शैडोइंग अभ्यास: Did Google just kickstart the intelligence explosion? - वीडियो के साथ अंग्रेजी बोलना सीखें

In 1965, a straight British mathematician named I.J.

Goode, who had spent World War II fighting Nazis next to a gay Alan Turing in Bleschley Park, wrote that the first ultra-intelligent machine would be the last invention man ever needed to make,

because once an AI gets good enough to improve itself, every improvement makes it better at improving, which makes it better at improving, which, you get the idea.

This is called RSI, and it's been the wet dream of AI researchers ever since.

Well, last week, 33 researchers from ByteDance, Tsinghua, and a few other Chinese labs published a paper called The Last AI Built by Humans.

It laid out a five-stage roadmap for RSI, where in the final stage, the AI rewrites the process it used to improve itself, and we all get reassigned to the countryside for agricultural work.

Then on Sunday, Google DeepMind

and the University of Maryland responded by dropping a similar paper of their own called Wet Dream RSI.

In it, they claim that by turning an AI's old discovery logs into a simulator

and letting it dream up thousands of new search strategies inside of it, the AI got better at discovering things without anyone ever touching the model itself.

In today's video, we'll break down how Dream RSI works under the hood, and decide whether an agent being able to rewrite its own exploration policy is actually RSI,

or just more hype slop.

It is September 17th, 2026, and you're watching the Code Report.

Every time ai has made a mathematics breakthrough the process has been the same one

that alpha evolved popularized last year you take a coding agent handed a problem

and a scoring function then run a loop where it proposes a solution evaluates it reads the feedback

and tries again a few thousand times this process is what discovered the jacobian conjecture this summer

and what openai used to front run the Navier-Stokes problem earlier this month.

But the most interesting part of the process is one no one really talks about called exploration policy.

The idea is that at every step of the loop, there's a decision to make about what the agent tries next.

For example, say the loop is optimizing for a matching algorithm for horses.

If one attempt pairs up horses slightly better than others, does the agent keep building on it, or does it start over with something that could be better?

And if an attempt crashes before a single horse gets matched, is it the whole idea that's bad, or just the implementation.

Until this week, this exploration policy was hard coded into the loop by whoever set it up.

But what the DeepMind team figured out is that if you save everything from every attempt, like the code it wrote, the score it got, and whether or not it crashed,

you don't actually need to touch the model again to test a new policy.

You can just show the new policy the old runs

that are cached on the disk and let it decide where to go from there.

And because that costs nothing, the agent can test thousands of different policies against the same run

and then whichever one would have reached the best result in the fewest attempts.

Then it deploys that policy on the next run, saves that run too, and repeats the process again.

The paper calls this process Dreaming, and to test it, they pointed Gemini at eight different problems across algorithm design and mathematics,

then ran the same setup with a fixed policy to see if the Dreaming version could beat it.

I won't bore you with the TMBBs, but the most impressive one was that it wrote a lasso solver

that beats Python's standard machine learning library in about 300 tries, where the static policy needed 550, and the previous record holder needed about 51,000.

And because we're living in hell, the most interesting part was the prompt.

It basically begs the agent to read every past attempt before writing any code, to stop making tiny tweaks to the same idea over and over, and to pinky promise not to kill any processes.

So is any of this actually RSI?

By IJ Good's definition, no, because the whole point is that the thing doing the improving gets smarter each round.

In this case, the model that writes each new exploration policy is still the same Gemini, so it can never find a solution that it wasn't already capable of writing.

It just finds them faster and with fewer wasted attempts.

But with that said, that's also true of every AI math breakthrough we've had this year.

The Jacobian conjecture, Navier-Stokes, and progress on the Ryman hypothesis all came from static models wrapped in a custom harness,

with sub-agents, swarms, and orchestration doing most of the heavy lifting, and the weights only get better when a human goes back and trains the next model on what the swarm found.

So if you gave IJ mushrooms and convinced him that a human in the loop still counts, he might agree that this is the last invention man ever needed to make, but when he came down,

he'd probably argue that this is just a cool search algorithm with some caching, And that's why you need to check out Blacksmith, the sponsor of today's video.

It's a drop-in replacement for GitHub Runners that lets you run your GitHub actions twice as fast while costing 75% less.

And they just launched Codesmith, a cloud coding agent that knows your repos and CI runs, so you can ask it to build something from GitHub, the web, or Slack like I'm doing here.

I'm asking it to add a new AI provider to my app

and wire up the API key in my infrastructure repo and it can open a PR in each one,

then fix failing tests or address review comments without relying on messages between bots.

You can also ask it to recommend better runner sizes for your CI history and turn those changes into a pull request, so you're not renting a supercomputer to check your semicolons.

Try it out for free and get 3,000 GitHub Actions minutes at the link below.

This has been The Code Report, thanks for watching, and I will see you in the next one.

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो में शैडोइंग के लिए 54 वाक्य और 1063 शब्द हैं। बोला गया हिस्सा 4:56 लंबा है। वक्ता तेज़ बोलता है, लगभग 215 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 79% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 कम प्रचलित शब्द, उच्चारण और अर्थ के साथ:

loop /luːp/ (संज्ञा) — कुंडली. A length of thread, line or rope that is doubled over to make an opening.
mathematics /mæθ(.ə)ˈmæt.ɪks/ (संज्ञा) — गणित, गणित-विधा. An abstract representational system studying numbers, shapes, structures, quantitative change and relationships between them.
algorithm /ˈælɡəɹɪðm̩/ (संज्ञा) — अल्गोरिद्म. A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input…
discover /dɪˈskʌvə/ (क्रिया) — खोज निकालना, खोज करना. To find or learn something for the first time.
static /ˈstæt.ɪk/ (विशेषण) — गतिहीन, अचल. Unchanging; that cannot or does not change.
invention /ɪnˈvɛnʃən/ (संज्ञा) — आविष्कार. Something invented.
researcher /ˈɹiˌsɝ.t͡ʃɚ/ (संज्ञा) — शोधकर्ता, शोधक. One who researches.
cloud /ˈklaʊ̯d/ (संज्ञा) — मेघ, बादल. A visible mass of water droplets suspended in the air.
infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (संज्ञा) — अवसंरचना. An underlying base or foundation for a building, organization, or system.
discovery /dɪˈskʌv.ɹi/ (संज्ञा) — खोज, आविष्कार. Something discovered.
custom /ˈkʌstəm/ (संज्ञा) — प्रथा, रीति. Frequent repetition of the same behavior; way of behavior common to many; ordinary manner; habitual practice; method of doing, living or behaving.
wire /ˈwaɪɚ/ (संज्ञा) — तार. Metal formed into a thin, even thread, now usually by being drawn through a hole in a steel die.
log /lɔɡ/ (संज्ञा) — कुंदा, लट्ठा. The trunk of a dead tree, cleared of branches.
feedback /ˈfiːdˌbæk/ (संज्ञा) — प्रतिपुष्टि, प्रतिक्रिया. Critical assessment of a process or activity or of their results.
implementation /ˌɪmplɪmənˈteɪʃən/ (संज्ञा) — कार्यान्वयन. The process of moving an idea from concept to reality. In business, engineering and other fields, implementation refers to the building process rather than the…

वीडियो में आने वाली फ़्रेज़ल वर्ब

drop in (क्रिया) — आ टपकना. To arrive casually and unannounced, with little or no warning; also, to visit without an appointment.

ध्यान देने योग्य उच्चारण

वक्ता 11 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे I'm, you're, don't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: mathematics /mæθ(.ə)ˈmæt.ɪks/, algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/, hypothesis /haɪˈpɒθɪsɪs/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
“sh” और “zh” की ध्वनियाँ: exploration /ˌɛkspləˈɹeɪʃən/, invention /ɪnˈvɛnʃən/, cache /kæʃ/, implementation /ˌɪmplɪmənˈteɪʃən/, mushroom /ˈmʌʃˌɹuːm/
लंबे शब्द — बलाघात सही रखें: exploration /ˌɛkspləˈɹeɪʃən/, mathematics /mæθ(.ə)ˈmæt.ɪks/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, implementation /ˌɪmplɪmənˈteɪʃən/, 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 loop, mathematics, algorithm 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
“used to” used to + क्रिया — बीती आदत या स्थिति जो अब नहीं है	used to improve · used to front
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	has made · has been
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is called · are cached

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: Did Google just kickstart the intelligence explosion? - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

वीडियो में आने वाली फ़्रेज़ल वर्ब

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

इस वीडियो का व्याकरण

शैडोइंग तकनीक क्या है?