Understanding Transformers: The Backbone of Modern AI

— Issue #15 of The Artificial Newsletter

Hey AI Enthusiasts,

This week, let's take a deep dive into Transformers—the game-changing architecture behind models like GPT-4, BERT, and countless others dominating the AI space today. If you’ve ever wondered how machines understand context, predict text, and answer questions like a human, this is where the magic happens.

⚙️ What Exactly is a Transformer?

Introduced in a seminal paper by Vaswani et al. in 2017 titled "Attention Is All You Need," Transformers fundamentally shifted how AI models process information.
Before Transformers, models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory) handled sequences of data one step at a time. Transformers shattered this bottleneck by processing entire sequences in parallel—significantly boosting speed and context understanding.

🔍 How Does It Work? (Breaking Down the Magic)

1️⃣ Input Embeddings and Positional Encoding

  • The process begins with raw text that is transformed into Input Embeddings. These are vector representations of words.

  • Since Transformers process words in parallel, they need to understand the order. This is where Positional Encoding comes in. It assigns each word a unique position in the sequence.

2️⃣ Encoder Layers

  • The main processing of data happens in the Encoder Layers, which are stacked multiple times (6 layers in the original paper).

  • Each Encoder Layer consists of:

    • Self-Attention: This mechanism lets the model look at other words in the sentence to understand context.

    • Feed Forward Network: A dense neural network that transforms the data.

    • Norm & Add: Normalization and residual connections help stabilize training and preserve information.

3️⃣ Self-Attention Mechanism

  • This is the core innovation. Instead of reading text one word at a time, Transformers weigh the importance of every word relative to each other.

  • Example: In the sentence "The cat sat on the mat because it was tired," the model understands that "it" refers to "the cat" even if they are a few words apart.

  • This allows Transformers to understand complex dependencies and relationships across the text, even when they are separated by many words.

4️⃣ Encoder-Decoder Attention

  • The encoded representation is passed to the Decoder.

  • The Encoder-Decoder Attention step allows the decoder to focus on relevant parts of the input sentence during translation or text generation.

5️⃣ Decoder Layers

  • The Decoder is also stacked in layers similar to the encoder.

  • It consists of:

    • Masked Self-Attention: Prevents the model from "seeing" future words during training to avoid cheating.

    • Encoder-Decoder Attention: Focuses on the relevant encoded information.

    • Feed Forward Network: Similar to the encoder, it processes the information.

    • Norm & Add: Ensures information flow remains stable.

6️⃣ Output Probabilities

  • Finally, the decoder outputs a probability distribution over the vocabulary for each word position.

  • The highest probability word is chosen as the next word in the sequence during generation.

🚀 Why Are Transformers So Powerful?

  1. Parallel Processing: Faster training and better scalability.

  2. Longer Context Understanding: Can interpret relationships between words even if they are far apart.

  3. Multi-Task Learning: Fine-tuning for various tasks (e.g., translation, summarization, Q&A).

  4. Universal Architecture: The same model architecture works across domains—text, images, and even audio.

🔎 Real-World Applications:

  • GPT-4 & ChatGPT: Uses Transformers to generate human-like conversations.

  • BERT: Powers Google Search’s understanding of user queries.

  • DALL-E: Extends Transformer architecture to image generation.

  • Bloom & LLaMA: Open-source initiatives using Transformers for multilingual tasks.

💡 DIY Project:

If you’re feeling adventurous, try building your own mini-transformer to summarize news articles or auto-generate LinkedIn messages.
Tools to Explore:

  • transformers library by Huggingface

  • Google Colab for quick experiments

  • OpenAI API for GPT models

📌 Next Week's Preview:

Tuesday we will be coming up with another DIY project while Thursday’s will be dedictaed to doing some deep dive into AI topics.

Got questions? Want me to cover a specific part of the Transformer architecture? Hit reply and let me know!

Till then, keep building and keep innovating!

The Artificial Newsletter