The Rise of the Transformers – a simplified version.

 The Rise of the Transformers – a simplified version.

Long before the wonderful world of ChatGPT, large language models struggled to understand meaning across sentences, paragraphs, and context. Sequence mattered, but older architectures—RNNs, LSTMs—could only peer through narrow windows. Comprehension was fleeting, memory limited, parallelism elusive.

Then came the breakthrough: the Transformer.

In this series, I’ll be simplifying how we got here, what Transformers are, why they matter, and where we’re headed in this rapidly evolving landscape of machine understanding.

How we got here. 

What was the Problem that the Transformer was trying to solve. 

Before Transformers, neural networks processed language one step at a time. Earlier models like RNNs(Recurrent Neural Networks) and LSTMs read sentences sequentially, word by word, with limited memory of what came before, like someone with a short memory trying to follow a long story.

Consider this paragraph:

“Sarah packed her suitcase with sunscreen, sandals, and a swimsuit. She couldn’t wait to relax and read by the water. After a long flight, she finally arrived.”

If you forget the first sentence, you might not realize Sarah is going on a beach vacation. The clue was in the sunscreen and swimsuit. Without remembering that, the last sentence just sounds like she traveled somewhere, but you miss the whole point of where and why. That’s exactly how older models struggled, they couldn’t connect the dots across longer passages. Adding a bit of AI speak to this, it was hard for earlier models to capture long-range relationships between words or groups of words in different parts of a sentence. In other words, they did not capture the context of a sentence accurately. 

Further, the older models couldn’t be trained in parallel. They had to read and process each word one after another, in order. Imagine a solo worker at an assembly line, assembling toy figures. They would first have to attach the arms, then the legs, paint the body and then run a final test. But, they can’t finish painting unless the arms and legs are attached. Every step must be completed in exact order, one at a time. If one step takes too long, everything else must wait.  That’s how RNNs work—processing one word at a time, always waiting for the previous step to finish.

This also means scaling was difficult. Scaling simply put, means making something work the same or better as it gets bigger. In the world of AI, it means training bigger models on more data, using more computing power—without slowing down or breaking the process.

Think of a small bakery. They have a set of ovens and bakers to handle their current order size. Now imagine they wanted to handle bigger orders and process them in the same time or better as the current order. They could buy additional ovens and employ additional bakers, but if they had no place to put the equipment or the bakers in, then they obviously cannot accept bigger orders and  process them at the same time as they did with current orders. This is a scaling problem. The bakery cannot scale well. 

Similarly, in older models like RNNs, scaling was hard because each word had to be processed one at a time, in order. Adding more computing power didn’t help much, because everything had to wait for the previous step to finish.

Enter Transformers.

Born from the seminal 2017 paper “Attention is All You Need” (Vaswani et al., 2017), Transformers changed everything.

Transformers solved three major problems that older models struggled with. First, they handled long-range relationships by using a technique called self-attention, which allows the model to look at all the words in a sentence or paragraph at once and understand how each word relates to the others—no matter how far apart they are. This is a big shift from older models, which quickly forgot earlier information in longer texts. 

Second, Transformers support parallel processing because they don’t have to read words of a sentence one by one. Instead, they analyze the entire sequence of words all at the same time, which makes training much faster. 

And third,  by design makes Transformers are incredibly scalable. As more data and computing power are added, Transformers can take advantage of it effectively—just like adding more lanes to a highway to handle more traffic without slowing down. 

With them came the dawn of models that didn’t just process language—they contextualized it, synthesized it, anticipated it. Transformers became the engine of progress behind BERT, GPT and the generative AI wave reshaping industries today.

The rise of Transformers isn’t just a story of better performance, it’s a story of rethinking how machines understand language. By enabling parallel processing, scaling with ease, and capturing meaning across entire sequences, Transformers have become the backbone of modern AI. In my next post, I’ll dive deeper into self-attention, one of the core innovations that helped Transformers take off. I’ll also explore how Transformers support parallel processing and scalability. Stay tuned!