Pratique du Shadowing: AI Algorithms Types| Fun Learning with Randy the Robot | Part-2 | Advanced AI for Kids | Courses - Apprendre l'anglais à l'oral avec la vidéo
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Welcome back Jenny and Ethan.
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Ready to continue our journey into the world of AI algorithms?
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Ready.
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Great.
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Last time, we talked about something super cool, AI algorithms.
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We learned about the different steps that AI follows to solve problems, just like how we follow steps to solve a puzzle.
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We also discussed machine learning, which is a special kind of AI that learns from examples.
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In case you missed AI algorithm part 1, make sure to click the link in the description below to catch up.
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Now, let's talk about the different types of AI algorithms.
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There are many kinds of AI algorithms, and each one is like a special tool designed to solve different kinds of problems.
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The common ones are decision trees, neural networks, clustering, linear regression, and k-nearest neighbors.
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Can you show us how some of these work?
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Absolutely Jenny.
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Let's explore each one with a fun example.
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A decision tree is like a flowchart that helps make decisions.
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Let's say we want to decide what activity to do.
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If it's sunny, we might play outside.
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If it's rainy, we might read a book.
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If it's evening, we might watch a movie.
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A decision tree has branches that represent different choices or questions.
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Each branch leads to another question or a final decision, much like choosing different paths based on your answers.
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Let's see an example.
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Imagine you have a big question.
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What snack should I have?
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We'll use a decision tree to figure it out.
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Here's how it works.
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Big question.
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What snack should I have?
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First, we ask.
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Is it sweet or salty?
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If you choose sweet, the next question is, do you want something cold?
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If you say yes, you choose ice cream.
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If you say no, you choose chocolate.
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But if you choose salty, the next question is, Do you want something crunchy?
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If you say yes, you choose chips.
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And if you say no, you choose pretzels.
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So, it's like choosing what to do step by step?
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Exactly Ethan.
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Each question in the decision tree helps us make the final decision.
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In real life, decision trees are used by doctors to diagnose illnesses.
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They ask a series of questions about your symptoms to decide what illness you might have and the best treatment.
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This is an example of supervised learning, where the algorithm learns from labeled data.
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Next, we have neural networks.
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Neural networks are designed to work like the human brain, with nodes, or neurons, connected by pathways.
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Neural networks learn by adjusting the connections between nodes based on the data they receive.
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It's like how your brain strengthens connections when you practice something.
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Can we see an example?
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Sure.
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Imagine a neural network learning to recognize handwriting.
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It looks at many examples of letters and learns to identify patterns to correctly recognize each letter.
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In real life, neural networks are used in facial recognition technology.
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Your phone can unlock by recognizing your face because of a neural network that has learned from many photos of different faces.
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This is also an example of supervised learning.
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Next AI algorithm we have is clustering.
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Clustering is an algorithm that groups similar items together.
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Let's play a game.
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We have a bunch of colored balls.
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Let's group them by color.
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So, clustering is like sorting things into groups?
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Exactly Ethan.
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The algorithm looks at the features of each item and groups similar ones together.
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In real life, clustering is used by streaming services to recommend movies.
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They group users with similar watching habits to suggest shows and movies that others in the same group have liked.
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This is an example of unsupervised learning, where the algorithm finds patterns without labeled data.
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Now, let's talk about linear regression.
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Linear regression is used to find the relationship between two things.
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Imagine you want to know how your height changes as you grow older.
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We plot your age and height on a graph and draw a line that best fits the points.
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So the line shows how height changes with age?
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Yes Jenny, the line helps us predict future heights based on the pattern it finds in the data.
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In real life, linear regression is used by economists to predict things like future sales based on past data.
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They can predict how much of a product will be sold next month based on sales data from previous months.
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This is another example of supervised learning.
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Now, let's talk about K-nearest neighbors.
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K-nearest neighbors is an algorithm that classifies items based on their similarity to others.
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Let's classify some fruits.
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We have apples, bananas, and oranges, each with different features like color, size, and shape.
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How does it work?
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The algorithm looks at the features of a new fruit and finds the most similar ones in its data.
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It then classifies the new fruit based on the majority of its neighbors.
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For example, if most of the neighbors are apples, it classifies the new fruit as an apple.
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In real life, K-nearest Neighbors is used in things like online stores to recommend products.
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If you like certain toys, the algorithm looks at what other kids who like those toys have bought and recommends similar toys to you.
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This is a type of supervised learning.
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Thanks Randy!
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This was so interesting.
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Yeah, I can't wait to see more AI algorithms in action.
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We have a range of fascinating topics lined up for our upcoming videos, including AI Deep Learning, AI Safety,
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Coding in AI, Natural Language Processing, NLP, and much more.
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Stay tuned for exciting content.
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To learn more about artificial intelligence go to www.aieducationforkids.com.
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If you would like to read books
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and do some activity then we have some wonderful collection of books on artificial intelligence like Introduction to AI,
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Brainy Bots, Fantastic Future, Building AI.
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These books will help you grow and explore the new world of artificial intelligence.
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You can find all the links and details in the video description below.
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If you like this video then please click the like button.
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To get notifications for more such educational videos on AI, please hit the subscribe button.
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See you next time.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau A2 s’appuie sur la vidéo « AI Algorithms Types ». Les mots qui reviennent le plus souvent : algorithm, example, decision, learning, neural. Cette vidéo contient 99 phrases et 1032 mots à répéter en shadowing. La partie parlée dure 7:42. Le locuteur parle à un rythme régulier d’environ 134 mots par minute, confortable pour le shadowing. 81 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| algorithm nom | /ˈælɡəɹɪðm̩/ | algorithme |
| neural adjectif | /ˈnʊɹəl/ | neural |
| cluster nom | /ˈklʌstɚ/ | groupe |
| linear adjectif | /ˈlɪn.i.əɹ/ | linéaire |
| solve verbe | /sɒlv/ | résoudre, régler |
| toy nom | /tɔɪ/ | jouet |
| artificial adjectif | /ˌɑː.tɪˈfɪʃ.əl/ | artificiel |
| predict verbe | /pɹɪˈdɪkt/ | prédire |
| label nom | /ˈleɪ.bəl/ | étiquette, libellé |
| illness nom | /ˈɪl.nəs/ | maladie |
| explore verbe | /ɪkˈsploɹ/ | explorer |
| snack nom | /snæk/ | casse-croûte m or casse-croute, hors-d'œuvre |
| salty adjectif | /ˈsɔlti/ | salé |
| topic nom | /ˈtɑpɪk/ | sujet, thème |
| recognition nom | /ˌɹɛkəɡˈnɪʃ(ə)n/ | reconnaissance |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- Can you show us how some of these work?
- Let's explore each one with a fun example.
- If it's sunny, we might play outside.
- What snack should I have?
- Can we see an example?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | has learned · have liked · have bought |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | are designed · will be sold |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | learning, which is · kids who like |
| Possibilité avec « might / may » might/may + verbe — quelque chose de possible mais pas certain | might play · might read · might watch |
Prononciation à surveiller
- Les sons « th »: algorithm /ˈælɡəɹɪðm̩/, strengthen /ˈstɹɛŋ(k)θən/, pathway /ˈpæθˌweɪ/
- Les sons « sh » et « zh »: artificial /ˌɑː.tɪˈfɪʃ.əl/, recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, facial /ˈfeɪ.ʃəl/, notification /ˌnoʊtɪfɪˈkeɪʃn̩/, regression /ɹiːˈɡɹɛʃ.ən/
- Mots longs — placez bien l’accent: artificial /ˌɑː.tɪˈfɪʃ.əl/, recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, notification /ˌnoʊtɪfɪˈkeɪʃn̩/, economist /iˈkɑ.nə.mɪst/, similarity /ˌsɪm.ɪˈlæɹ.ɪ.ti/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: habit /ˈhæb.ɪt/, handwriting /ˈhændɹaɪtɪŋ/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: chip /t͡ʃɪp/, adjust /əˈd͡ʒʌst/, crunchy /ˈkɹʌnt͡ʃi/
- /r/ anglais — langue recourbée, sans frotter la gorge: algorithm /ˈælɡəɹɪðm̩/, neural /ˈnʊɹəl/, linear /ˈlɪn.i.əɹ/, predict /pɹɪˈdɪkt/, explore /ɪkˈsploɹ/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme algorithm, neural, cluster.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.