Transformers

A transformer is the neural network design behind most modern language models. Its key trick is attention: each token can look at other tokens and decide what matters for the next prediction—without reading the sequence only left-to-right like older recurrent nets.

If someone says “the model is a transformer,” they mean this architecture family (2017 paper onward), not a movie robot. It is why scaling language models became practical.

Imagine students in a circle. When one speaks, everyone can glance at anyone else to gather context—not only the person sitting to their left. Multi-head attention is like glancing for different reasons (grammar vs topic vs quotes).

Stack many of those layers, add positional information so order is not lost, and you get a transformer block stack.

Chat models, many coding models, and a lot of modern speech/vision systems use transformer variants.

Read the first sections of “The Illustrated Transformer” (link below) and sketch one sentence with arrows showing which words might attend to which.

⚠️ Myth: Transformers understand like humans.

✓ Reality: They implement learned attention + feed-forward computations for prediction.

⚠️ Myth: Transformers replaced all other AI.

✓ Reality: Classical ML and CNNs still matter; transformers dominate many sequence tasks.

• Vaswani et al., 2017: https://arxiv.org/abs/1706.03762 ↗
• The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/ ↗
• ANN live: https://www.ainerdnetwork.com/learn/transformers ↗