What are Transformers?

In 2017, Google researchers published a paper with an audacious title: "Attention Is All You Need."

They introduced a new neural networkNeural NetworkA computing system inspired by biological brains, made of interconnected nodes that learn patterns from data.Click to learn more → architecture called the Transformer. It became the foundation for ChatGPT, GPT-4, Claude, Gemini, and almost every major AI breakthrough since.

Here's why Transformers changed everything.

The problem they solved

Before Transformers, AI processed text sequentially, word by word, like reading a book from left to right. This created problems:

Slow processing: You had to finish processing "The cat" before you could start on "sat on the mat."

Limited memory: By the time the AI got to "mat," it had mostly forgotten about "cat."

Missing long-range relationships: The AI struggled to connect words that were far apart in a sentence.

Transformers threw out sequential processing entirely. Instead of reading word by word, they look at all words simultaneously and figure out how they relate to each other.

The breakthrough: Attention

The core innovation is called the attention mechanism. It's like having a spotlight that can focus on different parts of the input at the same time.

When processing the word "it" in a sentence, the attention mechanism can look back and figure out what "it" refers to, even if it's many words away.

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   SENTENCE: "The cat sat on the mat because it was warm"    │
│                                                             │
│   When processing "it":                                     │
│                                                             │
│   The  cat  sat  on  the  mat  because  it  was  warm      │
│    │    │    │    │    │    │      │     ▲    │    │       │
│    │    ▼────────────────────────────────┘    │    │       │
│    │                                          │    │       │
│   Low attention              High attention   │    │       │
│                                              │    │       │
│   The AI figures out "it" refers to "mat"    │    │       │
│                                              │    │       │
└─────────────────────────────────────────────────────────────┘

How Transformers work

Step 1: Convert to Numbers

First, words become embeddingsEmbeddingConverting text into numbers (vectors) that capture meaning, so similar concepts are close together.Click to learn more → (vectors of numbers) plus positional information so the AI knows word order.

Step 2: Self-Attention

Each word looks at every other word and computes how much attention to pay to each one. This creates relationships between all words simultaneously.

Step 3: Multiple Attention Heads

The Transformer doesn't just have one attention mechanism. It has multiple "heads" that focus on different types of relationships:

• Head 1 might focus on grammatical relationships
• Head 2 might focus on semantic meaning
• Head 3 might focus on long-range dependencies

Step 4: Feed Forward

After attention, each word representation passes through a neural network that transforms it based on all the attention it gathered.

Step 5: Stack and Repeat

Modern Transformers repeat this process many times. GPT-4 has 120+ layers of attention and processing.

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   INPUT: "The cat sat on the mat"                          │
│                                                             │
│   ┌─────────────────────────────────────────────────────┐   │
│   │              TRANSFORMER LAYER                      │   │
│   │                                                     │   │
│   │   Multi-Head Attention ──► Feed Forward Network     │   │
│   │         │                        │                  │   │
│   │         └── Looks at all words   └── Processes      │   │
│   │             simultaneously           relationships  │   │
│   └─────────────────────────────────────────────────────┘   │
│                           │                                 │
│                           ▼                                 │
│   ┌─────────────────────────────────────────────────────┐   │
│   │              TRANSFORMER LAYER                      │   │
│   │   Multi-Head Attention ──► Feed Forward Network     │   │
│   └─────────────────────────────────────────────────────┘   │
│                           │                                 │
│                         ...                                 │
│                    (repeat 12-120 times)                    │
│                           │                                 │
│                           ▼                                 │
│   OUTPUT: Rich representation of sentence meaning          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Why attention changed everything

Parallelization

Unlike previous approaches that processed words sequentially, Transformers can process all words simultaneously. This makes training much faster with modern GPUsGPUGraphics Processing Unit — specialized chips that excel at parallel computations needed for AI.Click to learn more →.

Long-range understanding

Attention can connect words that are far apart. The AI can understand complex sentences with nested clauses and distant references.

Contextual representations

The same word gets different representations depending on context. "Bank" gets one representation in "river bank" and a different one in "savings bank."

Scalability

Transformers scale incredibly well. Bigger models with more parameters generally work better, leading to the rise of large language models.

Types of Transformers

Encoder-Only (BERT-style)

Good for understanding text: classification, search, sentiment analysis. They read the whole text and create rich representations.

Decoder-Only (GPT-style)

Good for generating text: writing, conversation, completion. They predict the next word based on previous words.

Encoder-Decoder (T5-style)

Good for translation and summarization. They encode input text and decode it into different output text.

What makes them so powerful

Scale

Modern Transformers are massive. GPT-3 has 175 billion parameters. GPT-4 has even more. This scale enables complex reasoning and broad knowledge.

Training on everything

They're trained on massive amounts of text from the internet, books, and articles. This gives them broad knowledge across many domains.

Emergent abilities

As Transformers get bigger, they develop abilities that weren't explicitly programmed: reasoning, planning, coding, even basic math.

Transfer learning

A single pre-trained Transformer can be adapted for many tasks: writing, analysis, coding, translation, and more.

The challenges

Computational requirements: Training large Transformers requires enormous amounts of compute and energy.

Memory usage: The attention mechanism scales quadratically with sequence length. Longer texts require exponentially more memory.

Interpretability: It's hard to understand why a Transformer makes specific decisions. The attention patterns are complex and often non-intuitive.

Training stability: Large Transformers can be finicky to train. Small changes in setup can lead to training failures.

Beyond language

While Transformers started with text, they've expanded to other domains:

Vision Transformers (ViTs): Process images by treating image patches like words in a sentence.

Multimodal Transformers: Handle text, images, and audio together in one model.

Protein folding: DeepMind's AlphaFold uses Transformer-like architectures to predict protein structures.

Code generation: GitHub Copilot and other coding AIs use Transformer architectures.

The Transformer revolution

Transformers didn't just improve existing AI. They enabled entirely new capabilities:

• Conversational AI: ChatGPT's ability to maintain context across long conversations
• Few-shot learning: Learning new tasks from just a few examples
• Chain-of-thought reasoning: Breaking down complex problems step by step
• In-context learning: Learning and applying patterns within a single conversation

Why Transformers matter: They're not just a better way to process text. They're a general-purpose architecture for finding patterns in sequences. Whether it's words in sentences, pixels in images, or amino acids in proteins, Transformers can learn the relationships that matter.

The bottom line: Transformers changed AI from narrow, task-specific tools to general-purpose reasoning engines. They're the foundation of the AI revolution we're experiencing today, and they're likely to power the next generation of AI breakthroughs.

Transformers process sequences with attention. But they're part of a broader field focused on human language. Next: What is Natural Language Processing?, the discipline that teaches machines to understand human communication.