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Applied AI · Beginner 🟢 · Session 3
✏️ Exercises
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Exercises — Session 3: The language of machines 🟢

Program : Applied AI — Beginner Level · Instructor: Yann Isola Material : paper + pencil. For exercise 3: the interactive page of the session or a chatbot.


Exercise 1 — The human tokenizer ✂️ (15 min)

The AI ​​cuts the text into tokens : pieces of words. Today, YOU are the tokenizer!

Part A — Cut like a machine

Cut each word into “logical” pieces, like an AI would do. Rules of the game: frequent pieces (prefixes, suffixes, small common words) remain intact; rare words get cut out.

Word Your cutting Number of tokens
cat ______________________ ____
chatbot ______________________ ____
intelligence ______________________ ____
Today ______________________ ____
incredibly ______________________ ____
unconstitutionally ______________________ ____
smurfology ______________________ ____

Part B — Questions for reflection

  1. Which word cost you the most tokens? Why, in your opinion?
  2. Why doesn't the AI ​​just give EVERY word in the dictionary a number? (Hint: How many forms does the verb “to be” have? What about invented words?)
  3. Did your neighbor cut out the same as you? What does this tell us about different AI models?

Part C — LEGO Challenge 🧱

With these 6 tokens: re + faire + + mont + er + able How many French words can you make by combining them? (Examples to get started: redo , disassemble …)

💡 To remember: tokens are reusable bricks. This is what allows AI to process even words it has never seen.

Answer key (for the teacher)

Part A — cutouts plausible (⚠ each model has its own, accept any consistent answer):

Part B : 1. Rare/invented words cost more. 2. Too many possible forms (conjugations, plurals, new words): the vocabulary would explode; the pieces recombine. 3. Different cuts = normal, each model learns its own cut.

Part C : redo, dismantle, reassemble, assemble, dismantle, reassemble, make, assemble… (accept any valid word).


Exercise 2 — Hallucination hunter 🕵️ (15 min)

An AI answered the questions below. Each answer contains at least one hallucination (invented or false information) slipped among true information. It's up to you to flush them out!

Answer #1 — History

“The Eiffel Tower was built for the 1889 Universal Exhibition by Gustave Eiffel. It measures approximately 330 meters and was painted blue-white-red for the 1924 Olympic Games. It was to be dismantled after 20 years. »

TRUE or invented ❌? Underline the suspicious passage(s): ____________________

Answer #2 — Sciences

“The Moon is the natural satellite of the Earth. It is approximately 384,000 km from us. Its hidden side is home to the Poincaré-Dumas crater, discovered in 1897 by the French astronomer Élise Chantereine using the first liquid mirror telescope. »

Suspicious passage(s): ____________________

Answer #3 — Literature

“Antoine de Saint-Exupéry wrote The Little Prince , published in 1943. He also wrote Night flight and the famous novel Desert Stars , who received the Goncourt prize in 1938.

Suspicious passage(s): ____________________

Questions for reflection

  1. What made these hallucinations difficult to spot? (Hint: look at the level of detail, the tone…)
  2. Name 2 reflexes to check an AI response.
  3. Why does AI invent instead of saying “I don’t know”?

Answer key (for the teacher)

Reflection : 1. The true/false mix + the precise details (names, dates) + the assured tone. 2. Check elsewhere, ask for sources and check them, be wary of being too precise, cross-reference several sources. 3. It calculates the most text plausible , not the most TRUE ; “I don’t know” is rarely the most likely outcome.


Exercise 3 — The temperature experiment 🌡️ (20 min)

There temperature regulates the randomness of the answers: low = wise and predictable, high = creative and surprising.

Material

The interactive session page (“Temperature Lab” tab), or a real chatbot giving it style instructions.

Protocol

Step 1. Choose ONE starting question:

Step 2. Get 3 answers to the same question:

Fashion On the interactive page With a real chatbot, ask…
🧊 Cold (T ≈ 0) Cursor left “Answer in the most classic and predictable way possible. »
🌤️ Medium (T ≈ 1) Slider in the middle “Answer normally. »
🔥 Hot (T ≈ 2) Cursor right “Answer in a very original, surprising, daring way. »

Step 3. Complete the observation table:

🧊 Cold 🌤️ Average 🔥 Hot
Was the response predictable? (0–10)
Are there any surprising ideas?
Does the answer remain consistent?
My pleasure rating (0–10)

Step 4. Questions for reflection:

  1. For translate a text , what temperature would you choose? For what ?
  2. For invent birthday party ideas , what temperature? For what ?
  3. What did you notice in very hot 🔥 mode? When does creativity become nonsense?
  4. Bonus: repeat the same question twice in 🧊 cold mode. Are the answers identical or almost identical? And in 🔥 fashion?

Answer key (for the teacher)

  1. Low: for a translation we want fidelity, not fantasy.
  2. High (or medium-high): we are looking for varied and original ideas.
  3. In very hot mode, the answers go in all directions and can lose all coherence: too much chance breaks the logical thread.
  4. In cold: almost identical answers (the most likely token almost always wins). Hot: very different answers each time.

💡 To remember: there is no absolute “good” temperature — there is the right temperature for a given task . ⚠ Most consumer chatbots don't allow you to adjust the temperature directly, but the style guidelines produce a comparable effect.