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technologyreview.com·2h ago
Data Science
Towards Data Science·25d ago·5 min read
RAG Was Always a Temporary Workaround. What is Next?
TL;DR
Current Retrieval-Augmented Generation (RAG) systems rely on a complex process of encoding and decoding information, which introduces latency and inefficiency. The article argues for a shift towards persistent neural states as a more effective memory solution.
✦ Why It Matters
Engineers should explore implementing persistent neural states in their AI models to reduce latency and improve memory efficiency.
Key Takeaways
- Investigate alternatives to RAG for memory management in AI.
- Consider latency budgets when designing AI systems for real-time applications.
- Stay updated on research in direct memory transfer techniques.
- Evaluate the architectural compatibility of models for effective state transfer.
How It Works
RAG operates by converting hidden states from neural networks into text, which is then embedded and stored as vectors. When needed, these vectors are retrieved and reconstructed into hidden states, creating a high-latency process that mimics memory.
The goal is to eventually enable direct transfer of latent states between models, bypassing the need for text-based translation.