تدريب Shadowing: Did Google just kickstart the intelligence explosion? - تعلم التحدث بالإنجليزية عبر الفيديو

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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المفردات وملاحظات التحدث لهذا الدرس

يحتوي هذا الفيديو على 54 جملة و1063 كلمة للتدرب على الترديد. مدة الكلام 4:56. يتكلم المتحدث بسرعة، نحو 215 كلمة في الدقيقة، لذا ستسمع كثيرًا من وصل الكلمات واختزال الأصوات. 79% فقط من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، فالمفردات صعبة نسبيًا.

أهم المفردات في هذا الفيديو

15 كلمة أقل شيوعًا من الفيديو، مع النطق والمعنى:

loop /luːp/ (اسم) — عُرْوَة, عُقْدَة. A length of thread, line or rope that is doubled over to make an opening.
exploration /ˌɛkspləˈɹeɪʃən/ (اسم) — اِسْتِكْشَاف. The process of exploring.
mathematics /mæθ(.ə)ˈmæt.ɪks/ (اسم) — رِيَاضَة, رِيَاضِيَّات. An abstract representational system studying numbers, shapes, structures, quantitative change and relationships between them.
algorithm /ˈælɡəɹɪðm̩/ (اسم) — خَوَارِزْمِيَّة. 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/ (فعل) — اِكْتَشَفَ. To find or learn something for the first time.
invention /ɪnˈvɛnʃən/ (اسم) — اِخْتِرَاع. Something invented.
researcher /ˈɹiˌsɝ.t͡ʃɚ/ (اسم) — بَاحِث, بَاحِثَة. One who researches.
breakthrough /ˈbɹeɪk.θɹuː/ (اسم) — اِخْتِرَاق. An advance through and past enemy lines.
argue /ˈɑɹ.ɡju/ (فعل) — جَادَلَ, حَاجَّ. To show grounds for concluding (that); to indicate, imply.
cloud /ˈklaʊ̯d/ (اسم) — سَحَاب, غَيْم. A visible mass of water droplets suspended in the air.
infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (اسم) — بِنْيَة تَحْتِيَّة. An underlying base or foundation for a building, organization, or system.
discovery /dɪˈskʌv.ɹi/ (اسم) — اِكْتِشَاف. Something discovered.
custom /ˈkʌstəm/ (اسم) — عَادَة, عُرْف. Frequent repetition of the same behavior; way of behavior common to many; ordinary manner; habitual practice; method of doing, living or behaving.
wire /ˈwaɪɚ/ (اسم) — سِلْك. Metal formed into a thin, even thread, now usually by being drawn through a hole in a steel die.
whoever /huˈɛvɚ/ — مَنْ. Who ever: an emphatic form of who.

الأفعال المركبة التي ستسمعها

talk about (فعل) — تَكَلَّمَ فِي, تَكَلَّمَ عَلَى. Used to draw attention to the speaker's characterization of someone or something.

نطق يحتاج إلى انتباه

يستخدم المتحدث 11 من الصيغ المختصرة، مثل I'm, you're, don't. انطقها بصيغتها القصيرة كما تسمعها.

أصوات “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/, hypothesis /haɪˈpɒθɪsɪs/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
أصوات “sh” و“zh”: exploration /ˌɛkspləˈɹeɪʃən/, invention /ɪnˈvɛnʃən/, cache /kæʃ/, implementation /ˌɪmplɪmənˈteɪʃən/, mushroom /ˈmʌʃˌɹuːm/
الكلمات الطويلة — انتبه لموضع النبر: exploration /ˌɛkspləˈɹeɪʃən/, mathematics /mæθ(.ə)ˈmæt.ɪks/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, implementation /ˌɪmplɪmənˈteɪʃən/, intelligent /ɪnˈtɛlɪd͡ʒənt/

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 loop, exploration, mathematics.

القواعد في هذا الفيديو

أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:

التركيب	في الفيديو
“used to” used to + فعل — عادة أو حالة في الماضي لم تعد قائمة	used to improve · used to front
المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن	has made · has been
المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله	is called · are cached

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.

تقنية الشادوينغ: اقرأ الدليل الكامل خطوة بخطوة →

تدريب Shadowing: Did Google just kickstart the intelligence explosion? - تعلم التحدث بالإنجليزية عبر الفيديو

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المفردات وملاحظات التحدث لهذا الدرس

أهم المفردات في هذا الفيديو

الأفعال المركبة التي ستسمعها

نطق يحتاج إلى انتباه

كيف تتدرب بهذا الفيديو

القواعد في هذا الفيديو

ما هي تقنية التظليل الصوتي؟