rules-based program.
- Jabberwocky in the 1980s enabled voice interaction, while A.L.I.C.E. in the 1990s allowed responses to prompt to inform future responses.
- The shift from rules-based chatbots to data-driven models was driven by advancements in compute power and availability of data.
- The development of large language models like ChatGPT, using GPT-3 architecture, enabled more varied and conversational responses.
- Open-source machine learning libraries like PyTorch and TensorFlow have made developing chatbots more accessible for businesses.
- Cost remains a barrier for heavy-duty chatbot use cases, with the need to consider GPU usage and model parameters.
- Multi-modal models are the future of chatbots, allowing interactions through text, speech, imagery, video, and audio, enhancing creative capabilities.
- Retrieval augmented generation (RAG) architectures empower chatbots to draw on proprietary data for advanced enterprise use cases.
- As chatbots evolve with technologies like RAG systems and AI agents, the innovation potential for organizations is vast, promising efficiency gains.
- In the future, multi-modal models and evolving technologies will continue to enhance the capabilities and efficiency of chatbots in various industries.
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