In 2017, "Attention is All You Need" revolutionized AI by introducing Transformers, powering the AI tools we use today. Now, in 2025, Google's Titans might do the same - but this time with memory.
While everyone's talking about DeepSeek's efficient AI models, Google published research that deserves equal attention. Their paper "Titans: Learning to Memorize at Test Time," shows a promising solution to AI's memory problem.
As the researchers note:
"Memory is a fundamental mental process and is an inseparable component of human learning. Without a properly functioning memory system, humans and animals would be restricted to basic reflexes and stereotyped behaviors."
The Problem
Current AI systems face a fundamental limitation: Today’s leading models can only process/remember about 32,000 words at once (~42,000 tokens* or 50-60 pages). The more text they try to handle at once, the more computing power they need (this grows exponentially),making the processing of long documents both expensive and inefficient.
Google's Solution: Titans
Google has developed a new architecture called Titans that enables AI to process and remember information from much longer texts - over 2 million words (which is far more than previous systems could manage). Titans is inspired by how human memory works and consists of three main parts working together*:
Foundation Memory (Persistent Memory)
Creates smart tags to help organize and recall information
Stores stable, global knowledge that doesn't change
Helps the AI focus on important information throughout the text
Context Memory (Contextual Memory)
Updates as it reads new information (like short-term memory)
Keeps track of what it learned from earlier parts of a document
Connects information across different sections
Integration Center (Core Component)
Combines both types of memory with new information
Uses attention mechanism to decide what's worth remembering
Makes sure only useful information gets stored
more information on solution below**
Results
Titans isn't just a minor upgrade - it's showing impressive capabilities:
Finds specific details in massive documents
Maintains accuracy even with very long texts
Uses computing power more efficiently
What This Means for Business
Better AI memory can potentially:
Process entire documents at once instead of small pieces
Maintain context across long conversations or analyses
Remember important details without needing to reprocess everything
Real Business Applications
Customer Service: Imagine AI that remembers your entire customer history, not just the current conversation
Document Analysis: Processing entire legal contracts or medical histories at once
Research & Development: Analyzing years of research papers to find hidden connections
Content Creation: Generating consistent, contextually aware content across long formats
The Bottom Line
Just as Transformers revolutionized AI in 2017, Titans could be the next big leap in 2025.
Your Next Move: Look at where your business deals with large documents or needs to remember long histories. Those are the spots where better AI memory could transform your operations.
Those are my Thoughts from the DataFront
Max
TFDF Notes
*Tokens are the basic units of text that AI models process. Unlike whole words, a token can be:
A complete word
Part of a word
A punctuation mark
A character
**Google's Titans architecture comes in several variations, each with slightly different ways of handling memory and processing information. These variations allow researchers to test different approaches and find the most effective method for various tasks. The main versions are:
Memory as a Context (MAC)
Memory as a Gate (MAG)
Memory as a Layer (MAL)
LMM (Long-term Memory Module only)
Each of these variations builds on the core idea of combining different types of memory to process and remember information from very long texts more effectively than previous AI systems. for more details check out the paper here