AI & Machine Learning Overview

The Hierarchy

Artificial Intelligence is the broadest category — any system that performs tasks requiring human-like reasoning. This includes everything from hand-coded rule engines to modern neural networks.

Machine Learning is the subset where systems learn patterns from data instead of being explicitly programmed. Three paradigms:

• Supervised Learning — labeled examples: "this image is a cat." Model learns to map inputs to known outputs.
• Unsupervised Learning — no labels. Model finds structure: clustering, dimensionality reduction, anomaly detection.
• Reinforcement Learning — trial and error with a reward signal. Agent takes actions in an environment and learns to maximize reward.

Deep Learning is ML using neural networks with many layers. This is what powers modern AI — image recognition, language models, speech synthesis.

Generative AI is the subset of deep learning that creates new content — text, images, audio, code. LLMs like ChatGPT and Claude are generative AI.

Why This Matters for Security

Every layer in this hierarchy introduces attack surface:

Understanding the ML pipeline isn't optional — it's the foundation for every attack and defense in this book.

Key Concepts

Parameters — the learned weights in a model. GPT-4 has ~1.8 trillion. Claude 3 Opus is estimated in the hundreds of billions. More parameters generally means more capability but also more compute cost.

Training — adjusting parameters by showing the model data and minimizing error. Uses backpropagation and gradient descent.

Inference — using the trained model to make predictions on new data. This is what happens when you send a message to ChatGPT.

Overfitting — the model memorized training data but can't generalize to new inputs. Relevant to training data extraction attacks.

Fine-tuning — taking a pre-trained model and training it further on a specific dataset. This is how base models become assistants.