The Transformer: Why It Changed Everything

Before 2017, AI processed text like a human: one word at a time, from left to right. This was slow, and the AI often forgot the beginning of a sentence by the time it reached the end.

The Transformer changed this by introducing Self-Attention.

1. What is "Attention"?

Imagine reading a sentence: "The bank of the river was muddy, but John went to the bank to deposit his check."

• How do you know the first "bank" is land and the second "bank" is a building?
• You look at the Surrounding Words ("river" vs "deposit").

The Transformer uses an Attention Mechanism to look at every word in a sentence simultaneously and decide which words are most important for explaining the current word.

2. The Context Window

The "Memory" of a Transformer is called its Context Window. It's the maximum number of tokens it can look at at one time.

• If a model has a 128k context window, it can "read" an entire book in one go and understand the relationships between the first and last chapters.

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Visualizing Attention

In the sentence: "The cat sat on the mat because it was soft." The model applies strong "Attention" between the words "it" and "mat".

graph LR
    The -- 0.1 --> it
    Cat -- 0.2 --> it
    Mat -- 0.9 --> it
    Soft -- 0.8 --> it

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Why Transformers are Scalable

Because they process all words at once (Parallelism), we can train them on massive supercomputers using millions of GPUs. This is why AI has improved so much faster than other technologies in history.

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💡 Guidance for Learners

When you hear about a "New Model" with a "Longer Context," think of it as an agent getting a larger desk. The bigger the desk, the more "Files" (Context) they can have open at once without forgetting anything.

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Summary

• Attention allows models to understand word relationships instantly.
• Transformers process data in parallel, enabling massive scale.
• Context Windows define how much information the model can "hold in its head" at once.