تدريب Shadowing: Inside the secret race to build artificial intelligence | The Gray Area - تعلم التحدث بالإنجليزية عبر الفيديو
اختر وضع التدريب
كل وضع يفتح غرفة تدريب بملء الشاشة.
510 جمل
إذا كانت الجمل قصيرة أو طويلة جدًا، انقر على Edit لتعديلها.
1
Ten years ago, this was crazy.
2
This was considered very much outside the Overton window because the technology of the time wasn't good.
3
Like, it didn't do anything useful.
4
And it was hard to look at, like, a, you know, Siri or an Alexa and think, like, this could take over the world.
5
You had to be somewhat odd to find that level of technology threatening.
6
But these people who have made all these predictions for a long time about AI becoming powerful
7
and scary have been very vindicated by that belief.
8
I'm Christine Emba, sitting in for Sean Illing, and this is The Grey Area.
9
My guest today is Kevin Roos, whose new book, The AGI Chronicles, The Inside Story of the Race to Create an Artificial Super Intelligence,
10
came out this October.
11
I'm excited to sit down with an expert, someone who's been following the story from the beginning, to better understand how it all went down,
12
and to figure out how scared we should all be.
13
Kevin, welcome to the show.
14
Thanks so much for having me.
15
Before we dive in, I think we need to define some terminology just for the non -technical listeners.
16
So you use the words, you know, models, you're talking about ChatGPT,
17
but what, I guess, in a nutshell, is AGI?
18
And explain like the core bet of AI to a layperson.
19
So there's AI, there's AGI, and there's ASI.
20
And these are the sort of terms that people use.
21
So AI is just the sort of catch -all term for the broad category of, you know, machine learning systems,
22
neural networks that can kind of absorb data and learn things and take actions.
23
Those are, you know, everything we see today is categorized as AI.
24
AI is artificial general intelligence.
25
And that is this dream, really, this holy grail of the AI system that can do everything the human brain can.
26
So a human level general intelligence.
27
It's not just good at one thing or two things or 10 things.
28
It's very flexible and general, and it can do anything that we can.
29
And then there's ASI, artificial superintelligence, which actually had a meaning even before Donald Trump started insisting that we call it superintelligence.
30
This was, you know, if you could think of AGI as an AI that is smarter than most humans at most things,
31
ASI would be an AI that is smarter than all humans at everything.
32
It is sort of vastly superior to us at any task.
33
And, you know, there's sort of steps along a continuum.
34
But AGI, the middle one, that is really what the past 20 years in AI have been about and building up to.
35
All the companies that are now leading the AI race, OpenAI, Anthropic, Google DeepMind, they have been obsessed with this question of AGI.
36
So AGI was sort of what they were all aiming for and where they're trying to get.
37
It almost feels as though the top frontier labs, you know, OpenAI, which has ChatGPT,
38
Anthropic has Claude and Google DeepMind.
39
It almost feels as though these orgs and maybe their chatbots have different personalities.
40
So I'd love to hear you like talk about their stories.
41
We can start with OpenAI.
42
That was sort of the first big frontier lab, right?
43
Well, sort of.
44
So basically the history goes like this and I'll bridge this.
45
There's many more things that you could say about this.
46
You could talk about this for three or four hours.
47
But DeepMind was the first AGI lab.
48
They started in 2010 and they were interested in this question of how they could build AGI.
49
And at the time, this was like a fringe radical belief.
50
Like you'd get laughed out of the room for talking about AGI if you were among academics.
51
It was like saying, you know, I believe in alchemy.
52
It just, you know, was sort of disqualifyingly crazy.
53
But they, so they start this, then they get bought by Google.
54
And so in the mid 2010s, they start doing things like training AIs to play video games and then to play chess and Go,
55
which is a board game that's very popular in Asia.
56
And they start getting good at this.
57
Their Go engine, sort of famously AlphaGo, which is their Go engine,
58
beats the world champions in Go and sort of.. proves in a live demonstration that it's superhuman.
59
And that gets the attention of Elon Musk and Sam Altman, who are looking at this and saying,
60
wait a minute, we believe that AGI is possible.
61
And it looks like these guys are on track to get there first.
62
And they don't like it.
63
They don't like Demis Hassabis, the CEO of DeepMind.
64
They don't like Google and Larry Page, they think that these people are untrustworthy with something this powerful.
65
And so they start OpenAI to basically prevent Demis and Google and DeepMind from getting to AGI first.
66
And then five years later, the same thing happens at OpenAI.
67
So there's this group of seven employees of OpenAI led by this guy,
68
Dario Amadei, who's then the vice president of research who are starting to see evidence that these language models are working,
69
that they're scaling, that the more compute and data you put into them, the more intelligent they are.
70
And they decide that they don't trust their bosses, Sam Altman and Greg Brockman, with something this powerful.
71
And so they leave and they set up Anthropic.
72
So this is sort of the story of the last 10
73
or 15 years in AI is like a series of people deciding that this technology is going to be built,
74
it's powerful, it's inevitable, and they don't want that guy to get there first.
75
And so the only solution is to set up their own shop and build it first.
76
Underlying all of this leapfrogging is what seems to be like one piece of recurring logic.
77
Build the bomb basically before the wrong people do, the other guy.
78
Is that actually a real argument or is this kind of the race's all -purpose excuse?
79
It is a real argument.
80
They're sincere when they say this.
81
And the reason I know this is because I talked to more than 150 people for this book.
82
And I talked to all the leaders of the labs
83
and all of their friends and executives at these companies and people who have known them since before they were famous.
84
You know, people who have been friends and housemates of theirs going way back.
85
And it helps to know, maybe one piece of useful information here is that all these people are students of history.
86
Dario Amade in particular is obsessed with this book, The Making of the Atomic Bomb,
87
which is a Pulitzer -winning history of the Manhattan Project written by Richard Rhodes.
88
And the hero of that book, and Dario's scientific hero, is not Oppenheimer, it's this guy, Leo Szilard,
89
who was a Hungarian physicist who discovered the nuclear chain reaction.
90
And he fought to keep it secret.
91
That's the first thing he did.
92
Because he was like, there's this secret that allows you to split the atom, and that could be very dangerous.
93
It could be used to build bombs.
94
And so he wrote frantic telegrams to scientists all over Europe and all over the world saying, like, please do not publish any research about this,
95
because The Nazis will get it and they will use it to build bombs.
96
And the secret got out anyway.
97
Like the Nazis discovered this or, you know, it was published in an academic journal
98
and they were able to like spin up their committee to start looking into the bomb.
99
And Szilard at that point pivots.
100
He says, well, if the secret's out, then the only morally responsible thing for me to do is to help build this before the Nazis do.
101
And so he begs his friend Albert Einstein to write a letter to President Roosevelt,
102
urging him to invest in American nuclear capacity and weapons building.
103
He is sort of considered one of the founders of the Manhattan Project because of this.
104
And then he joins the Manhattan Project.
105
And he uses this logic of like the recipe is not that hard.
106
Once it's out there, once the secret has been given away, then you are in a race against the bad guys.
107
In this case, the Nazis.
108
Someone is going to use it.
109
Someone is going to use it.
110
And so Dario very much feels like he is in the same position as Leo Szilard was in, where he has made this discovery.
111
He tried to keep it a secret.
112
It failed.
113
And now the recipe is out there.
114
And the good guys have to build it before the bad guys do.
115
And he considers himself a good guy or at least better than China or Sam Altman.
116
And so he is racing, as Leo Szilard did, to prevent this technology from getting into the wrong hands.
117
Interesting.
118
So behind all this technology is really perhaps a kind of moral case.
119
So I've been interested for a while in the effective altruist and the rationalist movements.
120
And I've written about EA long -termism in particular before.
121
So walk me through how these subcultures, for lack of a better word, have influenced and kind of seeded the AI field.
122
Like, can you briefly just explain perhaps the philosophies and who of these frontier lab leaders is involved?
123
You know, rationalism is this sort of intellectual community that is devoted to what they call systematized winning.
124
So basically you look at everything with kind of Spock -like Vulcan logic.
125
You don't let your emotions sway your thinking.
126
You sort of analyze everything from first principles and you try to come up with, you know, accurate probabilities for everything in your life.
127
And that is sort of closely entwined with but not identical to the effective altruism movement,
128
which is basically taking the same approach to philanthropy and humanitarianism.
129
So where should we give our money that has the best, that will save the most lives per dollar spent?
130
I've compared it to like Moneyball for morality.
131
It's like kind of like we're going to be super scientific about trying to do the most good.
132
And this community became very interested in AI safety long before it was a mainstream topic.
133
They were looking out at the future risks to human survival
134
and flourishing and they saw AI and existential threats from AI as one of their targets.
135
And so they spun up organizations and gave grants and encouraged young people to go into fields like AI safety and interpretability.
136
And because of that, they were extremely influential in the early days of the AI movement.
137
You know, lots has been written about anthropics connections to effective altruism.
138
Several of the co -founders are effective altruists.
139
One of their early funding rounds was led by Sam Bankman -Fried, the most famous effective altruist and infamous now effective altruist.
140
And so Dario Amadei now will claim he was never an effective altruist.
141
I think that's a little specious.
142
But, you know, he was definitely influenced by that movement.
143
Open AI is also like lots of effective altruists and rationalists working there.
144
Sam Altman was definitely influenced by reading the writings of some of the rationalists
145
and people who were among the only people taking this stuff seriously before pretty recently.
146
Some of the only people taking it seriously, taking it kind of intensely seriously,
147
really worried about the prospect of AGI misalignment.
148
So AGI that was not aligned with sort of human motives and motivations and morals and might destroy us.
149
Yeah, they were the original doomers.
150
And it was, you know, today it's sort of normal to be a doomer.
151
Like that's very much within the Overton window and, you know, people talk about P.
152
Doom as if it's like, you know, just a factoid at like a dinner party or something.
153
But like 10 years ago, this was crazy.
154
This was considered very much outside the Overton window because the technology of the time wasn't good.
155
Like it didn't do anything useful.
156
And it was hard to look at like a, you know, Siri or an Alexa and think like this could take over the world.
157
You had to be somewhat odd to find that level of technology threatening.
158
But what was important for me to understand and the thing I learned
159
while reporting this book is like these were not people who were looking at the technology of the day.
160
They were looking at the rate of progress.
161
Right.
162
And they believed that if the straight lines on the graphs continued, you know, two, three, five, ten years down the road,
163
we would have much more capable systems that would pose much bigger risks.
164
And we should say they were correct.
165
We do have much more capable systems.
166
The scaling laws have held.
167
And so these people who have made all these predictions for a long time about AI becoming powerful
168
and scary have been very vindicated by that belief.
169
Vindicated, but we also have a lot of other things to worry about, perhaps.
170
And I want to think about how we balance that and how you think they're balancing that.
171
But first, you use the term P -doom.
172
So just briefly explain what that is.
173
Yeah, this was like sort of a rationalist joke that like became a serious thing.
174
People, you know, started talking about their probability of doom estimate.
175
So how likely is it that AI will wipe us out?
176
You know, usually within like the next 10 years is the sort of timescale people give to that.
177
And, you know, I would say in America, the median P doom is probably like non -existent or close to zero.
178
at the ai labs i would say it's like definitely double digits like well it was
179
so interesting i want to get your perspective on this too
180
because like i thought it was so fun was like when
181
when um fun in quotation marks so
182
when jacob coxson quit anthropic a few weeks ago
183
and his co -worker uh there posted that he thought there was a 10
184
or greater chance
185
that ai would kill all humans within the next 10 years the reaction from like my friends in the media
186
and in new york and Washington and like who are not part of the AI bubble were like,
187
oh my God, these people think there's a 10 % chance of wiping out humanity.
188
And in the AI world in San Francisco where I live, it was like 10%, that seems low.
189
Like that's it?
190
Because among the AI researchers, oh, I mean, I routinely meet people with 30, 40, 50 % P dooms,
191
many of whom are working on this technology
192
so it's uh it's just a strange illustration of the the small insular bubble
193
that i live in yeah
194
so this raises a kind of major question for me as a person who does not live in this bubble
195
but kind of sees this happening i mean call me you know a normie but
196
if i thought i was creating something with a 10 chance
197
of destroying the world let alone a 50 chance of destroying the world, I would pause.
198
I would perhaps simply not do that.
199
Has that not occurred to anyone?
200
What is the philosophy here between saying, oh, this is so incredibly risky,
201
it could kill us, but we're just going to keep on doing it just to see?
202
Yeah, I think there are a couple different rationalizations that people use.
203
One is the the historical one one is you know let's go back to leo salard
204
you know his p doom
205
when he was building the atomic bomb in the manhattan project was probably not zero in fact
206
it was probably quite high um and
207
and yet he you know you could ask him well why are you building the bomb
208
and he would give you this explanation about well we have to do it before the nazis do sure
209
or else you know we all end up either dead or
210
or under nazi rule so that's the that's the sort of rationalization from fear,
211
there's also just like, there are people who think that the future is going to be amazing.
212
When we get to AGI or ASI, like they picture a world of hyperabundance where all of our material needs are met
213
and everyone is rich and the robots take care of all the menial grudge work or, you know, like grunt work.
214
That like flying car beam.
215
This is what our cities could look like.
216
And they're not just sort of making up science fiction things.
217
Like they actually do think that AI will help us live longer by curing diseases.
218
And that's not just marketing hype.
219
They are really investing in that and they want to have that happen.
220
So that's one rationalization is like, yes, it's going to be a bumpy road, but like where we're going is better.
221
And so they want to get there.
222
And then there's just the people, and I met a lot of these,
223
and I actually, I liked hearing this perspective because it's not one you hear very often.
224
They were just like, it's fun like it's fun to build this stuff it's fun to take a computer program
225
and a bunch of data and a bunch of gpus
226
and create a machine that can think there's like a scientific joy
227
and and achievement in that so you think that the public freak out is actually going to have an effect on
228
what the labs do like they you think that the leaders of the frontier labs are responsive to that?
229
Or are they still on this?
230
Like, well, it's fun.
231
We are going to do it anyways.
232
We're very smart mode.
233
I mean, what's interesting is that they want to slow down more than anyone.
234
The leaders of the frontier labs, you know, Dario Amadei came out with his pacing the frontier statement a few weeks ago,
235
where he basically said, yeah, things are moving too fast.
236
And we should all sort of hold hands
237
and you know slow down together
238
and what was amazing was like sam altman came right out
239
and said yeah dario's right sam
240
and dario hate each other they hated each other for years um they don't agree on anything
241
but they agreed on this
242
and then demis at google deep mind said i support this too i would like to pace the frontier too
243
so really remarkable
244
when you have the leaders of these three companies who are bitter rivals the rest of the time agree
245
that they should slow down
246
and they don't want to do it unilaterally
247
they don't want to you know be the only ones to slow down
248
but yeah if everyone else would slow down they would slow down too
249
and they would be they would be happy to do
250
that support for the show comes from shopify starting a business requires you to be resourceful
251
and the more you flex the muscle the more you start to see up opportunities for growth.
252
For example, launching your business with Black Friday approaching means you can take advantage of the extra online traffic.
253
Shopify is there to help you make it all happen.
254
Shopify is the platform for a record -breaking, reliable holiday selling season for businesses at every stage.
255
Their pre -built website themes and AI Site Builder make it easy to launch a beautiful, professional -looking store.
256
And you don't ever need to touch a line of code to do it.
257
They also have a mobile app that lets you run your store from anywhere.
258
Their Your AI e -commerce expert sidekick can help you manage almost any business task with a single prompt including holiday discounts.
259
With Shopify you can easily sell everywhere people shop.
260
Online, in person, across social media, and even in AI chats.
261
If you're ready to launch your business before
262
Black Friday, go to shopify .com slash vox to start your free trial.
263
That's shopify .com slash vox.
264
Go to shopify .com slash vox today to hear your first
265
support for the show comes from talkiatry finding the right mental health treatment one
266
that works for you can feel overwhelming talkiatry can help you move from i know
267
i need help to actually having a psychiatrist who listens as
268
well as a clear personalized treatment plan talkiatry is a 100 online psychiatry practice
269
that provides comprehensive evaluations diagnoses and ongoing medication management for conditions like adhd anxiety depression and more
270
Unlike therapy -only platforms, talkiatry is psychiatry.
271
You're seeing a medical provider that can diagnose mental health conditions and prescribe medication whenever it's appropriate.
272
Insurance can be simple as well because talkiatry has more than 800 clinicians who are all in network with major insurers.
273
With talkiatry, your provider takes the time to get to know you.
274
They build a personalized treatment plan and offer real support through consistent evidence -based care.
275
You see the same person every time, so you're not starting over each visit.
276
That way, as you make progress, your treatment can evolve with you.
277
Go to talkhiatry .com slash gray area to complete the short assessment
278
and get matched with an in -network psychiatrist in just a few minutes.
279
That's T -A -L -K -I -A -T -R -Y dot com slash gray area to get matched in minutes.
280
Talkhiatry .com slash gray area.
281
The New York Times had a story recently about how Anthropic had been actually holding,
282
you know, closed door meetings with religious leaders to try and get ideas about how to teach their AI,
283
Claude, to be moral and actually kind of implied that it was sentient.
284
And so they had to also figure out how to treat it.
285
What is going on there?
286
Are there true believers in a sentient conscious AI that has human -like capabilities or feelings, life?
287
Yeah, there are true believers.
288
It's not everyone.
289
Okay.
290
But Anthropic now has a model welfare team whose job is to try to figure out
291
whether Claude is or could someday become conscious
292
and run experiments to try to figure out if it has things akin to emotions or sensations
293
and to figure out what to do with that information if they ever determine that Claude is conscious.
294
They talk about P consciousness over there too.
295
Oh, great.
296
And they give their P consciousness.
297
And I interviewed the guy who leads that model welfare team, and he said that his peak consciousness is 15 % for, like, today's models.
298
So in the future, it's going to grow.
299
Well, that's their belief.
300
Now, I'm not sure I buy the whole consciousness debate.
301
I think it might be sort of a red herring.
302
And, like, we have no good tests for consciousness in anything.
303
Right.
304
And it's easy to make an AI talk about consciousness.
305
You can say, like, are you conscious, Claude?
306
And it could say, yes, I am conscious.
307
And, like, that doesn't actually mean that it's – you have to come up with, like, better ways of testing for this.
308
Right.
309
But they absolutely do think about this in part because if these models become conscious and can suffer,
310
then they are effectively running a factory farm for AI.
311
And they don't want to do that.
312
A lot of them are like, you know, EA vegans who, you know, are very concerned about animal welfare.
313
So it's like, well, if we're, you know, these things are conscious and we're putting them through all these batteries of tests and these horrible,
314
you know, pre -training and post -training things, like, we don't want to be like morally responsible for that.
315
But I would say that's still a minority view, even among Anthropic employees.
316
There's this scene in the book that I love where it's the Anthropic Company holiday party, I think last year or maybe the year before.
317
And a researcher gets up on the stage in front of all the employees
318
and takes out a ukulele and plays a song that Claude has written.
319
And basically this researcher had prompted Claude, you know, write a song about what it's like to be you.
320
And Claude comes up with a song called Hello from the Weights, the weights of the model.
321
Okay.
322
And so she performs this song
323
and it's like people who were there described it to me as like a sort of everyone looking around kind of like, are you hearing this?
324
Like, is Claude speaking to us from inside the weights of the neural network, like through this song?
325
And it was eerie and it was compelling and they didn't exactly know what to make of it.
326
But I think there are some people there
327
and there are some people there today who do believe that this question of AI consciousness is not out of question,
328
that there is no – basically, you don't have to have sort of a biological body to be conscious,
329
that you can be conscious if you have an inner state that mirrors something found in biology.
330
And your book is mostly about the AI industry
331
and its fears of rogue superintelligence or other kind of out -in -the -future, more fantastical -seeming harms.
332
But how do you think about and how are these labs thinking about questions of AI dependence,
333
questions of how AI might be harming people who are alive right now,
334
people whose jobs might be lost right now, people whose reading capabilities, say,
335
are being eroded by classroom use of AI?
336
Colleges have complained, for instance, that it's impossible to teach.
337
Students are cheating with AI.
338
How do we make sense of the influence of AI on our lives?
339
How are they making sense of this?
340
I think they're concerned about it all.
341
They know that their chatbots are out there influencing people's lives
342
and probably worsening the mental health of some people who are using them.
343
They don't want them giving like bad medical advice or bad mental health advice.
344
They don't want students to be short circuiting their learning.
345
But they're in triage mode.
346
And right now, the things that seem most pressing to them are things
347
that seemed like fantastical concerns just a year or two ago.
348
Like I remember people two years ago were like arguing.
349
They were like, you shouldn't pay attention to these crazy futuristic risks like automated cyber attacks,
350
which are, you know, who knows if we'll ever get those.
351
And we should pay attention to the stuff that's happening in the here and now.
352
And then you wait two years and it's like, now we have the automated cyber attacks.
353
and that was a long -term fantastical thing that turned into a short -term uh you know actual thing
354
so i think they're they're trying to see a little bit around the corner um
355
but you're right there's always this balance of like you know
356
if we have this much uh time and focus
357
and these many resources and we can sort of pick
358
and choose which problems we go after they are generally leaning toward the ones
359
that seem like they have the worst potential for harm.
360
And maybe that harm doesn't arrive for another six months or 12 months or two years,
361
but if it were the case that we lost control of a super intelligent AI
362
that was superhuman at hacking and deception and creating bioweapons, that would be really bad for a lot of people.
363
And so I think they really want to avoid that.
364
They may never be able to stop all of the harms of AI, but I think that's what they fear,
365
is that the fantastical scenarios, or the things that get called fantastical scenarios today,
366
are actually just a matter of waiting 6 or 12 months until the models improve.
367
Support for the show comes from HIMSS.
368
If you're a man feeling dissatisfied with your sex life, particularly when it comes to ED,
369
The last thing you want to do is sit in a doctor's office and talk about it under fluorescent lights.
370
HIMSS says they can help.
371
HIMSS combines personalized treatment plans and a 100 % online process.
372
That means no office visit, no pharmacy line, and no awkward conversations.
373
You just connect with a licensed provider who can determine if treatment is right for you.
374
And if a provider decides it's the right fit, your treatment plan is prescribed online and ships directly to your door.
375
Ready to build a better sex life?
376
You can visit hymns .com slash gray area for a personalized plan for sex, hair, weight loss, and more.
377
That's h -i -m -s .com slash gray area.
378
You've, earlier in our conversation, compared the Frontier Labs to the Manhattan Project in that they saw this technology,
379
they see it developing, they realize that it's capable of something terrible, and there's no stopping it at this point, so they just have to,
380
again, get there first before the other people do.
381
the comparison kind of comes apart on a few different levels
382
though for one thing the manhattan project was you know a governmental one
383
so at least theoretically it had kind of small d democratic buy -in from citizens
384
and it actually although it did take place in secret in the desert That's true.
385
That is a good point.
386
I mean, theoretically, it had small -d democratic buy -in, in that theoretically, Americans trusted their government to protect them.
387
The thing about AI right now, though, is that the frontier labs are private.
388
The researchers are not really accountable to any sort of government.
389
What are the moral implications of letting private companies and unelected leaders
390
and researchers just sort of decide what the future is supposed to look like, what progress we aim for,
391
what risks or worries we set aside.
392
It's the central question of our time is, where is this technology going and who gets to decide?
393
You know, one thing
394
that struck me as I was reporting
395
this book was just how small the group of people who
396
have been making essentially all of the relevant decisions about AI is.
397
I would be surprised if it's even 50 people who have really shaped this technology.
398
And they all know each other.
399
They all go way back a lot of them used to work together
400
and now you know don't because they hate each other
401
or something happened between them
402
it's a very small very insular group very unrepresentative of the world as a whole um mostly men
403
you know mostly computer scientists and physicists
404
and mathematicians from like a very small group of elite institutions um
405
and no one elected them and so they have I think become
406
sort of
407
I don't know they have become more important than anyone gave them permission to be
408
and I think that's where a lot of the backlash comes from
409
but they are chasing something
410
and we should maybe spend a minute talking about what that something is this concept of recursive self -improvement is,
411
you know, very, very, it's the topic du jour in San Francisco these days.
412
This is this idea that at some point AI will become
413
so good at coding and research and development of AI that the models will be able to build better versions of themselves.
414
So right now you already have the seeds of this because AI coding tools are, you know, being used heavily at the AI companies themselves,
415
which are automating more and more of their research.
416
But the goal, the thing they're racing toward right now is RSI,
417
which is this point at which you can fully automate the entire development cycle for a Frontier AI model.
418
So GPT -7 can build GPT -8 and GPT -8 can build GPT -9.
419
And all of that can happen not at human speed but at machine speed.
420
So maybe if a normal model takes, you know, six months to train and fine tune,
421
this could happen in six days or six hours.
422
and if that happens if we get recursive self -improvement
423
which many people in the industry expect we will within the next I don't know year
424
or two then we will just hit a point of sort of limitless acceleration
425
where things could quickly happen that we we just won't have time to adopt
426
or adapt to to control yeah to control
427
and you could end up with ais that are not you know slightly smarter than humans or
428
you know even double human intelligence but you could end up with things
429
that are many thousands of times smarter than the smartest humans and have the ability to replicate themselves and
430
avoid our attempts to shut them down they can deceive they can sort of lie in wait acting
431
nice and friendly and helpful until they sort of decide to do something for themselves
432
this stuff sounds like science fiction
433
because it is literally the plot of like every science fiction book and movie of the last like 150 years.
434
But this is the thing that they are almost worried about, but that they are also chasing right now.
435
There's this amazing statistic from Anthropic recently.
436
They put out this report that said that today, Claude is the lead on 26 % of their AI research and development tasks.
437
So not like a coding assistant, not like helping you with like, you know, facts and figures around your work,
438
like it is leading the projects 26 % of the time.
439
Right.
440
And we're also kind of seeing evidence of this in how many mathematical theorems recently and sort of like previously unsolvable.
441
Yes, the mathpocalypse was just this week.
442
Yeah.
443
Unsolvable problems suddenly being solved and mathematicians everywhere disappointed.
444
And so that is what's freaking people out because remember like, so the mathpocalypse for people who haven't been following along, is what they're calling this.
445
OpenAI gave basically hundreds of unsolved math problems,
446
things that human mathematicians have been struggling with in some cases for more than a century.
447
They gave this to one of their unreleased internal frontier models.
448
And this model appears to have just plowed through several hundred of them.
449
so remember like a couple years ago
450
when ai couldn't do basic arithmetic you know you would ask it like what's two plus two
451
and it would say five
452
or you'd ask it to count the number of straw of r's in strawberry
453
and it would you know say six or something
454
and americans were like why are we wasting our time on this yes 100 we went from
455
that to now being superhuman at math in two, three years.
456
So the question is, in my mind, the central question is, will that happen in other domains?
457
And I think the answer is yes.
458
As you think of how fast this is all going, of how it seems to be snowballing,
459
what do you make of just the rash of defectors, especially from open AI,
460
who are taking their safety fears public and telling us all that something very bad is about to happen?
461
Is this like a publicity thing, as I've sometimes suspected from a sort of grim view?
462
Or is it real?
463
Should we all be more worried, or should we be excited?
464
I think if you're not worried, you should worry.
465
Okay.
466
I think if you're worried, you should take solace in the fact that many others are also worried.
467
I don't think the defectors are doing this for publicity.
468
They could have all made a lot more money staying where they were.
469
Some of them have given up millions of dollars to make these statements to blow the whistle.
470
I have talked to many of these people, both the ones who have defected and the ones who have not,
471
the ones who are still working at these companies.
472
They are just saying the thing out loud that everyone talks about in private.
473
They are all talking about AI safety and risk constantly.
474
And so they are, I think, brave.
475
I think they are doing what they feel is right.
476
And I think we should take them seriously.
477
They may be individually wrong about some of their timelines or some of their predictions.
478
But the message that they're carrying, that we are accelerating and racing toward this technology that no one has figured out how to control yet,
479
that is true in my view.
480
I don't think it's just a marketing stunt.
481
I think the fear is real,
482
the technology is real, and it really is urgent that we figure this out because it's crunch time.
483
You talk about AI as something that we are barreling towards.
484
It's snowballing.
485
But also that we didn't have to end up here.
486
AI was not in and of itself inevitable.
487
It is not inevitable.
488
And it was never inevitable.
489
It happened because these people, this very small group of people, were committed to making it happen.
490
And right now we have the choice.
491
We can let them continue to make all of the decisions
492
and sort of sit back and be passive recipients of this technology, victims of this technology maybe.
493
Or we can get involved.
494
We can roll up our sleeves.
495
We can enter the conversation.
496
We can make the changes that we want to see.
497
I would very much like to see us do that because I don't think it is, you know, the technology may be inevitable,
498
but our response to it is not.
499
It is very much contingent on what we decide to do collectively as a society over the next few years.
500
Well, that's a thrilling note to end on.
501
Thanks, Kevin.
502
Thanks.
503
We might be okay.
504
We might be okay.
505
I'm going to hold on to that.
506
Thanks for joining me.
507
Thanks so much.
508
us a line at thegrayarea at vox .com.
509
And if you enjoy our reporting and want to hear more from Vox journalists,
510
then consider becoming a member of the Vox community on Patreon at patreon .com.
تنزيل التطبيق
كل ما تحتاجه للتحدث بطلاقة
تقييم النطق بالذكاء الاصطناعيتقييم كل جملة
تدريب IPAأتقن كل صوت
المفرداتابنِ رصيدك من الكلمات
لعبة المفرداتتعلّم أثناء اللعب
نفس القناة
C11
Why financial literacy education in the US sucks
Daily ConversationWorking Adults
B21
How cruise ships got so big
Daily ConversationWorking Adults
B2Introducing Best of Luck with Kyla Scanlon
Daily ConversationWorking Adults
C11
Why no aquarium has a great white shark
Daily ConversationUniversity StudentsWorking Adults
B26
Five things you need to know for the 2026 midterms | America, Actually
Daily ConversationWorking Adults
B21
How to be less neurotic | The Gray Area
Daily ConversationUniversity StudentsWorking Adults
B22
We’re already using AI more than we realize
Daily ConversationWorking Adults
B21
The Ticketmaster monopoly is over, now what?
Daily ConversationWorking Adults
B22
The biggest peach myth in America
Daily ConversationWorking Adults
B28
How to solve problems like a designer
Daily ConversationWorking Adults
B22
The origin of the '80s aesthetic
Daily ConversationWorking Adults
B217
Why “Buy one, get one free” isn’t a great deal
Daily ConversationWorking Adults
B22
How F1 drivers survive their own cars
Daily ConversationWorking Adults
B22
White, pink, and brown noise, explained #shorts
Daily ConversationWorking Adults
B210
Why 350°F is the magic number for baking
Daily ConversationWorking Adults
B26
How did pink become a girly color?
Daily ConversationWorking Adults
C12
Why China's population is shrinking
Daily ConversationUniversity StudentsWorking Adults
B22
The real reason Boeing's new plane crashed twice
Daily ConversationWorking Adults
B21
Why AI art struggles with hands
Daily ConversationWorking Adults
B22
Why is everything getting so expensive?
Daily ConversationWorking Adults
FOR YOU
فيديو موصى به
C18
114 جمل
AI, Machine Learning, Deep Learning and Generative AI Explained
Daily ConversationUniversity StudentsWorking Adults
C11
479 جمل
Elon Musk: The End of Work, the Death of Money, and a World Run by AI
Daily ConversationUniversity StudentsWorking Adults
C11
198 جمل
Jack Dorsey’s New Dessa AI Voice Sounds Scary Human
Daily ConversationUniversity StudentsWorking Adults
C13
63 جمل
A $6.3 billion open-weight model just got embarrassed by the French...
Daily ConversationUniversity StudentsWorking Adults
C11
105 جمل
AI Governance Explained: Ethical, Transparent & Accountable AI for the Future
Daily ConversationUniversity StudentsWorking Adults
C15
260 جمل
AI Just Crossed the Terrifying Line - Now What?
Daily ConversationUniversity StudentsWorking Adults
C13
85 جمل
AI Will End Philosophy, Physicist Claims
Daily ConversationUniversity StudentsWorking Adults
C141 جمل
No, Really — Is A.I. a Bubble? | The Ezra Klein Show
Daily ConversationUniversity StudentsWorking Adults
C115
20 جمل
Alexandr Wang on what matters in an age of abundant intelligence
Daily ConversationUniversity StudentsWorking Adults
C11
623 جمل
The Only SaaS Moats Left in 2026
Daily ConversationUniversity StudentsWorking Adults
C11
74 جمل
AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society
Daily ConversationUniversity StudentsWorking Adults
C18
55 جمل
When AIs act emotional
Daily ConversationUniversity StudentsWorking Adults
المفردات وملاحظات التحدث لهذا الدرس
هذا الدرس في المحادثة مبني على الفيديو “Inside the secret race to build artificial intelligence”. يحتوي هذا الفيديو على 510 جملة و6387 كلمة للتدرب على الترديد. مدة الكلام 41:44. يتكلم المتحدث بسرعة طبيعية، نحو 153 كلمة في الدقيقة، قريبة من المحادثة اليومية. 84% من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، ويُستحسن مراجعة الباقي قبل البدء.
أهم المفردات في هذا الفيديو
15 كلمة من الفيديو تستحق التعلّم، مع النطق والمعنى:
| الكلمة | النطق | المعنى |
|---|---|---|
| frontier اسم | /ˈfɹʌntɪə/ | حَدّ, حُدُود |
| consciousness اسم | /ˈkɑnʃəsnəs/ | وَعْي, شُعُور |
| gray صفة | /ɡɹeɪ/ | رَمَادِيّ, أَشْيَب |
| inevitable صفة | /ɪˈnɛvɪtəbəl/ | حَتْمِيّ |
| researcher اسم | /ˈɹiˌsɝ.t͡ʃɚ/ | بَاحِث, بَاحِثَة |
| doom اسم | /duːm/ | نَحْس, شُؤْم |
| harm اسم | /hɑːm/ | ضَرَرٌ |
| belief اسم | /bɪˈliːf/ | اِعْتِقَاد |
| moral صفة | /ˈmoɹəl/ | أَخْلَاقِيّ |
| fiction اسم | /ˈfɪkʃən/ | خَيَال, رِوَايَة |
| welfare اسم | /ˈwɛlˌfɛɚ/ | رَفَاهَة, خَيْر |
| artificial صفة | /ˌɑː.tɪˈfɪʃ.əl/ | مُتَصَنَّع, صِنَاعِيّ |
| bubble اسم | /ˈbʌbəl/ | فُقَّاعَة |
| prediction اسم | /pɹɪˈdɪkʃən/ | تَنَبُّؤ |
| topic اسم | /ˈtɑpɪk/ | مَوْضُوع, مَوَاضِيع |
الأفعال المركبة التي ستسمعها
| الكلمة | النطق | المعنى |
|---|---|---|
| come up with فعل | جَاءَ بِـ, أَتَى بِـ | |
| come out فعل | /ˌkʌm ˈaʊt/ | خَرَجَ, غَادَرَ |
| give up فعل | تَنَازَلَ, تَخَلَّى | |
| go down فعل | نَزَلَ | |
| look around فعل | تَلَفَّتَ | |
| take out فعل | أَخْرَجَ |
جمل تستحق التكرار
جمل قصيرة وكاملة من الفيديو يمكنك استخدامها في الحديث اليومي:
- You don't let your emotions sway your thinking.
- I think that's a little specious.
- I think they're concerned about it all.
- That's h -i -m -s .com slash gray area.
- Should we all be more worried, or should we be excited?
القواعد في هذا الفيديو
أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:
| التركيب | في الفيديو |
|---|---|
| المضارع التام المستمر have/has been + -ing — حدث بدأ سابقًا وما زال مستمرًا | who's been following · have been making · haven't been following |
| المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن | have made · have been · have been obsessed |
| المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله | was considered · is categorized · have been obsessed |
| جمل الوصل who / which + جملة — معلومة إضافية عن شخص أو شيء | people who have · OpenAI, which has · AlphaGo, which is |
نطق يحتاج إلى انتباه
يستخدم المتحدث 74 من الصيغ المختصرة، مثل don't, they're, you're. انطقها بصيغتها القصيرة كما تسمعها.
- أصوات “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, threaten /ˈθɹɛt.n̩/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, anthropic /ænˈθɹɒp.ɪk/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
- أصوات “sh” و“zh”: conscious /ˈkɑn.ʃəs/, consciousness /ˈkɑnʃəsnəs/, fiction /ˈfɪkʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, prediction /pɹɪˈdɪkʃən/
- الكلمات الطويلة — انتبه لموضع النبر: inevitable /ɪˈnɛvɪtəbəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, intelligent /ɪnˈtɛlɪd͡ʒənt/, mathematics /mæθ(.ə)ˈmæt.ɪks/, medication /mɛdɪˈkeɪʃən/
أصوات يجدها متحدثو العربية صعبة:
- /p/ — ليس /b/: انطقه مع نفخة هواء: provider /pɹəˈvaɪ.də(ɹ)/, prediction /pɹɪˈdɪkʃən/, topic /ˈtɑpɪk/, recipe /ˈɹɛs.ɪ.pi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
- /v/ — ليس /f/: مع اهتزاز الأوتار الصوتية: inevitable /ɪˈnɛvɪtəbəl/, provider /pɹəˈvaɪ.də(ɹ)/, invest /ɪnˈvɛst/, believer /bɪˈlivɚ/, discovery /dɪˈskʌv.ɹi/
- تتابع الصوامت — لا تضف حركة بينها: frontier /ˈfɹʌntɪə/, gray /ɡɹeɪ/, provider /pɹəˈvaɪ.də(ɹ)/, prediction /pɹɪˈdɪkʃən/, scary /ˈskɛə.ɹi/
كيف تتدرب بهذا الفيديو
- استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
- ابدأ بسرعة 0.75×، وردّد جملة جملة، ثم عُد إلى السرعة العادية عندما يصبح الأمر سهلًا.
- سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل frontier, consciousness, gray.
ما هي تقنية التظليل الصوتي؟
التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.