Pratique du Shadowing: We’re already using AI more than we realize - Apprendre l'anglais à l'oral avec la vidéo
Création de la leçon...
1
Of all the interactions you have with technology in a day, interacting with artificial intelligence, or not, feels like a choice.
2
But in some ways, it isn't.
3
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.
4
Whether we're aware of it or not is another story.
5
Imagine a day like this.
6
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,
7
and when you can't find it, talk to a customer support chatbot.
8
Each of those things are made possible by technologies that fall under the umbrella of artificial intelligence.
9
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.
10
Some of these applications of AI have become fairly ubiquitous.
11
They almost exist in the background and it's not terribly apparent to folks
12
that the tools or services they're using are being powered by this technology.
13
That's Alec Tyson, one of the researchers behind that Pew study.
14
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.
15
Some of them might be right, but most probably just don't know it.
16
We know about 85 % of U .S adults are online every day, multiple times a day.
17
Some folks are online almost all the time.
18
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.
19
They don't perceive it.
20
So why does that gap exist?
21
Part of the problem is that the term artificial intelligence has been used to refer to a lot of different things.
22
Artificial intelligence is totally this giant umbrella tent term that now has become a kitchen sink of everything.
23
That's Karen Howe.
24
She's a reporter who covers artificial intelligence and society.
25
In the past, there were distinct disciplines about which aspect of the human brain do we want to recreate.
26
Like, do we want to recreate the vision part?
27
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.
28
Giving a machine the ability to write and speak became the field of natural language processing.
29
But on their own, these tasks still required a machine to be programmed.
30
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.
31
That meant the tools weren't very adaptable to complex situations.
32
But that all changed when we started recreating the brain's ability to learn.
33
This became the subfield of machine learning, where computers are trained on massive amounts of data so
34
that instead of needing to hand code rules about what to see or speak or write, the computers can develop rules on their own.
35
With machine learning, a computer could learn to recognize new spam emails by reviewing thousands of existing emails
36
that humans have labeled as spam.
37
The machine recognizes patterns in this structured data and creates its own rules to help identify those patterns.
38
When that training data hasn't been structured and labeled by humans, that method is called deep learning.
39
Most of the time people talk about AI now, they're not talking about the whole field, but specifically these two methods.
40
We'll hear more about that after a word from this video's sponsor.
41
This episode is presented by Microsoft Copilot for Microsoft 365, your AI assistant at work.
42
Copilot can help you solve your most complex problems at work, going far beyond simple questions and answers.
43
From getting up to speed on a missed Teams meeting in seconds to helping you start a first draft faster in Word,
44
Copilot for Microsoft 365 gives everyone an AI assistant at work in their most essential apps to unleash creativity,
45
unlock productivity, and up -level skills.
46
And it's all built on Microsoft's comprehensive approach to security, privacy, compliance, and responsible AI.
47
Microsoft does not influence the editorial process of our videos, but they do help make videos like this possible.
48
To learn more, you can go to Microsoft .com slash Copilot for Work.
49
Now, back to our video.
50
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.
51
And existing ones swapped out their algorithms for machine learning, too.
52
A lot of the how in the back has been swapped into AI over time because people have realized,
53
oh wait, we can actually get an even better performance of this product if we just swap our original algorithm,
54
our original code, out for a deep learning model.
55
Now, machine learning and deep learning models power recommendation for shows, music, videos.
56
products, and advertisements.
57
They determine the ranking of items every time we browse search results or social media feeds.
58
They recognize images like faces to unlock phones or use filters, and the handwriting on remote deposit checks.
59
They recognize speech in transcription, voice assistance, and voice -enabled TV remotes.
60
And they predict text in auto -complete and auto -correct.
61
But AI is seeping into more than that.
62
There has been this tendency over the last 10 plus years where people have started putting AI into absolutely everything.
63
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,
64
and often carry the same biases as the human decisions that preceded them.
65
Are you actually automating the poor decision -making that happened in the past and just bringing it into the future?
66
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.
67
And that's part of the reason why it matters to close
68
that gap between those who knowingly interact with AI every day and those who don't quite know it yet.
69
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.
📺 Même chaîne
B21
How bite marks made one man a murder suspect | Part 1
IELTS PrepTOEFL PrepUniversity Students
B22
How Vladimir Putin won Republicans' approval
IELTS PrepTOEFL PrepUniversity Students
B28
How to solve problems like a designer
Business EnglishUniversity StudentsWorking Adults
B2Why Latino voters have turned on Trump | America, Actually
Business EnglishDaily ConversationUniversity Students
B26
Five things you need to know for the 2026 midterms | America, Actually
Business EnglishDaily ConversationUniversity Students
B24
Why are weddings so damn expensive?
Business EnglishDaily ConversationUniversity Students
B21
Are headphones destroying our hearing?
Daily ConversationUniversity StudentsWorking Adults
C11
How to be less neurotic | The Gray Area
Daily ConversationUniversity StudentsWorking Adults
B22
The origin of the '80s aesthetic
Daily ConversationUniversity StudentsWorking Adults
B217
Why “Buy one, get one free” isn’t a great deal
Business EnglishWorking Adults
B22
How F1 drivers survive their own cars
Business EnglishUniversity StudentsWorking Adults
B210
Why 350°F is the magic number for baking
Daily ConversationUniversity StudentsWorking Adults
B26
How did pink become a girly color?
Daily ConversationUniversity StudentsWorking Adults
B22
The real reason Boeing's new plane crashed twice
Business EnglishDaily ConversationUniversity Students
B21
Food waste is the world's dumbest problem
Business EnglishDaily ConversationUniversity Students
B21
Why AI art struggles with hands
Daily ConversationUniversity StudentsWorking Adults
B22
How the rich avoid paying taxes
Business EnglishDaily ConversationUniversity Students
B2What's safe to eat now?
Business EnglishDaily ConversationUniversity Students
B25
Zohran Mamdani on his first priorities as NYC mayor (Full video interview)
Business EnglishDaily ConversationUniversity Students
B210
Why we say “OK”
University StudentsWorking Adults
✨ Vidéo recommandée
B2154 phrases
Are We Getting Too Comfortable With Claude?
Business EnglishDaily ConversationUniversity Students
B11
53 phrases
What Is AI? | Learn all about artificial intelligence
Business EnglishDaily ConversationUniversity Students
B22
252 phrases
it’s boring, but it’s what bursts the ai bubble
Business EnglishDaily ConversationUniversity Students
B22
78 phrases
5 Things You Should Never Ask ChatGPT | Vantage with Palki Sharma
Business EnglishDaily ConversationUniversity Students
B2251 phrases
Is Siri AI Usable for English Learning?
Business EnglishDaily ConversationUniversity Students
B23
159 phrases
AI Is Stealing From You
Business EnglishDaily ConversationUniversity Students
B21
112 phrases
‘We don’t have months’: Bernie Sanders sounds alarm on AI's 'extinction' threat
Business EnglishDaily ConversationUniversity Students
B22
281 phrases
Virtual love - How dangerous are AI relationships? | DW Documentary
Business EnglishDaily ConversationUniversity Students
B21
386 phrases
Communicating the Future: Defining Where We Want AI To Take Us
Business EnglishDaily ConversationUniversity Students
B22
649 phrases
Nvidia’s Jensen Huang on the AI revolution, job losses and what drives him | Full interview
Business EnglishDaily ConversationUniversity Students
B22
43 phrases
AI is changing the world, but is it also trying to kill us? @RonnyChieng investigates #DailyShow
Business EnglishDaily ConversationUniversity Students
B21
147 phrases
Could AI Become Dangerous? The Real Risks Explained
Business EnglishDaily ConversationUniversity Students
Vocabulaire et conseils d’expression pour cette leçon
Cette vidéo contient 69 phrases et 1096 mots à répéter en shadowing. La partie parlée dure 6:21. Le locuteur parle à un rythme naturel d’environ 173 mots par minute, proche d’une conversation courante. 82 % 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 |
|---|---|---|
| artificial adjectif | /ˌɑː.tɪˈfɪʃ.əl/ | artificiel |
| folk nom | /foʊk/ | peuple |
| interact verbe | /ɪn.təˈɹækt/ | interagir |
| spam nom | /spæm/ | spam, pourriel |
| recreate verbe | /ˈɹɛk.ɹi.eɪt/ | se réjouir |
| algorithm nom | /ˈælɡəɹɪðm̩/ | algorithme |
| swap verbe | /ˈswɑp/ | permuter |
| label nom | /ˈleɪ.bəl/ | étiquette, libellé |
| remote adjectif | /ɹəˈməʊt̞/ | distant, loin |
| qualify verbe | /ˈkwɑ.lɪ.faɪ/ | caractériser, qualifier |
| predict verbe | /pɹɪˈdɪkt/ | prédire |
| automobile nom | /ˈɔː.tə.məˌbil/ | voiture, auto |
| unlock verbe | /ʌnˈlɑk/ | déverrouiller |
| umbrella nom | /ʌmˈbɹɛl.ə/ | parapluie, parasol |
| perceive verbe | /pɚˈsiv/ | percevoir |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| get up to verbe | commettre |
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 | we've become surrounded · have become · has been |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | are made · being powered · be programmed |
Prononciation à surveiller
Le locuteur utilise 17 contractions et formes réduites, comme don't, they're, you're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: algorithm /ˈælɡəɹɪðm̩/, ethical /ˈɛθɪkəl/
- Les sons « sh » et « zh »: artificial /ˌɑː.tɪˈfɪʃ.əl/, interaction /ˌɪn.təˈɹæk.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, transcription /tɹænˈskɹɪpʃən/, unleash /ʌnˈliʃ/
- Mots longs — placez bien l’accent: artificial /ˌɑː.tɪˈfɪʃ.əl/, automobile /ˈɔː.tə.məˌbil/, participate /pɑːˈtɪs.ɪ.peɪt/, comprehensive /ˌkɑm.pɹəˈhɛn.sɪv/, interaction /ˌɪn.təˈɹæk.ʃən/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme artificial, folk, interact.
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.