What is a Transformer in AI?

A Transformer is a neural network architecture that uses self-attention to process sequences in parallel. Introduced in 2017, it is the foundation of virtually all modern large language models including GPT-4, Claude, and Gemini.

Why were Transformers better than RNNs?

Transformers process entire sequences in parallel (vs. RNNs' sequential processing), making them much faster to train on modern GPU hardware. They also capture long-range dependencies more effectively using attention mechanisms.

What is self-attention?

Self-attention is a mechanism where each token in a sequence computes a weighted representation of all other tokens, allowing the model to understand context and relationships across the entire input simultaneously.

Architecture

Transformer Architecture

The Transformer is a deep learning architecture introduced in the 2017 paper "Attention Is All You Need" by Google researchers. It replaced recurrent networks for sequence processing, using self-attention mechanisms to relate all positions in a sequence to each other in parallel. Every major large language model — GPT-4, Claude, Gemini, Llama — is built on the Transformer architecture.

How Transformers Works

Transformers process sequences using multi-head self-attention: for each token, they compute attention weights over all other tokens in the sequence to determine which context is relevant. This enables parallelised training (unlike RNNs) and captures long-range dependencies. The architecture consists of encoder and decoder stacks (original), or just decoders (GPT-style language models), with positional encodings, layer normalisation, and feed-forward networks.

Key Use Cases

Large language models (ChatGPT, Claude, Gemini)
Machine translation
Code generation (GitHub Copilot)
Image recognition (Vision Transformers)
Protein structure prediction (AlphaFold)
Audio and speech processing
Multimodal understanding

Frequently Asked Questions

What is a Transformer in AI?: A Transformer is a neural network architecture that uses self-attention to process sequences in parallel. Introduced in 2017, it is the foundation of virtually all modern large language models including GPT-4, Claude, and Gemini.
Why were Transformers better than RNNs?: Transformers process entire sequences in parallel (vs. RNNs' sequential processing), making them much faster to train on modern GPU hardware. They also capture long-range dependencies more effectively using attention mechanisms.
What is self-attention?: Self-attention is a mechanism where each token in a sequence computes a weighted representation of all other tokens, allowing the model to understand context and relationships across the entire input simultaneously.