शैडोइंग अभ्यास: What is AI Ethics? - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।

72 वाक्य

यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।

I want to start off with talking to you about three things that keep me up at night, right?

Three things.

The first, and it may be, you know, very common for you too, is climate change.

Climate change absolutely keeps me up at night.

The second thing that keeps me up at night is

that people may have no idea that an artificial intelligence is making a decision

that directly impacts their lives what percentage interest rate you get on your loan,

whether you get that job that you applied for, whether your kid gets into that college that they really want to go to.

Today, AI is making decisions that directly impact you.

The third thing that keeps me up at night is even

when When people know that an AI is making a decision about them,

they may assume that because it's not a fallible human with bias,

that somehow the AI is going to make a decision that's morally or ethically squeaky clean.

And that could not be farther from the truth.

So if you think about organizations and what happens, over 80 % of the time,

proof of concepts associated with artificial intelligence actually gets stalled in testing.

And more often than not, it is because people do not trust the results from that AI model.

So we're going to talk a lot about trust.

And when thinking about trust, I'm going to switch colors here, there's actually five pillars.

Okay.

When you're thinking about what does it take to earn trust in an artificial intelligence

that's being made by your organization or being procured by your organization, five pillars.

The first thing to be thinking about is fairness.

How can you ensure that the AI model is fair towards everybody, in particular historically underrepresented groups?

The second is explainable.

Is your AI model explainable such that you'd be able to tell somebody,

an end user, what data sets were being used in order to curate that model?

What methods, what expertise, what's the data lineage and provenance associated with how that model was trained?

The third, robustness.

Can you assure end users that nobody can hack such an AI model such

that a person could disadvantage willfully other people and or make the results of that model benefit one particular person over another?

The fourth is transparency.

Are you telling people right off the bat than the AI model is indeed being used to make that decision?

And are you giving people access to a fact sheet or metadata so that they can learn more about that model?

And the fifth one is, are you assuring people's data privacy?

So those are the five pillars, okay?

Now, IBM has come up with three principles when thinking about AI in an organization.

The first being that the purpose of artificial intelligence is really meant to be to augment human intelligence,

not to replace it.

The second is that data and the insights from those data belong to the creator alone.

And the third is that AI systems, and I would opine the entire AI lifecycle, really should be transparent and explainable right?

So those are the five pillars.

Now, the next thing I want you to remember

as you're thinking about this space of earning trust and artificial intelligence is that this is not a technological challenge.

It can't be solved with just throwing tools and tech over some kind of a fence.

This is a socio -technological challenge.

Socio meaning people, people, people.

Socio -technological challenge.

And because it's a socio -technological challenge.

It must be addressed holistically, okay?

Holistically, meaning there's three major things that you should think about.

I mentioned people, people, the culture of your organization, right?

Thinking about the diversity of your teams, you know, your data science team who is curating that data to train that model.

How many women are on that team?

How many minorities are on that team, right?

Think about diversity.

I don't know if you've ever heard of the the wisdom of crowns that's actually a proven mathematical theory

The more diverse your group of people, the less chance for error.

And that is absolutely true in the realm of artificial intelligence.

The second thing is process or governance, right?

What is it that you as your organization,

what are you going to promise both your employees as well as the market with respect to what standards

you're going to stand by for your AI model in terms of things like fairness and explainability, accountability, etc., right?

And the third area is tooling, right?

What are the tools, AI engineering,

methods, frameworks that you can use in order to ensure these things, ensure those five pillars?

And we're going to do a deep dive into that as well.

But the next show that I'm going to be running with you, we're actually going to be talking about this one, about people and culture.

So stay tuned.

If you like this video and series, please comment below.

Stay tuned for more videos that are part of this series.

And to get updates, please like and subscribe.

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

QR स्कैन करें

Download on theApp Store Get it onGoogle Play

उसी चैनल से

FOR YOU

अनुशंसित वीडियो

105 वाक्य

AI Governance Explained: Ethical, Transparent & Accountable AI for the Future

Daily ConversationUniversity StudentsWorking Adults

623 वाक्य

The Only SaaS Moats Left in 2026

Daily ConversationUniversity StudentsWorking Adults

74 वाक्य

AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society

Daily ConversationUniversity StudentsWorking Adults

76 वाक्य

Make AI Believe in God to Keep Humans Safe, Scientist Argues

Daily ConversationUniversity StudentsWorking Adults

211 वाक्य

IAM

Daily ConversationUniversity StudentsWorking Adults

55 वाक्य

When AIs act emotional

Daily ConversationUniversity StudentsWorking Adults

114 वाक्य

Harnessing generative AI in architecture, engineering & construction

Daily ConversationUniversity StudentsWorking Adults

74 वाक्य

How AI is helping brands identify consumer trends faster

Daily ConversationUniversity StudentsWorking Adults

106 वाक्य

DPM Gan Kim Yong's Speech at the "Trust and AI: Navigating a World in Transition" Leaders Dialogue

Daily ConversationUniversity StudentsWorking Adults

85 वाक्य

AI Can Write Code. And That’s the Problem.

Daily ConversationUniversity StudentsWorking Adults

इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “What is AI Ethics?” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: model, intelligence, artificial, organization, thinking। इस वीडियो में शैडोइंग के लिए 21 वाक्य और 859 शब्द हैं। बोला गया हिस्सा 6:09 लंबा है। वक्ता लगभग 140 शब्द प्रति मिनट की स्थिर गति से बोलता है, जो शैडोइंग के लिए आरामदायक है। 86% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा। इसके 21 वाक्यों में से 13 प्रश्न हैं, इसलिए यह प्रश्नों के लहजे और छोटे जवाबों के अभ्यास के लिए अच्छा है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
artificial विशेषण	/ˌɑː.tɪˈfɪʃ.əl/	कृत्रिम
pillar संज्ञा	/ˈpɪlɚ/	स्तंभ, खंभा
earn क्रिया	/ɝn/	कमाना
diversity संज्ञा	/daɪˈvɜː(ɹ)sɪti/	विविधता
tune संज्ञा	/tjuːn/	धुन
percentage संज्ञा	/pə(ɹ)ˈsɛntɪd͡ʒ/	प्रतिशतता
solve क्रिया	/sɒlv/	सुलझाना, हल करना
minority संज्ञा	/maɪˈnɒɹ.ɪ.ti/	अवयस्कता, नाबालिग़पन
sheet संज्ञा	/ʃit/	पत्र, चद्दर
privacy संज्ञा	/ˈpɹaɪ.və.si/	एकांत
principle संज्ञा	/ˈpɹɪn.sɪ.pəl/	जड़सूत्र, असूल
wisdom संज्ञा	/ˈwɪzdəm/	बुद्धिमत्ता, पांडित्य
diverse विशेषण	/daɪˈvɜːs/	विविध
creator संज्ञा	/kɹiːˈeɪ̯təː/	विधाता
fence संज्ञा	/fɛns/	बाड़

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	being made · was trained · is really meant
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	has come · you've ever heard

ध्यान देने योग्य उच्चारण

वक्ता 13 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे we're, you're, I'm। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
“sh” और “zh” की ध्वनियाँ: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
लंबे शब्द — बलाघात सही रखें: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, wisdom /ˈwɪzdəm/, framework /ˈfɹeɪm.wɜːk/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: wisdom /ˈwɪzdəm/, expertise /ˌɛkspɜːˈtiːz/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: stall /stɔːl/, squeaky /ˈskwiːki/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
सामान्य गति पर एक-एक वाक्य की शैडोइंग करें और हर वाक्य तब तक दोहराएँ जब तक आपकी लय वक्ता से न मिल जाए।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर artificial, pillar, earn जैसे शब्दों पर ध्यान दें।

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।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: What is AI Ethics? - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

ऐप डाउनलोड करें

Every Generation Thinks This Time Is Different: The AI Job Myth

What is Event Driven Architecture (EDA)?

Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production

AI, Machine Learning, Deep Learning and Generative AI Explained

Spec-Driven Development: AI Assisted Coding Explained

AI Agents vs Business Rules: Which Should Make Decisions?

AI & Education: Generative AI & the Future of Critical Thinking

EDR vs. EPP vs. NGAV

MCP vs ADK: How Modern AI Agents Connect and Work Together

Cybersecurity Architecture: Data Security

The 7 Skills You Need to Build AI Agents

What Is Jev? The AI Model That Doesn't Generate Text

How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact

What are AI Agents?

Cybersecurity Architecture: Five Principles to Follow (and One to Avoid)

Zero Trust Explained in 4 mins

What is Responsible AI? A Guide to AI Governance

AI Governance Explained: Ethical, Transparent & Accountable AI for the Future

The Only SaaS Moats Left in 2026

AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society

Make AI Believe in God to Keep Humans Safe, Scientist Argues

IAM

When AIs act emotional

Harnessing generative AI in architecture, engineering & construction

How AI is helping brands identify consumer trends faster

DPM Gan Kim Yong's Speech at the "Trust and AI: Navigating a World in Transition" Leaders Dialogue

AI Can Write Code. And That’s the Problem.

इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?