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The goal C Builder

AI, ethics and society

Where AI turns up in everyday life, how it changes work, what responsible use means, and what disinformation, bias and manipulation look like.

Knowledge nodes
58
From zero
about 31 h
Labs
6
See what you already know — no account

The diagnostic removes what you already know, so your path is usually much shorter.

What you can do afterwards

Labs along the way

You write the code. Tests you cannot see decide whether it holds up.

Lab: a neural network in pure NumPy — forward, backprop, gradient checkDa sandbox · about 75 minLab: dot product, norm and cosine similarityDin the browser · about 40 minLab: gradient descent from scratchDin the browser · about 50 minLab: matrix multiplication and one layer of a neural networkDin the browser · about 45 minLab: scaled dot-product attention with a causal maskDa sandbox · about 60 minLab: semantic search with embeddingsDa sandbox · about 45 min

The whole path

Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map

AExplorer8 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
  3. Training a machine with examples
  4. What is a computer?
  5. Input and output
  6. Digital images and pixels
  7. Talking to an AI — what can it do, and what can't it?
  8. Write a story together with an AI
BInvestigator17 knowledge nodes
  1. Data in everyday life
  2. Personal data: what do apps collect about you?
  3. Algorithmic thinking
  4. Rule-based systems and machine learning
  5. Source criticism and responsibility in AI use
  6. Training data, features and labels
  7. Classification: how a model sorts information
  8. AI in everyday life — where is it?
  9. The technology behind the web: how a page is fetched
  10. Passwords and security
  11. How does a model recognise an object?
  12. Does the AI remember what I said?
  13. Generative AI: how it creates content
  14. Language models and probabilities
  15. AI's invented answers
  16. AI or a human being? Guess
  17. Whose picture is it? Copyright for children
CBuilder16 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Disinformation and synthetic content
  6. Statistics — mean, median and spread
  7. The data behind a recommendation
  8. Linear regression: fitting a straight line to data
  9. Neural networks — the intuition
  10. Prompting — steering a language model
  11. Responsible use of AI
  12. Facial recognition — what is acceptable?
  13. AI and the labour market
  14. Can an AI be fooled?
  15. Why did it answer that?
  16. Who decides over AI?
DAI developer13 knowledge nodes
  1. Derivatives and optimisation
  2. Bias and fairness in models
  3. Gradient descent
  4. Vectors
  5. Matrices and matrix multiplication
  6. Neural networks — the forward pass with matrices
  7. Backpropagation
  8. Embeddings — words as vectors
  9. Attention
  10. PyTorch — tensors and autograd
  11. Train a neural network in PyTorch
  12. Transformers — the architecture
  13. Energy and environmental impact
EUniversity4 knowledge nodes
  1. Multi-head attention in detail
  2. The KV cache
  3. Latency, throughput and batching in inference
  4. Cost modelling for LLM systems