Transformers — The Architecture That Changed AI (Part 1 of 3)

Short summary

The Transformer architecture (2017) replaced sequential RNNs by processing entire sequences in parallel via attention, solving bottleneck and gradient-flow problems while enabling GPU-scale training. It powers GPT-4, Claude, Gemini, DALL-E—virtually every major AI breakthrough. Part 1 of 3 covers core Transformer design; Part 2 covers Vision Transformers, Part 3 Vision-Language Models.

• •Transformers process all tokens in parallel using attention instead of RNNs' sequential token-by-token approach
• •Solved three RNN failures: information bottleneck from sequential compression, lack of GPU parallelization, vanishing/exploding gradients
• •Foundational to ChatGPT, Claude, Gemini, DALL-E, Stable Diffusion, and virtually all modern AI systems

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