Understanding GPT and Transformers

Introduction to GPT

• GPT: Generative Pretrained Transformer
  • Generative: These bots generate new text.
  • Pretrained: Model trained using a large dataset. Can be fine-tuned for specific tasks.
  • Transformer: A specific type of neural network, key to modern AI advancements.

Goal of the Video

• Provide a visually-driven explanation of transformers.
• Follow the data flow step-by-step.

Applications of Transformers

• Audio processing: Transcribing speech to text.
• Synthetic speech: Generating speech from text.
• Image generation: Tools like Dolly and Midjourney produce images from text descriptions.
• Text translation: The original transformer by Google (2017) was used for translating languages.
• Text prediction: Models like ChatGPT predict the next part of a passage, generating coherent text.

Text Prediction Process

1. Input Tokenization: Breaking input into tokens (words, parts of words, etc.).
2. Embedding Tokens: Associating tokens with vectors (list of numbers).
3. Attention Mechanism: Vectors pass through blocks updating their values based on context.
4. Feed-Forward Layers: Vectors processed in parallel, updated based on certain features.
5. Probability Distribution: Final vector predicts the next token, creating a probability distribution of possible next tokens.
6. Text Generation: Repeating the process to generate text piece by piece.

Details of Transformers

• Tokens: Small pieces of input (e.g., words or image patches).
• Vectors: List of numbers encoding token meanings. Words with similar meanings have close vectors.
• Attention Blocks: Allow vectors to update values based on other relevant tokens in the context.
• Feed-Forward Layers: Apply same operations in parallel to all vectors.
• Training: Learning optimal weights through data. Transformers greatly scale with data and parameters (like 175 billion in GPT-3).
• Matrix Multiplications: Core computation is matrix-vector multiplication.

Embeddings

• Embedding Matrix (We): Predefined vectors for each token in the vocabulary.
• Matrix Learning: Value begins randomly, then is optimized during training.
• High-Dimensional Space: Position in space represents word meaning. Directions encode semantics.

Example: Word Embeddings

• Taking differences in embeddings helps find relations (e.g., king + woman-man ≈ queen).
• Semantic Direction Embeddings: e.g., Italy - Germany + Hitler ≈ Mussolini.

Key Operations

• Dot Products: Measure how well vectors align, critical for next word predictions.
• Context Size: The fixed number of vectors processed at a time; GPT-3 uses 2048.
• Attention Mechanism: Heart of transformers, updates vectors with contextual meanings.
• Softmax Function: Transforms logits into probability distributions.
• Temperature: Affects randomness in text generation. High temp = more random, low temp = more predictable.

Final Notes

• Model Insights: Importance of separating weights (learned parameters) from data being processed.
• Unembedding Matrix (WU): Processes context-rich vectors to predict next tokens.

Embeddings Visualization

• Examples illustrating embedding intuition.
• Training aligns embeddings with their contextual semantic meaning.

Looking Ahead

• Next focus: in-depth attention mechanism, multi-layer perceptrons.
• Context: How transformers build intuition from basic machine learning principles.