Google Titans architecture, helping AI have long-term memory
4 days ago
- #AI
- #Machine Learning
- #Sequence Modeling
- Introduction of Titans architecture and MIRAS framework for AI long-term memory.
- Titans combines RNN speed with Transformer accuracy, updating memory in real-time.
- MIRAS provides a theoretical blueprint for generalizing sequence modeling approaches.
- Titans uses a 'surprise metric' to prioritize novel information for memory storage.
- MIRAS defines sequence models via memory architecture, attentional bias, retention gate, and memory algorithm.
- Three MIRAS variants: YAAD (robust to outliers), MONETA (strict penalties), MEMORA (stable memory updates).
- Titans outperforms state-of-the-art models in language tasks and long-context recall.
- Demonstrated scalability to context windows larger than 2 million tokens.
- MIRAS transcends mean squared error limitations, enabling non-Euclidean objectives.
- Significant advancement in sequence modeling with potential for long-context AI applications.