01 — What is AI actually doing?
Time: 2 h. Prerequisites: none. Milestone: explain the difference between classical search, machine learning, and a language model.
Teaching storyline
- AI includes rule-based systems, search, planning, machine learning and neural networks; not every AI problem uses an LLM.
- A neural network is a parametrized function. Training changes those parameters. Inference computes outputs using existing parameters.
- Language models turn text into tokens, evaluate probabilities for the next token, and repeatedly sample/select tokens.
- Fluent output is not a reliability guarantee. Distinguish models' abilities from the surrounding application's checks.
Watch (pause and discuss)
- 3Blue1Brown — But what is a Neural Network? (~19 min) video / illustrated article.
- Optional Andrej Karpathy — Intro to Large Language Models video, especially 00:00–18:00; security segment later.
- Slides: course presentation, chapter 1.
Lab: same task, different strategies
Ask: "Is the number 34 even?" A two-line Python rule answers exactly; a text model is overkill. Compare with "Explain parity to a 10-year-old": an LLM might be useful, but the answer needs checking.
Open rule_bot.py after learning the minimal Python syntax; use the browser for discussions now.
Check understanding
- What changes when an LLM is trained? What changes when you set temperature?
- Can a Tetris solver use search instead of a trained network?
- If the LLM says 17 × 19 = 324, is that sufficient evidence?
Exit ticket (2 minutes): Imagine asking a chatbot, “Why is the sky blue?” Put these three cards into the order in which they participate: You, chat app, language model. Say who types the question, who generates the response, and who displays it. No drawing and no Python knowledge are needed. Example answer: You type → chat app sends the question → language model produces words → chat app displays the words to you. For later: Ollama is the program that runs the language model on the Mac. A rule-based chatbot can use if/else instead of a model.
Next: Mac editor + terminal workbench, then Python foundations.