Prática de Shadowing: We’re already using AI more than we realize - Aprenda a falar inglês com vídeo
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Of all the interactions you have with technology in a day, interacting with artificial intelligence, or not, feels like a choice.
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But in some ways, it isn't.
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Over the past decade, we've become surrounded by AI systems that perceive our worlds, that support our decisions, and that mimic our ability to create.
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Whether we're aware of it or not is another story.
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Imagine a day like this.
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You do some exercise with a smartwatch, put on a suggested playlist, go to a friend's house and ring their camera doorbell, browse recommended shows on Netflix, check your spam folder for an email you've been waiting for,
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and when you can't find it, talk to a customer support chatbot.
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Each of those things are made possible by technologies that fall under the umbrella of artificial intelligence.
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But when a Pew survey asked Americans to identify whether each of those used AI or not, they only got it right about 60 % of the time.
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Some of these applications of AI have become fairly ubiquitous.
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They almost exist in the background and it's not terribly apparent to folks
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that the tools or services they're using are being powered by this technology.
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That's Alec Tyson, one of the researchers behind that Pew study.
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When Tyson and his team asked respondents how often they think they use AI, almost Most half didn't think they regularly interact with it at all.
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Some of them might be right, but most probably just don't know it.
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We know about 85 % of U .S adults are online every day, multiple times a day.
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Some folks are online almost all the time.
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This suggests a bit of a gap where there seem to be some folks who really must be interacting with AI, but it's not very salient to them.
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They don't perceive it.
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So why does that gap exist?
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Part of the problem is that the term artificial intelligence has been used to refer to a lot of different things.
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Artificial intelligence is totally this giant umbrella tent term that now has become a kitchen sink of everything.
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That's Karen Howe.
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She's a reporter who covers artificial intelligence and society.
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In the past, there were distinct disciplines about which aspect of the human brain do we want to recreate.
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Like, do we want to recreate the vision part?
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Do we want to recreate our ability to hear, our ability to write and speak?" Giving a machine the ability to see became the field of computer vision.
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Giving a machine the ability to write and speak became the field of natural language processing.
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But on their own, these tasks still required a machine to be programmed.
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If we wanted machines to recognize spam emails, we had to explicitly program them to look out for specific things, like poor spelling and urgent phrasing.
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That meant the tools weren't very adaptable to complex situations.
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But that all changed when we started recreating the brain's ability to learn.
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This became the subfield of machine learning, where computers are trained on massive amounts of data so
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that instead of needing to hand code rules about what to see or speak or write, the computers can develop rules on their own.
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With machine learning, a computer could learn to recognize new spam emails by reviewing thousands of existing emails
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that humans have labeled as spam.
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The machine recognizes patterns in this structured data and creates its own rules to help identify those patterns.
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When that training data hasn't been structured and labeled by humans, that method is called deep learning.
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Most of the time people talk about AI now, they're not talking about the whole field, but specifically these two methods.
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We'll hear more about that after a word from this video's sponsor.
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This episode is presented by Microsoft Copilot for Microsoft 365, your AI assistant at work.
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Copilot can help you solve your most complex problems at work, going far beyond simple questions and answers.
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From getting up to speed on a missed Teams meeting in seconds to helping you start a first draft faster in Word,
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Copilot for Microsoft 365 gives everyone an AI assistant at work in their most essential apps to unleash creativity,
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unlock productivity, and up -level skills.
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And it's all built on Microsoft's comprehensive approach to security, privacy, compliance, and responsible AI.
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Microsoft does not influence the editorial process of our videos, but they do help make videos like this possible.
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To learn more, you can go to Microsoft .com slash Copilot for Work.
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Now, back to our video.
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Improvements in computing power, together with the massive amounts of data generated on the internet, made possible a whole new generation of technologies that leveraged machine learning.
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And existing ones swapped out their algorithms for machine learning, too.
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A lot of the how in the back has been swapped into AI over time because people have realized,
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oh wait, we can actually get an even better performance of this product if we just swap our original algorithm,
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our original code, out for a deep learning model.
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Now, machine learning and deep learning models power recommendation for shows, music, videos.
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products, and advertisements.
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They determine the ranking of items every time we browse search results or social media feeds.
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They recognize images like faces to unlock phones or use filters, and the handwriting on remote deposit checks.
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They recognize speech in transcription, voice assistance, and voice -enabled TV remotes.
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And they predict text in auto -complete and auto -correct.
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But AI is seeping into more than that.
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There has been this tendency over the last 10 plus years where people have started putting AI into absolutely everything.
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Machine learning algorithms are already being used to decide which political ads we see, which jobs we qualify for, and whether we qualify for loans or government benefits,
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and often carry the same biases as the human decisions that preceded them.
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Are you actually automating the poor decision -making that happened in the past and just bringing it into the future?
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If you're going to use historical data to predict what's going to happen in the future, you're just going to end up with a future that looks like the past.
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And that's part of the reason why it matters to close
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that gap between those who knowingly interact with AI every day and those who don't quite know it yet.
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Awareness needs to grow for folks to be able to participate in some of these conversations about the moral and ethical boundaries, what AI should be used for and what it shouldn't be used for.
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Vocabulário e dicas de fala para esta lição
Este vídeo tem 69 frases e 1096 palavras para praticar shadowing. A fala dura 6:21. O falante fala em ritmo natural, cerca de 173 palavras por minuto, próximo de uma conversa do dia a dia. 82% 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 |
|---|---|---|
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| folk substantivo | /foʊk/ | povo |
| interact verbo | /ɪn.təˈɹækt/ | interagir |
| spam substantivo | /spæm/ | spam, lixo eletrônico |
| algorithm substantivo | /ˈælɡəɹɪðm̩/ | algoritmo |
| swap verbo | /ˈswɑp/ | trocar, intercambiar |
| label substantivo | /ˈleɪ.bəl/ | etiqueta, rótulo |
| remote adjetivo | /ɹəˈməʊt̞/ | remoto, longínquo |
| predict verbo | /pɹɪˈdɪkt/ | prever |
| automobile substantivo | /ˈɔː.tə.məˌbil/ | automóvel, carro |
| unlock verbo | /ʌnˈlɑk/ | destrancar |
| umbrella substantivo | /ʌmˈbɹɛl.ə/ | guarda-chuva, guarda-sol |
| perceive verbo | /pɚˈsiv/ | perceber, entender |
| browse verbo | /bɹaʊz/ | observar |
| moral adjetivo | /ˈmoɹəl/ | moral |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | we've become surrounded · have become · has been |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | are made · being powered · be programmed |
Pronúncia para ficar de olho
O falante usa 17 contrações e formas reduzidas, como don't, they're, you're. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: algorithm /ˈælɡəɹɪðm̩/, ethical /ˈɛθɪkəl/
- Os sons de “sh” e “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, interaction /ˌɪn.təˈɹæk.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, transcription /tɹænˈskɹɪpʃən/, unleash /ʌnˈliʃ/
- Palavras longas — acerte a sílaba tônica: artificial /ˌɑː.tɪˈfɪʃ.əl/, automobile /ˈɔː.tə.məˌbil/, participate /pɑːˈtɪs.ɪ.peɪt/, comprehensive /ˌkɑm.pɹəˈhɛn.sɪv/, interaction /ˌɪn.təˈɹæk.ʃən/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como artificial, folk, interact.
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.