If until now AI was like a translator who sat with a dictionary in his hand and translated word by word (sometimes with hilarious results), in 2017 everything changed. A scientific paper appeared with a title that sounds almost like philosophy: “Attention Is All You Need”. From that moment on, we stopped building networks that only “process” and started building networks that “understand context”.

1. The Problem with Old “Memory” (RNN and LSTM)

Before Transformers, we used recurrent neural networks (RNN). Their problem? They had a short memory. If you gave them a long text, by the time they reached the end of the sentence, they would forget how it started. It was like trying to read a book, but forgetting the main characters after each chapter.

2. The “Self-Attention” Mechanism (Essence of S366 AI)

Transformers introduced the concept of Attention. Instead of reading linear text (from left to right), they look at all the words at once and decide which ones are most important in relation to the others.

Example: In the sentence “The bank on the riverbank collapsed”, the AI ​​needs to know whether “bank” refers to a joke or a feature of the landscape. The attention mechanism analyzes the word “river” and makes the connection instantly, paying “attention” to the correct context.

Parallelism: Unlike older models, Transformers can process huge volumes of data in parallel. This is where the power of our infrastructure (Xeon + RTX) comes in again, allowing these massive matrix calculations to be processed in record time.

3. From Transformers to the GPT Generation

Once we had the Transformer architecture, we could move on to Pre-training. We let the models read the entire internet (or almost all of it) to learn the structure of the language. These models are no longer just “if/then” programs. They are systems that have learned the logical probability of ideas. When you interact with S366 AI, you don’t get an answer from a dusty database, but a text generated in real time, where each word is chosen because the model’s “attention” calculated that it is the most appropriate in the context of your question.

4. Business Impact: RAG (Retrieval-Augmented Generation)

Here is the “magic” we do. Transformers are good, but sometimes they are a bit too much (hallucinate). Through the RAG technique, we force the AI ​​to “turn its attention” to your company’s documents first, ensuring that the answer is not only intelligent, but also technically and legally correct.

Conclusion
Transformers were the missing piece of the puzzle to move from “calculating machines” to “digital partners”. We taught the computer to prioritize information, just like we humans do.

In Episode 7, we'll talk about the future: Multimodal AI. We'll see how Transformers have learned to "see" images and "hear" sounds.

If you want to understand why hardware is vital for Transformers, reread Episode 5 about GPUs.