Titans by Google - The Era of AI After Transformers?

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AI Summary

Summary of Video Transcript

• Introduction to Transformers and their Limitations
  • Google’s 2017 paper “Attention is All You Need” introduced Transformers.
  • Transformers process entire sequences at once using attention mechanisms.
  • They have a quadratic dependency on input sequence length, limiting scalability.
• Recurrent Models and Scalability
  • Recurrent models process sequences gradually, with linear dependency on sequence length.
  • They have better scalability but lower performance compared to Transformers.
• Titans: A New Model Architecture
  • Google Research’s paper “Titans: Learning to Memorize at Test Time” presents Titans.
  • Titans address the quadratic cost issue of Transformers.
  • Inspired by human memory, Titans incorporate a deep neural long-term memory module.
• Deep Neural Long-Term Memory Module
  • Unlike fixed vectors in recurrent networks, this module uses a neural network to encode past history into parameters.
  • The module learns to memorize without overfitting by focusing on “surprising” events.
  • It updates its parameters based on previous parameters and a gradient representing surprise.
  • The model includes a decay factor for past surprise and an adaptive forgetting mechanism.
• Loss Function and Memory Management
  • The loss function models associative memory, storing data as key-value pairs.
  • The model processes sequences gradually, embedding memory information into its weights.
• Titan Model Architectures
  • Memory as a Context: Uses persistent memory tokens, contextual memory from the long-term memory module, and an attention block.
  • Memory as a Gate: Utilizes sliding window attention and combines neural memory output with the core branch using a gating mechanism.
  • Memory as a Layer: Stacks layers of neural memory modules and attention blocks.
  • LMM: A version without an attention block, relying solely on the memory module.
• Performance of Titan Models
  • Titan models outperform baselines in language modeling and commonsense reasoning tasks.
  • Memory as a Context Titan achieves the best results among hybrid models.
  • LMM Titan performs best among non-hybrid models.
  • Titans excel in “needle in a haystack” tasks, showing effective context length.
  • Memory as a Context Titan significantly outperforms other models on the BABILong benchmark for long sequences.

Detailed Instructions and URLs

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