शैडोइंग अभ्यास: AI for Everyone by Andrew Ng - वीडियो के साथ अंग्रेजी बोलना सीखें
अभ्यास मोड चुनें
हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।
78 वाक्य
यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।
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Welcome to AI for Everyone.
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AI is changing the way we work and live.
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This non-technical course will teach you how to navigate the rise of AI.
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Whether you want to know what's behind the buzzwords, or whether you want to perhaps use AI yourself,
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either in the personal context or in a corporation or other organization, this course will teach you how.
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And if you want to understand how AI is affecting society and how you can navigate that, you also learn that from this course.
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In this first week, we'll start by cutting through the hype and giving you a realistic view of what AI really is.
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Let's get started.
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You've probably seen news articles about how much value AI is creating.
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According to a study by McKinsey Global Institute,
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AI is estimated to create an additional 13 trillion US dollars of value annually by the year 2030.
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Even though AI is already creating tremendous amounts of value in the software industry,
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a lot of the value to be created in the future lies outside the software industry in sectors such as retail,
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travel, transportation, automotive, materials, manufacturing, and so on.
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I have a hard time thinking of an industry
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that I don't think AI will have a huge impact on in the next several years.
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My friends
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and I used to challenge each other to name an industry where we don't think AI will have a huge impact.
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And my best example was the hairdressing industry because you know how to use AI robotics to automate hairdressing.
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But I once said this on stage
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and one of my friends who is a robotics professor was in the audience
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that day that day and she actually stood up
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and she looked at me in the eye
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and she said you know Andrew most people's hairstyles I couldn't get a robot to cut their hair that way.
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But she looked at me and said, your hairstyle, Andrew, that a robot can do.
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There is a lot of excitement, but also a lot of unnecessary hype about AI.
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One of the reasons for this is because AI is actually two separate ideas.
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Almost all the progress we are seeing in AI today is artificial narrow intelligence.
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These are AIs that do one thing, such as a smart speaker or self-driving car
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or AI to do web search or AI applications in farming or in a factory.
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These types of AI are one-trick ponies, but when you find the appropriate trick, this can be incredibly valuable.
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Unfortunately, AI also refers to a second concept of AGI or artificial general intelligence.
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That is the goal to build AI that can do anything a human can do, or maybe even be super intelligent and do even more things than any human can.
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I'm seeing tons of progress in ANI, artificial narrow intelligence, and almost no progress toward AGI, or artificial general intelligence.
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Both of these are worthy goals, and unfortunately, the rapid progress in ANI, which is incredibly valuable.
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That has caused people to conclude that there's a lot of progress in AI, which is true, but that has caused people to falsely think
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that there might be a lot of progress in AGI as well,
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which is leading to some irrational fears about evil color robots coming over to take over humanity anytime now.
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I think AGI is an exciting goal for researchers to work on, but it'll take multiple technological breakthroughs before we get there.
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And it may be decades or hundreds of years or even even thousands of years away.
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Given how far away AGI is, I think there is no need to unduly worry about it.
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In this week, you will learn what ANI can do and how to apply them to your problems.
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Later in this course, you also see some case studies of how ANI,
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these one-trick ponies, can be used to build really valuable applications such as smart speakers and self-driving cars.
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In this week, you will learn what is AI.
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You may have heard of machine learning and the next video will teach you what is machine learning.
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You also learn what is data and what types of data are valuable, but also what types of data are not valuable.
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You learn what it is that makes a company an AI company or an AI first company,
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so that perhaps you can start thinking if there are ways to improve your company or other organization's ability to use AI.
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And importantly, you also learned this week what machine learning can and cannot do.
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In our society, newspapers as well as research papers tend to talk only about the success stories of machine learning and AI.
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And we hardly ever see any failure stories because they just aren't as interesting to report on.
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But for you to have a realistic view of what AI and what machine learning can and cannot do, I think it's important that you see examples of both
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so they can make more accurate judgments about what you may and maybe should not try to use these technologies for.
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Finally, a lot of the recent rise of machine learning has been driven through the rise of deep learning, sometimes also called neural networks.
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In the final two optional videos of this week,
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you can also see an intuitive explanation of deep learning so that you will better understand what they can do,
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particularly for a set of narrow ANI tasks.
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So that's what you learned this week.
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And by the end of this week, you have a sense of AI technologies and what they can and cannot do.
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In the second week, you learn how these AI technologies can be used to build valuable projects.
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You learn what it feels like to build an AI project
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as well as what you should do to make sure you select projects that are technically feasible
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as well as valuable to you or your business or other organization.
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After learning what it takes to build AI projects, in the third week, you learn how to build AI in your company.
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In particular, if you want to take a few steps toward making your company good at AI,
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you see the AI transformation playbook and learn how to build AI teams and also build complex AI products.
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Finally, AI is having a huge impact on society.
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In the fourth and final week, you learn about how AI systems can be biased and how to diminish or eliminate such biases.
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You also learn how AI is affecting developing economies and how AI is affecting jobs,
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and be better able to navigate this rise of AI for yourself and for your organization.
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By the end of this four-week course, you'll be more knowledgeable
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and better qualified than even the CEOs of most large companies in terms of your understanding of AI technology,
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as well as your ability to help yourself or help your company or other organization navigate the rise of AI.
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And so I hope that after this course, you'll be in a position to provide leadership to others as well as they navigate these issues.
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Now, one of the major technologies driving the recent rise of AI is machine learning.
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But what is machine learning?
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Let's take a look in the next video.
ऐप डाउनलोड करें
धाराप्रवाह बोलने के लिए ज़रूरी सब कुछ
AI उच्चारण स्कोरहर वाक्य का स्कोर
IPA अभ्यासहर ध्वनि में महारत
शब्दावलीअपना शब्द भंडार बनाएँ
शब्द गेमखेलते हुए सीखें
उसी चैनल से
![#1 Machine Learning Specialization [Course 1, Week 1, Lesson 1]](https://img.youtube.com/vi/vStJoetOxJg/mqdefault.jpg)
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इस पाठ की शब्दावली और बोलने के सुझाव
इस वीडियो में शैडोइंग के लिए 78 वाक्य और 1231 शब्द हैं। बोला गया हिस्सा 7:35 लंबा है। वक्ता लगभग 162 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। 85% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 कम प्रचलित शब्द, उच्चारण और अर्थ के साथ:
- artificial /ˌɑː.tɪˈfɪʃ.əl/ (विशेषण) — कृत्रिम. Man-made; made by humans; of artifice.
- trick /ˈtɹɪk/ (संज्ञा) — चाल. Something designed to fool, dupe, outsmart, mislead or swindle.
- narrow /ˈnæɹəʊ/ (विशेषण) — तंग. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
- robot /ˈɹoʊ.bɑt/ (संज्ञा) — रोबोट, यंत्रमानव. An intelligent mechanical being designed to look like a human or other creature, and usually made from metal.
- bias /ˈbaɪ.əs/ (संज्ञा) — पक्षपात, पूर्वाग्रह. Inclination towards something.
- realistic /ˌɹɪəˈlɪs.tɪk/ (विशेषण) — वास्तविक. Expressed or represented as being accurate, practicable, or not idealistic.
- robotics /ɹoʊˈbɑ.tɪks/ (संज्ञा) — रोबॉटिक्स. The science and technology of robots, their design, manufacture, and application
- select /sɪˈlɛkt/ (क्रिया) — चुनना, चयन करना. To choose one or more elements of a set, especially a set of options.
- explanation /ˌɛkspləˈneɪʃən/ (संज्ञा) — स्पष्टीकरण, समझाना. The act or process of explaining.
- humanity /hjuˈmænɪti/ (संज्ञा) — मानवजाति, मानवता. Humankind; human beings as a group.
- estimate /ˈɛs.tɪ.mət/ (संज्ञा) — आकलन, अनुमान. A rough calculation or assessment of the value, size, or cost of something.
- intelligent /ɪnˈtɛlɪd͡ʒənt/ (विशेषण) — बुद्धिमान, चतुर. Of high or especially quick cognitive capacity, bright.
- ton /tʌn/ (संज्ञा) — टन. The short ton of 2000 pounds (about 907 kg), 20 hundredweights of 100 pounds avoirdupois each.
- worthy /ˈwɝ.ði/ (विशेषण) — योग्य. Having worth, merit, or value.
- judgment /ˈd͡ʒʌd͡ʒ.mənt/ (संज्ञा) — मूल्यांकन. The act of judging.
वीडियो में आने वाली फ़्रेज़ल वर्ब
- look at (क्रिया) — हेरना. To observe or watch.
- stand up (क्रिया) — उठना. To rise from a lying or sitting position.
ध्यान देने योग्य उच्चारण
वक्ता 10 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, you'll, aren't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: worthy /ˈwɝ.ði/, breakthrough /ˈbɹeɪk.θɹuː/
- “sh” और “zh” की ध्वनियाँ: artificial /ˌɑː.tɪˈfɪʃ.əl/, explanation /ˌɛkspləˈneɪʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, optional /ˈɑp.ʃə.nəl/, irrational /ɪˈɹæʃ.(ə.)nəl/
- लंबे शब्द — बलाघात सही रखें: artificial /ˌɑː.tɪˈfɪʃ.əl/, incredibly /ɪŋˈkɹɛdɪbli/, explanation /ˌɛkspləˈneɪʃən/, humanity /hjuˈmænɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर artificial, trick, narrow जैसे शब्दों पर ध्यान दें।
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is estimated · be created · has been driven |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | You've probably seen · has caused · has been driven |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | friends who is · ANI, which is |
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।