Why Old Neural Networks Hit a Wall
RNNs were slow. CNNs were blind to sequence. Both struggled with long context. Language and reasoning require remembering things far apart. Old models forgot too easily.
The Idea That Changed Everything
Instead of reading text word by word, transformers look at the entire sentence at once and decide what matters. This is called attention.
Attention is the ability to decide what is important without reading in order.
How Attention Works
Each token generates three vectors: Query, Key, and Value. The model compares queries to keys to decide how much attention to pay, then combines the values.
attention = softmax(Q * K.T / sqrt(d)) * VWhy This Is Fast
Because the model processes the entire sequence at once. No waiting for previous tokens. GPUs love this. Training becomes massively parallel.
Stacking the Magic
Transformers stack multiple attention layers. Each layer learns higher level relationships: grammar, meaning, reasoning, and context.
Why This Enabled LLMs
Large language models are just very large transformers trained on enormous text. The architecture made scale practical.
Limitations
Attention grows quadratically with input length. Memory becomes the problem. This is why context windows are a hot research area.
Conclusion
Transformers did not just improve AI. They made modern AI possible.