Building a Proto-Artificial Hippocampus: The Key to Long-Term AI Chatbot Conversations

Author: Florin Dzeladini

AI-Generated image using stable diffusion and the Askord imagine telegram bot. Using the following prompt /imagine the artificial brain https://api.telegram.org/file/bot5995537965:AAHp0a2cD5WpjV_RlFCnrPiMWVWhBpaJZWg/photos/file_41.jpg | samples=1 | original_batch_size=5 | target_index=4 | seed=82906

Introduction

AI chatbots have made significant strides in understanding and generating human-like conversations. However, their ability to maintain context over long-term interactions remains limited. To address this, we propose a memory module inspired by the human hippocampus that doesn’t require retraining the language model. In this article, we’ll explore the concept of a proto-artificial hippocampus and its potential to revolutionize long-term AI chatbot conversations.

The Inspiration

In the human brain, the hippocampus plays a crucial role in memory formation and consolidation. It works in tandem with other regions, like Broca’s area, which is responsible for language comprehension and production. By drawing inspiration from the hippocampus, we can design a memory module to enhance chatbot conversations without retraining the language model.

The Memory Module

The proto-artificial hippocampus involves the use of BIRCH clustering and contextual summaries, which can be implemented in the following steps:

1. Extract contextual summaries from user inputs and chatbot responses using a text summarization technique (e.g., FastText, BertSum).
2. Convert the contextual summaries into numerical embeddings using a vectorizer (e.g., TF-IDF, Word2Vec).
3. Train separate BIRCH clustering models to organize the embeddings:
4. Update the BIRCH models with new contextual summary embeddings for each interaction.
5. When selecting the context for a new conversation, use the combined BIRCH model (Model 3) to find the most relevant cluster based on the current user input. Retrieve corresponding contextual summaries from both user inputs and chatbot responses within that cluster to establish context.
6. Use the selected context to inform the chatbot’s responses, ensuring a more coherent and contextually relevant conversation.

The Proto-Artificial Hippocampus Advantage

• Improved long-term contextual understanding for chatbots, leading to more relevant and coherent responses.
• Enhanced ability to recall and utilize information from previous conversations, creating a more natural and engaging user experience.
• A scalable and modular solution that separates memory management from the language model, allowing for more flexible and adaptable AI systems.

Conclusion

By developing a proto-artificial hippocampus inspired by human brain functionality, we can revolutionize long-term AI chatbot conversations. This memory module, based on BIRCH clustering and contextual summaries, will enable chatbots to maintain context across extended interactions, creating more natural and engaging conversations. The result is a groundbreaking advancement in AI that brings us closer to truly human-like communication.

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