init: Questionnaire+Mindmap

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--- a/.gitignore
+++ b/.gitignore
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+.idea/
--- a/map.json
+++ b/map.json
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\ No newline at end of file
--- a/questionnaire.md
+++ b/questionnaire.md
+# Intro
+**"Software is Eating the World"**, dit Marc Andreessen: aucune partie du monde n'est protégée de la révolution technologique en cours.  
+Vous le savez déjà, vous qui apprenez les secrets.
+
+Mais de plus en plus, "**Machine Learning** is eating the _Software_ World".  
+Entre l'utilisation du ML dans des logiciels de plus en plus nombreux,
+l'intégration de fonctionnalités "d'IA" dans les téléphones, le marketing de nombreux services qui prétendent mettre "l'Intelligence Artificielle au service de votre entreprise", **comment trier le vrai du faux** ?
+
+- Qu'est-ce que du Machine Learning ?  
+- Qu'est-ce que du **bon** Machine Learning ?  
+- Quels sont les **pires exemples** d'applications du ML qui ont mal tourné ?  
+
+Avec ce cours, vous aurez une expérience pratique du Machine Learning : choisir un modèle, l'entrainer, l'évaluer, l'utiliser.
+Vous découvrirez l'histoire du Machine Learning, ses victoires et ses échecs, sa grandeur passée et son avenir brillant.
+
+En attendant, j'aimerais vous poser quelques questions pour préparer nos prochains ateliers :
+
+1. Pour vous, c'est quoi du Machine Learning?
+2. Quelle est votre opinion sur l'utilisation du Machine Learning dans les logiciels?
+    - Enthousiaste : C'est une dinguerie, j'ai hâte d'en utiliser dans tous mes projets !
+    - Prudent : Ça a l'air cool, mais bon ça doit être compliqué de faire bien les choses
+    - Sceptique : C'est mort ça va jamais marcher
+    - Inquiet : "J'ai peur que les IAs me mettent au chômage / contrôlent l'attention du monde entier avec leurs recommendations / détruisent la planête et l'humanité en nous transformant en trombones, "Paperclip Maximizer"-style
+
+3. Avez vous déjà utilisé un service qui repose sur du Machine Learning ?
+   1. Qu-est-ce qui marchait bien? Avez-vous vu qqch qui marchait SUPER BIEN, qui vous a semblé limite magique ? (la "_unreasonable effectiveness_" dont [parle Karpathy](https://karpathy.github.io/2015/05/21/rnn-effectiveness/))
+   2. Qu'est-ce qui marchait mal, voire pas du tout ?
+4. Avez vous déjà développé du logiciel qui utilisait du Machine Learning ?
+   1. Qu-est-ce qui marchait bien? Avez-vous vu qqch qui marchait SUPER BIEN?
+   2. Qu'est-ce qui marchait mal, voire pas du tout ?
+
+5. Quel est le pire use-case de Machine Learning dont vous ayez entendu parler ?
+Quel est votre dream use-case? Si vous pouviez choisir une tâche de votre quotidien, n'importe laquelle, pour la faire faire par un algorithme, laquelle serait-ce ?
+
+6. Si vous avez déjà une expérience professionnelle, avez vous déjà vu :
+   1. Un projet qui en optimisant X, a fini par dégrader Y ?
+     > (Exemple : sur un site d'e-commerce, optimiser la page d'accueil pour maximiser le montant du panier moyen -> on force la main avec des produits absurdes ajoutés au panier -> deux mois plus tard on a tellement de retours clients que le revenu est plus mauvais qu'avant le projet d'optimisation du pfanier)
+   2. Un projet qui ajoute du Machine Learning quelque-part pour permettre au Marketing de dire que notre service est "AI-powered" ?
+   3. Un projet qui tente d'appliquer du Machine Learning à un endroit où ça ne fait aucun sens ?
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