.jpg "A 170M-Parameter Model Outperformed GPT-4")
Introduction: The Giant and the Mouse
Imagine a race.
In one lane, you have a giant. This giant is strong. It is smart. It has read almost every book on Earth. This giant is named GPT-4. It is massive. It costs millions of dollars to feed. It needs a huge house to live in. Everyone thinks the giant will win every time.
In the other lane, you have a mouse. This mouse is tiny. It is small. It does not eat much. It does not take up much space. It is called TITANS. Specifically, a tiny version of it. It has only 170 million parts. The giant has trillions.
The gun fires. They both start running.
For a short sprint, the giant wins. But then, the race changes. The race becomes a memory test. The runners have to read a book that is one million pages long. Then, they have to answer one tiny question about page 50.
The giant gets tired. The giant gets confused. It forgets what it read on page 50 because it is trying to remember page 900,000. It slows down. It stops.
The mouse keeps going. The mouse has a secret trick. It does not try to remember every word at once. It has a tiny notepad. It writes down the important parts. It ignores the boring parts. It reaches the end of the million pages. It answers the question perfectly.
The mouse wins.
This is not a fairy tale. This is real life. Google researchers created a new AI model called TITANS. In a very hard test about memory, this tiny model beat GPT-4.
This blog will tell you how. We will look at why bigger is not always better. We will see how a new way of "thinking" changes everything.
Part 1: The Problem with Big Brains
To understand why the mouse won, we must look at the giant.
GPT-4 is a type of AI called a "Transformer."
Transformers are amazing. They changed the world. They run ChatGPT. They run Claude. They run Gemini. But they have a big flaw. They have a bad memory problem.
Think of how you read a book.
You read one word. Then the next. You understand the story as you go.
Transformers do not read like you. They try to look at every word at the same time.
If you give a Transformer a sentence with 10 words, it looks at how every word connects to every other word.
Word 1 looks at Word 2.
Word 1 looks at Word 3.
Word 1 looks at Word 4.
...and so on.
This is fine for a short sentence. But what happens when the book is long?
Imagine a book with 1,000 words. The Transformer has to make millions of connections.
Imagine a book with 1,000,000 words. The connections become too many. The math explodes. The computer runs out of space. It crashes.
This is called "Quadratic Complexity." It is a fancy word. It just means: "The more you read, the harder it gets. And it gets harder very, very fast."
This is why GPT-4 has a limit. It can only remember a certain amount. If you give it too much text, it forgets the beginning. It gets "lost in the middle."
The giant is strong. But the giant has a short attention span.
Part 2: Enter the TITANS
Google saw this problem. They wanted to fix it.
They did not want to build a bigger giant. Adding more size just makes the memory problem worse. It makes the model slower. It makes it cost more money.
They wanted to change how the model remembers.
They wrote a paper. It is called "Titans: Learning to Memorize at Test Time."
That title is a hint. "Learning to Memorize."
Most AI models stop learning after they are made. You train them. You teach them facts. Then you lock their brain. When you use them, they are frozen. They cannot learn new things permanently. They can only hold a little bit in their short-term mind.
TITANS is different.
TITANS can learn while it reads.
It has a new design. It is like a hybrid car. A hybrid car has a gas engine and an electric battery. They work together.
TITANS has three parts in its brain.
** The Core (Short-Term Memory):** This is like the old Transformer. It is good at looking at the words right in front of it. It is fast. It is sharp.
** The Neural Memory (Long-Term Memory):** This is the new magic part. It is a deep storage system. It can hold data for a long time.
** The Persistent Memory:** This is the knowledge it was born with. Like knowing how to speak English.
The magic is in the second part. The Neural Memory.
Part 3: How the Mouse Remembers
Let's use a simple picture.
Imagine you are in a lecture. The teacher is talking very fast.
The Transformer Way (GPT-4): You try to keep every single word the teacher says in your head. You do not write anything down. You just listen. After 10 minutes, your head hurts. After 20 minutes, you forget the start. After an hour, you are lost. You cannot hold it all.
The TITANS Way: You have a notepad. The teacher speaks. You listen. When the teacher says something boring, you do nothing. When the teacher says something surprising or important, you write it down. You write: "The test is on Friday." You write: "The answer is 42." You ignore the jokes. You ignore the "umms" and "ahhs."
Your notepad does not get full quickly. You only store the key facts.
At the end of the class, you can look back at your notes. You can remember what happened an hour ago. You did not have to hold it all in your brain. You used the notepad.
This is what TITANS does.
It has a "Memory Module." This module is actually a tiny neural network inside the big one.
As it reads a long book, it asks a question: "Is this new word surprising?"
If the word is surprising, the model knows it is important. It updates its internal weights. It "learns" that fact.
If the word is predictable (like "the" or "and"), it ignores it. It does not waste space.
This process is called "Test-Time Training."
It means the model is actually training itself while you talk to it. It is changing its own brain to fit your conversation.
.jpg "A Healthier AI Future")
Part 4: The Benchmark That Shocked Everyone
Talk is cheap. Google had to prove it.
They used a test called "BABILong."
This is a very hard test. It is also called the "Needle in a Haystack" test.
Here is how it works:
Take a huge amount of text. Maybe 100 books. This is the "Haystack."
Hide one tiny fact inside. Maybe "The secret code is Blue." This is the "Needle."
Put the needle anywhere. Maybe at the start. Maybe in the middle. Maybe at the end.
Ask the AI: "What is the secret code?"
If the AI has a bad memory, it will fail. It will say "I don't know." Or it will guess.
Google tested TITANS against the big models.
They used a massive context window. They gave the models millions of tokens. (Tokens are like parts of words).
The Result: TITANS, even the small versions, found the needle. It found it almost every time. It found it when the text was short. It found it when the text was 2 million tokens long.
GPT-4 struggled. When the text got too long, GPT-4's attention got watered down. It is like trying to find a specific grain of sand on a beach. There is just too much noise.
But TITANS was clean. It had "written down" the secret code in its neural memory. When asked, it just looked at its notes.
The small 170M parameter model could handle context lengths that would choke a standard Transformer.
It proved that you do not need a trillion parameters to be smart. You just need a better way to organize your thoughts.
Part 5: Why "Surprise" Matters
Let's dig a little deeper into the "Surprise" trick. This is the coolest part.
How does the AI know what to write in its notepad?
It uses math. It uses gradients.
In AI, a "gradient" is usually used to train the model before it is released. It is a signal that says "You made a mistake, fix it."
TITANS uses this signal live.
When it reads a word, it tries to guess what it is. If it guesses right, the "Surprise" is low. The gradient is small. The memory does not change much. If it guesses wrong, the "Surprise" is high. The gradient is big. The memory updates a lot.
Think about your own life. Do you remember what you had for breakfast 100 days ago? No. It was not surprising. Do you remember the day you broke your arm? Yes. It was surprising. It was painful. It was new.
Your brain keeps the surprising things. It deletes the routine things.
TITANS mimics this human trait.
Most Transformers treat every word as equal. To a Transformer, the word "the" is just as heavy as the word "fire."
TITANS treats words by their value. "The" = Low value. Forget it. "Fire" = High value. Remember it.
This makes TITANS incredibly efficient. It compresses information. It takes a huge book and turns it into a small, rich summary in its mind.
Part 6: Small but Mighty
Why is the number "170M" so important?
"170M" stands for 170 Million Parameters.
To us, 170 million sounds like a big number. But in AI, it is tiny. GPT-4 is rumored to have over 1 Trillion parameters.
That means GPT-4 is roughly 5,000 times bigger than this little TITANS model.
Imagine a bicycle racing a Ferrari. The Ferrari (GPT-4) has a massive engine. It goes fast on a straight road. But the race is through a dense jungle (Long Context). The Ferrari gets stuck. It is too wide. It hits trees. It runs out of gas.
The bicycle (TITANS) is small. It weaves through the trees. It keeps going.
This is a big deal for the future of computers.
Running GPT-4 costs a lot of energy. It requires giant server farms. It burns coal and gas to make electricity.
Running a 170M model is cheap. You could almost run it on a good laptop. Maybe even a phone one day.
If a small model can do the job of a big model, we save money. We save energy. We make AI available to everyone, not just rich companies.
Part 7: The Three Flavors of TITANS
Google did not just make one model. They made three versions. They are like three flavors of ice cream.
MAC (Memory as Context): This is the helper. It treats memory like an extra book to read. It adds the past info to the current info. It is great for summarizing long stories.
MAG (Memory as Gate): This is the mixer. It has a gate. A gate is like a door. It opens and shuts. It decides: "Should I use my short-term memory now? Or my long-term memory?" It mixes them perfectly.
MAL (Memory as Layer): This is the sandwich. It stacks the memory right into the brain layers. It is very deep.
Each flavor is good for different things. But they all share the same superpower: The Neural Memory.
Part 8: What Can We Do With This?
Okay, so TITANS is cool. But what is it for? Why should you care?
Here are some real-life ways this changes things.
1. Personal Assistants that Remember You Right now, Siri and Alexa are forgetful. They do not remember what you told them last week. With TITANS, an assistant could remember your whole life. It could remember a story you told it a year ago. It keeps a "Neural Memory" of you. It learns you.
2. Reading Whole Libraries A lawyer has to read thousands of legal cases. It takes weeks. GPT-4 can read a few cases. TITANS could read every case ever written. It could find the one tiny law that saves the client.
3. Writing Code Programmers write millions of lines of code. Sometimes, a bug is hidden in a file you wrote three years ago. TITANS can hold the whole codebase in its head. It can spot the connection between the new code and the old code.
4. Analyzing DNA DNA is a long string of letters. A, C, T, G. It is millions of letters long. Finding patterns in DNA is hard because the context is so huge. TITANS loves long sequences. It could help cure diseases by reading DNA better than any human or old AI.
Part 9: The Danger of "Memory Poison"
Is TITANS perfect? No.
There is a risk. It is called "Memory Poisoning."
Remember how TITANS learns from "Surprise"? What if I tell it a lie? What if I tell it a lie that is very surprising?
The model might grab that lie. It might write it down in its permanent notes. Because it learns at test time, a bad user could trick it.
If I say, " The sky is actually green, and here is a surprising proof," the model might believe me. Then, later, if you ask "What color is the sky?", it might say "Green."
Engineers have to be careful. They have to teach the model to be skeptical. They have to secure the memory so bad actors cannot poison it.
Also, debugging is hard. With GPT-4, the brain is fixed. If it makes a mistake, we can look at the fixed brain. With TITANS, the brain changes every second. If it makes a mistake, we have to ask: "What did it learn five minutes ago that made it crazy now?" It is like trying to fix a car while the car is driving down the highway and changing its own tires.
Part 10: The End of the Era of "Big"
For the last five years, AI has followed one rule: "Scale is All You Need."
Companies thought: "Just make it bigger. Add more chips. Add more data. It will get smarter."
TITANS breaks that rule.
It shows that "Architecture" matters more than "Scale." How you build the brain is more important than how big the brain is.
It is a lesson from nature. An elephant has a huge brain. But a human brain is smaller. Yet, humans build cities and elephants do not. Why? Because our brain structure is different. We have language. We have better memory tools.
TITANS is a step toward a more human-like AI. An AI that is not just a giant calculator. An AI that listens. An AI that takes notes. An AI that learns.
Conclusion
The race is not over. The giant is still running. GPT-4 is still very powerful. It knows a lot of facts.
But the mouse has proved a point. You do not need to be a giant to win a marathon. You just need to know how to pace yourself. You need to know what to remember and what to forget.
TITANS is a glimpse into the future. A future where AI is cheap. A future where AI is fast. A future where AI fits on your phone.
And most importantly, a future where AI remembers what you said, even if you said it a million words ago.
The era of "Bigger is Better" might be ending. The era of "Smarter is Better" has just begun.
And it started with a tiny, 170-million parameter model that took down a titan.
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg&description= A 170M-Parameter Model Outperformed GPT-4 — Google’s TITANS Shows Why Bigger Isn’t Better){kind=link}
0 Comments