Understand AI by watching it work.

Foundations

What AI is, and how machines learn at all.

• Artificial Intelligence (AI): Computers doing things that normally need a human brain, like understanding words or recognising faces.
• Machine Learning (ML): Teaching a computer by showing it lots of examples instead of writing step-by-step instructions.
• Model: The "brain file" an AI produces after learning. You give it an input and it gives back an answer.
• Training Data: The examples an AI learns from.
• Training: The learning phase: the AI makes guesses, gets told how wrong it was, and adjusts itself a little. Repeated millions of times.
• Inference: Using an already-trained AI to get an answer.
• Parameters: The adjustable knobs inside an AI. Training sets their values.
• Neural Network: A web of tiny maths units, loosely inspired by brain cells, that pass signals to each other.
• Deep Learning: Machine learning with neural networks that have many layers.
• Overfitting: When an AI memorises its practice examples instead of learning the general idea, so it fails on new ones.

Language Models

How ChatGPT-style tools read and write.

• Generative AI: AI that creates new things: text, images, music, video or code.
• Large Language Model (LLM): An AI trained on huge amounts of text that writes by predicting the next word, over and over.
• Token: The bite-sized pieces of text an AI reads and writes: often a word, sometimes part of a word.
• Context Window: How much text an AI can "see" at once: its short-term memory.
• Temperature: A setting for how adventurous or predictable the AI's word choices are.
• Prompt: What you type to ask the AI to do something.
• Prompt Engineering: The skill of writing instructions that get good results from AI.
• Hallucination: When AI confidently says something that sounds right but is made up.
• Transformer: The design behind modern language AIs. The "T" in GPT.
• Attention: How an AI decides which other words matter most when understanding each word.
• Multimodal: AI that handles more than one kind of input or output, such as text, images, audio and video.
• Diffusion Model: The kind of AI that makes images by starting with static noise and gradually cleaning it into a picture.

Building with AI

How products connect AI to your data and tools.

• Embedding: Turning words or documents into a list of numbers so that similar meanings end up close together.
• Vector Database: A database that finds things by similar meaning instead of exact words.
• Retrieval-Augmented Generation (RAG): Letting the AI look things up in your documents before it answers.
• Fine-Tuning: Giving an already-trained AI extra training on specific examples to specialise it.
• AI Agent: An AI that doesn't just answer, but takes actions step by step to complete a goal.
• Tool Use: Letting an AI use other software, such as a calculator, search engine or calendar.

Risks & Society

Where AI goes wrong and how we keep it in check.

• AI Bias: When an AI treats some people or groups unfairly because of patterns in its data or design.
• Alignment: Making sure AI does what people actually intend, and does it safely.
• Deepfake: Fake but realistic video, audio or images made with AI.
• Responsible AI: Building and using AI in ways that are fair, safe, transparent and respectful of people.

Guided paths

• Understand ChatGPT in 20 minutes: From "what is it?" to why it sometimes makes things up. (~20 min)
• How AI learns: Training, models, and why memorising isn't learning. (~15 min)
• AI at work: The vocabulary behind the AI products your company is buying. (~18 min)
• Stay sharp & safe: Spot the risks: bias, deepfakes and confident nonsense. (~10 min)