When Machines Learned to Pay Attention
10 min read6 days ago
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In the last decade, artificial intelligence has undergone a remarkable transformation. But in 2017, something extraordinary happened, a single paper from Google Brain, titled “Attention Is All You Need,” quietly rewrote the future of deep learning.
The Transformer architecture introduced in that paper became the foundation of almost every modern AI model, from GPT-4 and Gemini to BERT, Whisper, and Stable Diffusion. It not only improved language understanding but changed how machines learn context.
So what makes this architecture so powerful? Let’s dissect the Transformer step by step from its roots to its inner workings and see why attention truly …
When Machines Learned to Pay Attention
10 min read6 days ago
–
Press enter or click to view image in full size
In the last decade, artificial intelligence has undergone a remarkable transformation. But in 2017, something extraordinary happened, a single paper from Google Brain, titled “Attention Is All You Need,” quietly rewrote the future of deep learning.
The Transformer architecture introduced in that paper became the foundation of almost every modern AI model, from GPT-4 and Gemini to BERT, Whisper, and Stable Diffusion. It not only improved language understanding but changed how machines learn context.
So what makes this architecture so powerful? Let’s dissect the Transformer step by step from its roots to its inner workings and see why attention truly was all we needed.
The World Before Transformers
Before Transformers, the world of natural language processing (NLP) revolved around sequence-to-sequence (Seq2Seq) models built using RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory).
A Seq2Seq model was like a translator with two brains:
- The encoder read the input sentence and stored…