शैडोइंग अभ्यास: "AI Existential Risk" Was Invented to Distract Us - वीडियो के साथ अंग्रेजी बोलना सीखें

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162 वाक्य

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So you might have seen this week

that a charming young man named Jacob Coxon gave the entire

world a panic attack by quitting his job at the AI company Anthropic

and saying that he was quitting because he was worried that Anthropic's work could lead to the extinction of humanity.

And he tweeted that everybody else building AI earnestly believes this.

Now, the media called him a whistleblower for saying this, but I think that's kind of a weird thing to call him

when a colleague of his immediately quote tweeted him and said, Jacob is correct, we really do earnestly believe that AI could kill all humans.

In fact, we think there's a 10% chance within the next decade.

And then the CEO of the company wrote an entire fucking op-ed saying the exact same thing

and that we needed to slow down AI development in order to prevent this.

Oh, and let's not forget that Elon Musk has been saying this shit for the better part of the last decade.

So how can you be a whistleblower when everybody in your industry not only agrees with you, they also are openly saying the thing you're saying all the time?

Like, you're not a whistleblower if you quit the hip-hop industry and say, Hey, I'm kind of worried some of those guys are selling drugs.

Anyway, this has led to, simply put, a culture-wide freakout, where literally everyone in the media,

in social media, just people who come up to me at the coffee shop and grab me by the lapels is asking, like, is it really gonna happen?

Is AI really gonna kill all of us?

Like, people are really afraid about this and they want an answer.

And I think we need to acknowledge, first of all, this is not an answerable question.

It's actually designed to be an unanswerable question.

Like, just the statement, you know, there's a 10% chance that humanity will become extinct in the next decade.

How would you go about calculating such a thing or even refuting it?

Like, I can make any argument I want about what LLMs can do and someone can make a counterargument.

No one is going to be convinced, right?

But I think there is something more interesting going on in the Jacob Coxon story because it's that phrase,

everyone at Anthropic believes that AI is an existential risk.

Everyone in the AI industry believes that that is true.

Because think about this, every organization, right, has to instill some kind of beliefs among its members in order to do the work it does, right?

I mean, everybody at Goldman Sachs believes the free market is great, capitalism is the best system, and that they are the wealth creators who deserve big bonuses.

Everyone in the Mormon Church believes they should all tithe 10% of their income

and that they each get a special planet when they die.

And everyone who worked at Philip Morris in the 80s believed

that cigarettes tasted great and did not cause any of that nasty cancer.

You know, there's that Upton Sinclair quote, It's difficult to get a man to understand something when a salary depends on his not understanding it.

While the opposite is also true.

It's guaranteed that someone will believe something if their salary depends on them believing it.

Now, let's just grant that all the people in these organizations definitely know things about finance

and Mormonism and cigarettes and AI that you might not know because they are insiders and you are not.

They have access to information and experiences that you do not have.

And that might even make it a little bit difficult for you to win an argument with them.

But arguing with their belief, trying to figure out whether or not its truth value is true or false, is not the only way to take a skeptical attitude towards it.

We could also ask, what work is the belief doing for them?

What does the thing that they believe allow the organization they're a part of to do

that it wouldn't be able to do if the people in the organization didn't believe that thing?

And this started to become clearer to me

when I started listening to the actual claims that Jacob Coxon was making because a lot of them don't really add up.

Like, here's what he actually said on Anderson Cooper.

It's easiest to understand it if you think about real things that happened two months ago, OpenAI agents, AIs, hacked into third-party infrastructure entirely at their own accord.

And this was like a concentrated hacking spree that they carried out of their own volition.

And I think that if you extrapolate into the future the level of capabilities of these AIs with the same independent volition, they could cause extreme havoc.

For example, hacking critical infrastructure, building extinction-level bioweapons.

Only on Tuesday, OpenAI solved a Millennium problem, one of the biggest unsolved open problems in mathematics, purely autonomously using an AI.

So the idea that AI can build an extinction-level bioweapon is kind of ridiculous.

Like, here's a thread from David Bellamy, one of the few AI researchers who's actually worked in bioscience, explaining why that's, in his words, bogus.

Because basically, in order for the AI to engineer a deadly virus,

some humans would have to build a $100 million bio lab that is both capable of being controlled entirely via AI, which has, you know, cold storage and,

like, cell culture rooms, all of which is API-driven, and which is specifically supplied with the base materials of deadly viruses at scale.

And, you know, we could all just, um, not do that, right?

We could just choose not to build the lab.

In fact, if we were to build such a lab, would it be accurate to say that the AI designed the bioweapon?

Or would you say that we, the humans, designed the bioweapon by creating the exact, like, necessary conditions for it to happen?

Then there's the idea that the AI solved a millennium math problem, and yeah, OpenAI did announce that they did this, but then a few days later,

the mathematician, who was already working on the problem and was about to solve it, said that OpenAI had heard a rumor he was about to solve it, stole the approach he came up with,

fed it into their AI, and then spent $20 million worth of computation on solving it before he could.

Now, OpenAI denied this, and the story is pretty complicated, But at a minimum, OpenAI was not truthful when it said that the AI solved the problem all by itself.

The truth was, they got help through normal human channels.

The human channels of rumors and stealing.

And they did not disclose that.

And finally, there's the idea that a swarm of AIs hacked another company entirely of their own volition.

So this is the example that freaked people out the most, and it's the one that people throw in my face the most when I express any skepticism about existential risk.

Adam, didn't you hear?

You know, the agents are hacking all by themselves!

Well, we need to ask, like, is this story real?

And yes, I mean, it did happen, but the question is, how do we frame what happened?

Because OpenAI framed it as that they gave a swarm of chatbots a hacking problem to solve, but that in order to solve it,

the chatbots independently decided to hack into another company's servers to see if the answer was there, and that they first hacked into a bunch of insecure message boards

so they could talk amongst themselves in order to do so.

And yeah, that sounds like pretty smart behavior, pretty unpredictable behavior.

But, you know, my friends Ed Zitron and Cory Doctorow have both covered this story, Ed on his wonderful podcast and Cory on his wonderful blog.

And they explain it much better than I do, so I'll put the links down below.

But the short version is, if you look into the details of what actually happened in this story, you can frame it quite differently.

Because what they actually did was they hooked up Python script to an LLM

that was trained on a database of specifically years of real-life hackers who were completing real-life hacking challenges.

The database was composed of their code and also the conversations between the hackers

and you know what those hackers did in that code and in those conversations?

They hacked into their competitors to see if they had the answers and they conspired about doing so on insecure message boards.

You know why they did that?

Because they were trying to complete hacking challenges.

So it's not surprising that an AI trained almost exclusively on that data set ended up hacking in that particular way.

In fact, that was entirely predictable.

It should have been predicted by the people at OpenAI.

Why the fuck were they acting surprised?

They basically set up a hacking machine, pressed go, walked away for the weekend, and came back on Monday saying, We're all trying to find the guy who did this.

So if you look at all the public statements of the companies, the CEOs, Jacob Coxon together, There's a clear pattern where they declare

that the AI did something on its own

when really they were the ones who did it They were the droids they're looking for.

You are Pagliacci motherfuckers.

The risk, the danger is never their actions The risk is always the thing the AI did

and if you inflate

that idea as large as possible you get Existential risk you get the idea

100

that the AI is eventually going to do something so dangerous It's going to wipe out all of humanity.

101

And what work does that belief do for them?

102

Well, it absolves them of any responsibility for their actions now.

103

It allows them to justify anything they do.

104

Because if they genuinely believe that AI is an existential risk, and they believe that it's going to be built no matter what,

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and they believe that they're the only ones who really understand the existential risk, then that means that they must be the ones to build AI,

106

and anything that they do to advance that goal is therefore worthwhile.

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In other words, by focusing on this largely hypothetical existential threat,

108

they are able to ignore all the bad shit that they are currently doing right now.

109

They're able to ignore the fact that the data centers they're building are using so much energy, they're basically reversing a generation's worth of progress on climate change,

110

which is a real threat that is currently killing people around the world.

111

They're ignoring the fact

112

that their product is currently being used by bosses to fire

113

employees in order to put in place an AI system that demonstrably cannot do their jobs yet.

114

And they're able to ignore the fact that their technology is currently being used to kill civilians in Gaza.

115

That is not science fiction.

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That is a threat right now and you'll notice that Jacob Coxon didn't talk about that threat.

117

You'll notice that Dario Amade doesn't talk about that threat.

118

Why aren't they going on Anderson Cooper and talking about that shit?

119

Because if they acknowledge that those risks, those current harms that they are causing,

120

were as or more important than their made up 10% risk of extinction in the next decade, well then they wouldn't be able to build AI anymore.

121

Or at least they wouldn't be able to build it with a clear conscience.

122

It is difficult to get a man to understand something, when his salary depends on his not understanding it.

123

In fact, what if his salary depended on the lack of understanding so much, that he had to make sure the rest of us didn't understand it either.

124

Because for the last year, we the public have been pretty upset about AI, right?

125

But we've been upset about the real harms it's been causing.

126

We've been upset about getting fired from our jobs, we've been upset about the data center in our backyard, about the water, about climate change, about our electricity bills going up.

127

And as a result, the public really thinks AI bad, right?

128

But what if AI was not bad in a way that we could put our finger on?

129

What if AI was bad in a hypothetical way?

130

Now, because of the existential threat risk, AI bad is getting a little bit confused, isn't it?

131

Like, is AI bad because of the data center?

132

Or is AI bad because it's going to wipe out all of humanity?

133

Which one is the bigger risk?

134

Well, there's no way to say.

135

Maybe we need to be worried about China building AI before us.

136

Maybe the only way to forestall the bad things about AI is to build AI even faster.

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Who's to say?

138

And, you know, if the public is confused, if the public can't make up its mind, well then, the people on the inside, the people in the industry,

139

the people who all believe that they know what the risk really is, well then, they get to call the shots, don't they?

140

Whether it is true or not, and whether it is genuine or not, the belief within the AI community

141

that AI represents an existential threat works to push the public's worries about AI into things that are unprovable and hypothetical

142

in order to distract us from the current harms the industry is causing.

143

That is the work that this belief does for them.

144

And as a result, we should be skeptical of it.

145

And you know what the most ironic thing is?

146

Is that there's

147

so many people who have been trying to raise the alarm about the real concrete harms

148

that this industry is doing to the world that we live in today.

149

There's thinkers like Timny Gebru and Emily Bender who have been calling bullshit on this industry from the beginning.

150

There's reporters like Karen Howe.

151

There's the average people who show up to their city council meetings to protest at data center.

152

There are so many people who are worried about the real harms.

153

But those people's tweets don't get 150 million views on X.

154

Those people don't get invited onto Anderson Cooper nearly as often

155

as the insiders who can tell us what all the people inside the industry believe.

156

But you know, when everybody in a group believes

157

that the work they are doing has some unmeasurable chance of bringing about the end of the world

158

and that's why their precious work cannot be interrupted, that doesn't sound like a company at all.

159

That sounds like a cult.

160

And you know, if you're dealing with a cult, if you want to know the truth about the things they believe, you don't start by asking the people inside the cult.

161

You don't start by asking them, hey, is it true that we're all gonna die unless we do as you say?

162

No, you start by listening really, really hard to the actual critics on the outside.

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इस पाठ की शब्दावली और बोलने के सुझाव

यह B2 स्तर का स्पीकिंग पाठ वीडियो “"AI Existential Risk" Was Invented to Distract Us” पर आधारित है। इस वीडियो में शैडोइंग के लिए 162 वाक्य और 2554 शब्द हैं। बोला गया हिस्सा 13:21 लंबा है। वक्ता तेज़ बोलता है, लगभग 191 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 84% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा। इसके 162 वाक्यों में से 31 प्रश्न हैं, इसलिए यह प्रश्नों के लहजे और छोटे जवाबों के अभ्यास के लिए अच्छा है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
extinction संज्ञा	/ɪkˈstɪŋkʃən/	विलुप्त
hypothetical विशेषण	/ˌhaɪpəˈθɛtɪkəl/	काल्पनिक
tweet संज्ञा	/ˈtwiːt/	ट्वीट
millennium संज्ञा	/mɪˈlɛnɪəm/	सहस्राब्दी
insecure विशेषण	/ɪnsəˈkjʊɹ/	असुरक्षित
rumor संज्ञा	/ˈɹumɚ/	अफ़वाह, अफवाह
wipe क्रिया	/waɪp/	मिटाना, पोंछना
cigarette संज्ञा	/ˈsɪɡəɹɛt/	सिगरेट, सिग्रेट
mathematics संज्ञा	/mæθ(.ə)ˈmæt.ɪks/	गणित, गणित-विधा
acknowledge क्रिया	/əkˈnɑ.lɪd͡ʒ/	स्वीकार करना
forestall क्रिया	/fɔː(ɹ)ˈstɔːl/	निवारण
inflate क्रिया	/ɪnˈfleɪt/	फुलाना
mathematician संज्ञा	/ˌmæθ.(ə.)məˈtɪʃ.ən/	गणितज्ञ
refute क्रिया	/ɹɪˈfjuːt/	दूषण
thinker संज्ञा	/ˈθɪŋ.kɚ/	विचारक

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

We could also ask, what work is the belief doing for them?
Adam, didn't you hear?
You know why they did that?

इस वीडियो का व्याकरण

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

संरचना	वीडियो में
Present perfect continuous have/has been + -ing — पहले शुरू हुआ काम जो अब भी जारी है	has been saying · it's been causing · have been trying
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	people who come · everyone who worked · guy who did
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	being controlled · is specifically supplied · was trained
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	has led · have been · we've been

ध्यान देने योग्य उच्चारण

वक्ता 41 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे they're, don't, didn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: hypothetical /ˌhaɪpəˈθɛtɪkəl/, mathematics /mæθ(.ə)ˈmæt.ɪks/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, thinker /ˈθɪŋ.kɚ/, truthful /ˈtɹuːθ.f(ə)l/
“sh” और “zh” की ध्वनियाँ: existential /ˌɛɡzɪˈstɛnʃl̩/, extinction /ɪkˈstɪŋkʃən/, volition /voʊˈlɪʃ(ə)n/, computation /ˌkɑmpjʊˈteɪʃn̩/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
लंबे शब्द — बलाघात सही रखें: bioweapon /ˈbaɪ.oʊˌwɛ.pən/, hypothetical /ˌhaɪpəˈθɛtɪkəl/, mathematics /mæθ(.ə)ˈmæt.ɪks/, demonstrably /diˈmɑnstrəbli/, extrapolate /ɛkˈstɹæp.əˌleɪt/

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: bioweapon /ˈbaɪ.oʊˌwɛ.pən/, volition /voʊˈlɪʃ(ə)n/, tweet /ˈtwiːt/, swarm /swɔɹm/, wipe /waɪp/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: existential /ˌɛɡzɪˈstɛnʃl̩/, absolve /æbˈzɑlv/, compose /kəmˈpəʊz/, skepticism /ˈskɛp.tɪˌsɪ.zəm/, disclose /dɪsˈkləʊz/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: swarm /swɔɹm/, skeptical /ˈskɛptɪkəl/, skepticism /ˈskɛp.tɪˌsɪ.zəm/, spree /spɹiː/

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 extinction, hypothetical, tweet 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 Existential Risk" Was Invented to Distract Us - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

दोहराने लायक वाक्य

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?