Transformer Model

Think of the game of Telephone. The first person whispers a message to the next, and it travels down the line. As it passes along, the original message gets blurry, and reaching the end takes time.

Older AI models read sentences much like this game. They processed text one word after another from front to back. In long sentences, details from the beginning often faded out by the time the model reached the end.

Then came the Transformer model, flipping the seating chart entirely. Instead of lining words up to whisper down a chain, it arranged them so that every word looks at every other word simultaneously.

In this circle, even the very last word can see the first word directly. No matter the distance, details fade much less than before, and computers can crunch the numbers in parallel across multiple chips. While extremely long inputs can still run into limits, and text-generating chatbots hide future words to keep predictions fair, this open view transformed how AI reads.

Simply sitting in a circle isn't enough. Consider the sentence: 'The cat grabbed the fish and bolted, and it was fast.' What does 'it' refer to?

Inside a Transformer, each word scores every other word to decide where to focus. Here, 'it' gives a high score to 'cat' and a low score to 'fish.' This scoring mechanism that decides where to look is called attention.

These attention scores are calculated for the whole sentence at once. Since words don't have to wait in line for their turn, computer chips like GPUs can process massive chunks of data simultaneously. That speed is precisely what made training large language models on vast libraries of text possible.

Today, modern translators, chatbots, and even image generators are built on top of this exact seating chart.

Let's return to the classroom. For students to interact, they need a common language. Computers cannot read letters directly; they only process numbers.

Because of this, every word is first converted into a long list of numbers called an embedding. Words with similar meanings are designed to have numbers that sit close together.

However, if everyone looks at each other at once, you lose track of who stood where. To fix this, the model attaches a seat number to each word, tracking its position. This positional info is crucial for telling apart 'dog bites man' from 'man bites dog,' where word order completely changes the meaning.

Remember, this architecture doesn't actually feel or understand meaning. It simply calculates mathematical relationships between words. It doesn't comprehend text like a human does, and there is still a hardware ceiling on how many words it can look at at one time.