शैडोइंग अभ्यास: AI, Machine Learning, Deep Learning and Generative AI Explained - वीडियो के साथ अंग्रेजी बोलना सीखें

Everybody's talking about artificial intelligence these days, AI.

Machine learning is another hot topic.

Are they the same thing or are they different?

And if so, what are those differences?

And deep learning is another one that comes into play.

I actually did a video on these three, artificial intelligence, machine learning, and deep learning, and talked about where they fit.

And there were a lot of comments on that, and I read those comments, and I'd like to address some of the most frequently asked questions

so that we can clear up some of the myths and misconceptions around this.

In addition, something else has happened since that video was recorded, and that is the absolute explosion of this area of generative AI.

Things like large language models and chatbots have seemed to be taking over the world.

We see them everywhere.

Really interesting technology.

And then also things like deepfakes.

These are all within the realm of AI, but how do they fit within each other?

How are they related to each other?

We're going to take a look at that in this video

and try to explain how all these technologies relate and how we can use them.

First off, a little bit of a disclaimer.

I'm going to have to simplify some of these concepts in order to not make this video last for a week.

So those of you that are really deep experts in the field, apologies in advance.

But we're going to try to make this simple and that will involve some generalizations.

First of all, let's start with AI.

Artificial intelligence is basically trying to simulate with a computer something that would match or exceed human intelligence.

What is intelligence?

Well, it could be a lot of different things, but generally we tend to think of it as the ability to learn, to infer, and to reason, things like that.

So that's what we're trying to do in the broad field of AI, of artificial intelligence.

And if we look at a timeline of AI, it really kind of started back around this timeframe.

And in those days, it was very premature.

Most people had not even heard of it.

And it basically was a research project.

But I can tell you as an undergrad, which for me was back during these times, we were doing AI work.

In fact, we would use programming languages like Lisp or Prolog,

and these kinds of things were kind of the predecessors to what became later expert systems.

This was a technology, again, some of these things existed previous, but that's when it really hit kind of a critical mass and became more popularized.

Expert systems of the 1980s, maybe in the 90s, and again, we used technologies like this.

All of this was something that we did before we ever touched in to the next topic I'm going to talk about.

That's the area of machine learning.

Machine learning is, as its name implies, the machine is learning.

I don't have to program it.

I give it lots of information and it observes things.

For instance, if I start doing this, if I give you this and then ask you to predict what's the next thing that's going to be there, well, you might get it, you might not.

You have very limited training data to base this on.

But if I gave you one of those and then ask you what to predict would happen next, well, You're probably going to say this, and then you're going to say it's this, and then you think you got it all figured out,

and then you see one of these, and then all of a sudden I give you one of those and throw you a curve ball.

So this, in fact, and then maybe it goes on like this.

So a machine learning algorithm is really good at looking at patterns and discovering patterns within data.

The more training data you can give it, the more confident it can be in predicting.

So predictions are one of the things that machine learning is particularly good at.

Another thing is spotting outliers like this and saying, oh, that doesn't belong in .. it looks different than all the other stuff because the sequence was broken.

So that's particularly useful in cybersecurity, the area that I work in, because we're looking for outliers.

We're looking for users who are using the system in ways that they shouldn't be or ways that they don't typically do.

So this technology, machine learning, is particularly useful for us.

And machine learning really came along and became more popularized in this timeframe, in the 2010s.

And again, back when I was an undergrad riding my Dinosaur to class, we were doing this kind of stuff.

We never once talked about machine learning.

It might have existed, but it really hadn't hit the popular mindset yet.

But this technology has matured greatly over the last few decades, and now it becomes the basis of a lot we do going forward.

The next layer of our Venn diagram involves deep learning.

Well it's deep learning in the sense that with deep learning we use these things called neural networks.

Neural networks are ways that in a computer we simulate and mimic the way the human brain works, at least to the extent that we understand how the brain works.

It's called deep because we have multiple layers of those neural networks.

The interesting thing about these is they will simulate the way a brain operates, but I don't know if you've noticed,

but human brains can be a little bit unpredictable.

You put certain things in, you don't always get the very same thing out and deep learning is the same way.

In some cases, we're not actually able to fully understand why we get the results we do

because there are so many layers to the neural network, it's a little bit hard to decompose and figure out exactly what's in there.

But this has become a very important part and a very important advancement.

That also reached some popularity during the 2010s and as something

that we use still today as the basis for our next area of AI.

The most recent advancements in the field of artificial intelligence all really are in this space, the area of generative AI.

Now I'm going to introduce a term that you may not be familiar with.

It's the idea of foundation models.

Foundation models is where we get some of these kinds of things.

For instance, an example of a foundation model would be a large language model, which is where we take language and we model it

and we make predictions in this technology where if I see certain types of words, then I can sort of predict what the next set of words will be.

I'm going to oversimplify here for the sake of simplicity, but think about this as a little bit like the auto-complete.

When you start typing something in and then it predicts what your next word will be, except in this case with large language models, they're not predicting the next word,

they're predicting the next sentence, the next paragraph, the next entire document.

There's really an amazing exponential leap in what these things are able to do.

We call all of these technologies generative because they are generating new content.

Some people have actually made the argument that the generative AI isn't really generative,

that these technologies are really just regurgitating existing information and putting it in different format.

Let me give you an analogy.

If you take music, for instance, then every note has already been invented.

So, in a sense, every song is just a recombination,

some other permutation of all the notes that already exist already and just putting them in a different order.

Well, we don't say new music doesn't exist.

People are still composing and creating new songs from the existing information.

I'm going to say Gen.AI is similar.

It's an analogy, so there'll be some imperfections in it, but you get the general idea.

Actually new content can be generated out of these and there are a lot of different forms that this can take.

Other types of models are audio models, video models, and things like that.

Well in fact, these we can use to create deepfakes.

And deepfakes are examples where we're able to take,

for instance, a person's voice and recreate that and then have it seem like the person said things they never said.

Well, it's really useful in entertainment situations, in parodies and things like that.

where if someone's losing their voice, then you could capture their voice and then they'd be able to type

and you'd be able to hear it in their voice.

But there's also a lot of cases where this stuff could be abused.

100

The chatbots, again, come from this space.

101

The deepfakes come from this space.

102

But they're all part of generative AI and all part of these foundation models.

103

This again is the area that has really caused all of us to really pay attention to AI.

104

possibilities of generating new content, or in some cases, summarizing existing content and giving us something that is bite-sized and manageable.

105

This is what has gotten all of the attention.

106

This is where the chatbots and all of these things come in.

107

In the early days, AI's adoption started off pretty slowly.

108

Most people didn't even know it existed, and if they did, it was something that always seemed like it was about five to 10 years away.

109

But then machine learning, deep learning, and things like that came along, and we started seeing some uptick.

110

Then foundation models, Gen AI and the light came along and this stuff went straight to the moon.

111

These foundation models are what have changed the adoption curve and now you see AI being adopted everywhere.

112

And the thing for us to understand is where this is, where it fits in, and make sure that we can reap the benefits from all of this technology.

113

If you like this video and want to see more like it, please like and subscribe.

114

If you If you have any questions or want to share your thoughts about this topic, please leave a comment below.

📺 उसी चैनल से

✨ अनुशंसित वीडियो

26 वाक्य

How does AI actually work?

Business EnglishUniversity StudentsWorking Adults

64 वाक्य

P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】

Business EnglishUniversity StudentsWorking Adults

747 वाक्य

MIT Introduction to Deep Learning | 6.S191

Business EnglishUniversity StudentsWorking Adults

105 वाक्य

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

Business EnglishUniversity StudentsWorking Adults

188 वाक्य

NVIDIA GTC 2025 - Keynote Highlights by NVIDIA CEO Jensen Huang

Business EnglishUniversity StudentsWorking Adults

31 वाक्य

What is AI?

Business EnglishUniversity StudentsWorking Adults

409 वाक्य

I think AI slop is about to trigger a renaissance, actually

Business EnglishUniversity StudentsWorking Adults

8 वाक्य

Neurosymbolic AI Explained

Business EnglishUniversity StudentsWorking Adults

126 वाक्य

How I use AI as a senior software engineer and 5 principles of AI explained in 10 minutes

Business EnglishUniversity StudentsWorking Adults

52 वाक्य

What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners

Business EnglishUniversity StudentsWorking Adults

इस पाठ की शब्दावली और बोलने के सुझाव

यह C1 स्तर का स्पीकिंग पाठ वीडियो “AI, Machine Learning, Deep Learning and Generative AI Explained” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: learning, model, machine, deep, intelligence। इस वीडियो में शैडोइंग के लिए 114 वाक्य और 1738 शब्द हैं। बोला गया हिस्सा 9:55 लंबा है। वक्ता लगभग 175 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। 87% 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 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
neural विशेषण	/ˈnʊɹəl/	तंत्रिकीय
advancement संज्ञा	/ædˈvæns.mənt/	उन्नति, प्रगति
prediction संज्ञा	/pɹɪˈdɪkʃən/	प्रागुक्ति, भविष्यवाणी
decompose क्रिया	/ˌdiːkəmˈpəʊz/	विघटन
imperfection संज्ञा	/ɪmpɚˈfɛkʃən/	अपूर्णता
misconception संज्ञा	/ˌmɪs.kənˈsɛp.ʃən/	भ्रम
parody संज्ञा	/ˈpæɹədi/	व्यंग्य, हास्यानुकृति
predecessor संज्ञा	/ˈpɹɛd.ə.sɛs.ɚ/	पूर्ववर्ती, पूर्वज
diagram संज्ञा	/ˈdaɪ.ə.ɡɹæm/	आलेख, मापचित्र
mindset संज्ञा	/ˈmaɪn(d)ˌsɛt/	मानसिकता
leap क्रिया	/ˈliːp/	कूदना, उछलना
automobile संज्ञा	/ˈɔː.tə.məˌbil/	मोटरवाहन, गाड़ी
algorithm संज्ञा	/ˈælɡəɹɪðm̩/	अल्गोरिद्म
paragraph संज्ञा	/ˈpæɹəɡɹæf/	अनुच्छेद, पैराग्राफ़
apology संज्ञा	/əˈpɑ.lə.d͡ʒi/	क्षमा, क्षमायाचना

वीडियो में आने वाली फ़्रेज़ल वर्ब

शब्द	अर्थ
come along क्रिया	साथ चलना, साथ आना

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	has happened · have seemed · has matured
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	was recorded · was broken · been invented

ध्यान देने योग्य उच्चारण

वक्ता 33 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे we're, don't, I'm। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: algorithm /ˈælɡəɹɪðm̩/, myth /mɪθ/
“sh” और “zh” की ध्वनियाँ: prediction /pɹɪˈdɪkʃən/, imperfection /ɪmpɚˈfɛkʃən/, misconception /ˌmɪs.kənˈsɛp.ʃən/, permutation /ˌpɝ.mjʊˈteɪ.ʃ(ə)n/
लंबे शब्द — बलाघात सही रखें: generative /ˈd͡ʒɛnəɹətɪv/, analogy /əˈnæləd͡ʒi/, cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, exponential /ˌɛk.spəˈnɛn.t͡ʃəl/, imperfection /ɪmpɚˈfɛkʃən/

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: generative /ˈd͡ʒɛnəɹətɪv/, advancement /ædˈvæns.mənt/, invent /ɪnˈvɛnt/, observe /əbˈzɜːv/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: compose /kəmˈpəʊz/, decompose /ˌdiːkəmˈpəʊz/, summarize /ˈsʌməˌɹaɪz/, observe /əbˈzɜːv/
/æ/ — “ऐ” से ज़्यादा खुला: chatbot /ˈtʃætbɑt/, analogy /əˈnæləd͡ʒi/, advancement /ædˈvæns.mənt/, manageable /ˈmænəd͡ʒəb(ə)l/, parody /ˈpæɹədi/

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 neural, advancement, prediction 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: AI, Machine Learning, Deep Learning and Generative AI Explained - वीडियो के साथ अंग्रेजी बोलना सीखें

What is Event Driven Architecture (EDA)?

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

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

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

How does AI actually work?

P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】

MIT Introduction to Deep Learning | 6.S191

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

NVIDIA GTC 2025 - Keynote Highlights by NVIDIA CEO Jensen Huang

What is AI?

I think AI slop is about to trigger a renaissance, actually

Neurosymbolic AI Explained

How I use AI as a senior software engineer and 5 principles of AI explained in 10 minutes

What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners

इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

वीडियो में आने वाली फ़्रेज़ल वर्ब

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?