Prática de Shadowing: AI & Machine Learning for Kids: Fun & Easy Introduction! (AI for Kids) | AI Courses | ML Education - Aprenda a falar inglês com vídeo
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Hi there, Jenny and Ethan.
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Welcome back to Randy's Robo School.
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Hi Randy.
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We're so happy to be here.
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Yeah Randy.
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We're excited to learn more about AI.
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What are we learning today?
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Today, we're going to learn about something really cool called machine learning.
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Ready to dive in?
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Ready.
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Machine learning is a special kind of AI where computers learn from data.
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It's like teaching a robot to do something by showing it lots of examples.
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Do you think you could teach a computer to recognize your favorite toy?
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How do computers learn from data?
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Great question Jenny.
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Imagine you're learning to recognize animals.
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You look at many pictures of cats and dogs, and you learn to tell them apart.
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Computers do the same thing, but they need lots and lots of pictures to learn from.
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So, can we help computers learn?
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Absolutely Ethan.
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Just like your brain learns from every book you read, computers learn from all the data they see.
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Think of machine learning as a student in school.
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The more it studies, the better it gets.
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It learns from data, which is like homework for computers.
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So, does the computer get better with practice, like we do? do?
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Exactly Ethan.
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Just like you get better at math or reading with practice, computers improve at their tasks by learning from more data.
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Imagine practicing your multiplication tables.
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The more problems you solve, the faster and more accurate you become.
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Can we see an example?
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Sure Jenny.
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Imagine you're sorting your toys by size and color.
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The more you practice, the faster you get.
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Computers do the same with data.
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There are different types of machine learning.
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Let's start with supervised learning.
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It's like having a teacher who gives you the right answers to learn from.
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How does that work?
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Imagine you have a bunch of pictures of animals, and each picture is labeled with the correct animal name.
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The computer uses these labeled pictures to learn how to recognize new pictures of animals.
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Each new picture helps it get better and more precise at identifying animals correctly.
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The more examples it sees, the smarter it becomes.
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Let's look at some more examples of supervised learning in real life.
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Yes, please.
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For supervised learning, think about voice recognition on your smartphone.
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When you speak into your phone, it understands what you're saying because it has been trained with many voice samples.
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These samples are recordings of people talking, each labeled with the correct words they are saying.
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By studying these labeled recordings, The smartphone learns to recognize words and sentences when you speak.
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This process helps the phone get better at understanding different voices and accents over time.
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What other examples are there?
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Another great example is facial recognition technology.
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The computer learns to recognize faces by being trained with lots of pictures of people's faces, each labeled with the person's name.
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Like, when our camera knows it's me or my sister?
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Yes, Ethan.
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Also, self-driving cars use supervised learning to recognize objects on the road.
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They are trained with thousands of images of cars, pedestrians, and road signs, all labeled correctly.
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So, when the car is driving, it can see and understand what's around it, helping it to make safe driving decisions by knowing where the other cars,
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people, and signs are.
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That's really cool.
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Any more examples?
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Sure.
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Another example is in medical diagnosis.
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Computers are trained with labeled images of X-rays to learn how to identify diseases like pneumonia or fractures.
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Wow, machine learning helps in so many ways.
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It sure does, Ethan.
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Remember, supervised learning is like having a teacher who helps computers learn from examples, just like your teacher helps you.
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I hope by now you have a very good understanding on supervised learning.
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Yes.
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Let's move on to the next type of machine learning, called unsupervised learning.
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In unsupervised learning, the computer tries to find patterns and group in data without any labels or guidance.
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So, the computer figures things out by itself?
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Exactly.
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It's like solving a puzzle without knowing the picture on the box.
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The computer looks for similarities and groups the data based on those.
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Like, sorting our toy cars by color or size, without anyone telling us how?
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Yes, Ethan.
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The computer groups similar things together, just like you would with your toys.
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Let's explore an example of unsupervised learning.
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Imagine we have a collection of blocks in various colors and sizes.
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If we instruct the computer to categorize these blocks without providing any specific guidelines,
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it will use unsupervised learning to analyze all the blocks and group them based on their similarities.
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So it finds the patterns all by itself?
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Exactly, Jenny.
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That's unsupervised learning.
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The computer figures out the groups based on what it sees, just like you would sort your toys.
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That's cool.
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It's like the computer is playing a sorting game.
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Yes, Ethan.
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And this kind of learning helps computers understand data and find patterns, even when we don't tell them what to look for.
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Let's look at some real-life examples of unsupervised learning.
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Yes, please.
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A good example is organizing photos on your computer or phone.
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The computer groups similar photos together, like all pictures of sunsets or all pictures of your pet, without telling it to you.
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Can we have more examples?
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Sure.
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For unsupervised learning, another example is market basket analysis used by supermarkets.
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They find patterns in items frequently bought together to organize their shelves and create combo deals.
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For example, it might find that customers who buy bread often buy butter too, so the store can place these items close to each other to boost sales.
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Remember, unsupervised learning is when the computer figures things out on its own, just like you sorting your toys without any help.
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I hope by now you have a very good understanding on unsupervised learning.
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Yes.
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Finally, there's reinforcement learning.
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In reinforcement learning, the computer learns by doing something and getting rewards or penalties.
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Like playing a video game and getting points for doing well?
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Exactly Jenny.
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The computer tries different actions and learns which ones give the best results by getting positive or negative feedback.
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Let's see an example.
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Imagine a robot trying to find its way through a maze.
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Each time it makes a correct turn, it gets a point.
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Each time it hits a wall, it loses a point.
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Over time, the robot learns the best path through the maze by trying different routes and learning from its mistakes.
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So, it's like when we get stars for good behavior at school?
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Yes.
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The computer learns from the feedback it receives, just like you learn from getting stars or points. That makes sense.
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Can we see one more example?
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Sure.
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Think about training a pet.
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When the pet does a trick correctly, it gets a treat. If not, it doesn't.
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Over time, the pet learns to do the trick to get the treat.
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That's reinforcement learning.
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Let's look at some real-life examples of reinforcement learning.
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Yes, please.
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A good example is recommendation systems, such as Netflix's Movie Suggestions.
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These systems improve their recommendations with increased user feedback, learning from viewing choices and the ratings users provide.
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That's really cool.
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It is.
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And video game AI is another example.
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Game characters learn to become better opponents by receiving rewards for winning and penalties for losing, making the game more challenging and fun.
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Wow, machine learning is everywhere.
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It sure is, Jenny.
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It makes our lives easier, safer, and more fun.
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Remember, reinforcement learning is when the computer learns from rewards and penalties, just like you learn from getting points in a game or stars at school.
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I hope you enjoyed learning about machine learning today.
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Remember, it's all about teaching computers to learn from data and get better over time.
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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 machine learning in action.
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I'm glad you enjoyed it.
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Keep being curious and exploring new things every day.
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See you next time at Randy's Robo School.
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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 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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Vocabulário e dicas de fala para esta lição
Esta aula de conversação de nível A2 usa o vídeo “AI & Machine Learning for Kids: Fun & Easy Introduction!”. As palavras que mais se repetem: learning, computer, learn, example, picture. Este vídeo tem 148 frases e 1451 palavras para praticar shadowing. A fala dura 10:56. O falante mantém um ritmo estável de cerca de 133 palavras por minuto, confortável para o shadowing. 84% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| label substantivo | /ˈleɪ.bəl/ | etiqueta, rótulo |
| toy substantivo | /tɔɪ/ | brinquedo |
| penalty substantivo | /ˈpɛn.əl.ti/ | pena |
| reward substantivo | /ɹɪˈwɔɹd/ | recompensa |
| feedback substantivo | /ˈfiːdˌbæk/ | comentário, avaliação |
| explore verbo | /ɪkˈsploɹ/ | explorar |
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| robot substantivo | /ˈɹoʊ.bɑt/ | robot, robô |
| correctly advérbio | /kəˈɹɛk(t).li/ | corretamente |
| trick substantivo | /ˈtɹɪk/ | artimanha, truque |
| recognition substantivo | /ˌɹɛkəɡˈnɪʃ(ə)n/ | reconhecimento |
| solve verbo | /sɒlv/ | resolver, solucionar |
| recommendation substantivo | /ˌɹɛkəmɛnˈdeɪʃən/ | recomendação |
| smartphone substantivo | /ˈsmɑɹtfoʊn/ | smartphone |
| organize verbo | /ˈɔɹɡənaɪz/ | organizar |
Phrasal verbs que você vai ouvir
| Palavra | Significado |
|---|---|
| figure out verbo | descobrir, deduzir |
Frases que vale a pena repetir
Frases curtas e completas do vídeo que você pode usar na conversa do dia a dia:
- We're so happy to be here.
- What are we learning today?
- Can we see an example?
- Can we have more examples?
- Can we see one more example?
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is labeled · has been trained · being trained |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | data, which is · teacher who gives · teacher who helps |
| Superlativos the -est / the most … — o maior ou o menor de um grupo | the best |
| Present continuous am/is/are + -ing — algo que está acontecendo agora ou nestes dias | are we learning · you're learning · you're sorting |
Pronúncia para ficar de olho
O falante usa 10 contrações e formas reduzidas, como we're, you're, can't. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “sh” e “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, facial /ˈfeɪ.ʃəl/, shelf /ʃɛlf/
- Palavras longas — acerte a sílaba tônica: artificial /ˌɑː.tɪˈfɪʃ.əl/, recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, similarity /ˌsɪm.ɪˈlæɹ.ɪ.ti/, mathematics /mæθ(.ə)ˈmæt.ɪks/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: reward /ɹɪˈwɔɹd/, feedback /ˈfiːdˌbæk/, robot /ˈɹoʊ.bɑt/, trick /ˈtɹɪk/, boost /buːst/
- /l/ final — a língua toca o céu da boca, não vira “u”: label /ˈleɪ.bəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, facial /ˈfeɪ.ʃəl/, puzzle /ˈpʌzəl/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como label, toy, penalty.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.