Pratique du Shadowing: Did Google just kickstart the intelligence explosion? - Apprendre l'anglais à l'oral avec la vidéo

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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Vocabulaire et conseils d’expression pour cette leçon

Cette vidéo contient 54 phrases et 1063 mots à répéter en shadowing. La partie parlée dure 4:56. Le locuteur parle vite, environ 215 mots par minute : attendez-vous à des liaisons et à des sons réduits. Seuls 79 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.

Vocabulaire clé de cette vidéo

15 mots moins courants de la vidéo, avec leur prononciation et leur sens :

loop /luːp/ (nom) — boucle. A length of thread, line or rope that is doubled over to make an opening.
exploration /ˌɛkspləˈɹeɪʃən/ (nom) — exploration. The process of exploring.
mathematics /mæθ(.ə)ˈmæt.ɪks/ (nom) — mathématiques. An abstract representational system studying numbers, shapes, structures, quantitative change and relationships between them.
algorithm /ˈælɡəɹɪðm̩/ (nom) — algorithme. 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/ (verbe) — découvrir. To find or learn something for the first time.
static /ˈstæt.ɪk/ (adjectif) — statique. Unchanging; that cannot or does not change.
invention /ɪnˈvɛnʃən/ (nom) — invention. Something invented.
researcher /ˈɹiˌsɝ.t͡ʃɚ/ (nom) — chercheur, chercheuse. One who researches.
breakthrough /ˈbɹeɪk.θɹuː/ (nom) — percée, trouée. An advance through and past enemy lines.
cache /kæʃ/ (nom) — cache, mémoire cache. Such a store of physical supplies, placed by humans or other animals for practical reasons.
impressive /ɪmˈpɹɛsɪv/ (adjectif) — impressionnant, épatant. Making, or tending to make, a positive impression; having power to impress.
argue /ˈɑɹ.ɡju/ (verbe) — affirmer, débattre. To show grounds for concluding (that); to indicate, imply.
cloud /ˈklaʊ̯d/ (nom) — nuage. A visible mass of water droplets suspended in the air.
infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (nom) — infrastructure. An underlying base or foundation for a building, organization, or system.
discovery /dɪˈskʌv.ɹi/ (nom) — découverte. Something discovered.

Les verbes à particule que vous entendrez

break down (verbe) — tomber en panne. To stop functioning.
check out (verbe) — mater, regarder. To record the departure or withdrawal of someone or something (such as guests, employees, books, etc.).
come down (verbe) — descendre, tomber. To descend, fall down, collapse.
figure out (verbe) — cerner. To come to understand; to discover or find a solution; to deduce.
go back (verbe) — rentrer, retourner. To return to a place or state after having been there at a previous time.
rely on (verbe) — se fier à, compter sur. To be confident in.

Prononciation à surveiller

Le locuteur utilise 11 contractions et formes réduites, comme I'm, you're, don't. Prononcez-les sous leur forme courte, telles que vous les entendez.

Les sons « th »: mathematics /mæθ(.ə)ˈmæt.ɪks/, algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/, hypothesis /haɪˈpɒθɪsɪs/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
Les sons « sh » et « zh »: exploration /ˌɛkspləˈɹeɪʃən/, invention /ɪnˈvɛnʃən/, cache /kæʃ/, implementation /ˌɪmplɪmənˈteɪʃən/, mushroom /ˈmʌʃˌɹuːm/
Mots longs — placez bien l’accent: exploration /ˌɛkspləˈɹeɪʃən/, mathematics /mæθ(.ə)ˈmæt.ɪks/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, implementation /ˌɪmplɪmənˈteɪʃən/, intelligent /ɪnˈtɛlɪd͡ʒənt/

Comment s’entraîner avec cette vidéo

Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme loop, exploration, mathematics.

La grammaire de cette vidéo

Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :

Structure	Dans la vidéo
« Used to » used to + verbe — une habitude ou un état passé qui n’est plus vrai	used to improve · used to front
Present perfect have/has + participe passé — une action passée qui compte encore maintenant	has made · has been
Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait	is called · are cached

Qu'est-ce que la technique du Shadowing ?

Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.

Technique du shadowing : lire le guide complet étape par étape →

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Vocabulaire et conseils d’expression pour cette leçon

Vocabulaire clé de cette vidéo

Les verbes à particule que vous entendrez

Prononciation à surveiller

Comment s’entraîner avec cette vidéo

La grammaire de cette vidéo

Qu'est-ce que la technique du Shadowing ?