Shadowing-Übung: Gemini 4 Argon explained in 5min.. - Englisch Sprechen Lernen mit Video

Übungsmodus wählen

Jeder Modus öffnet einen Übungsraum im Vollbild.

56 Sätze

Wenn Sätze zu kurz oder zu lang sind, klicke auf Edit, um sie anzupassen.

It's been about seven months since Google announced a new flagship model, Gemini 4 Argon.

Even during Google I .O back in May, I expected Google to drop Gemini 3 .5 Pro.

But Google has been releasing a lot of flash models instead,

and a lot of them basically struggle to get market adoption given a variety of other flagship models

and cheaper alternatives from Chinese open models.

And Google finally made a flagship model with the codename Argon, and the preliminary benchmarks show a pretty strong number as you can see.

The first thing you might have noticed here is coding benchmarks, more specifically, scoring 77 .9 % on DeepSuite,

which would put Gemini 4 Argon above every other model we currently have.

But it's always good to have some healthy skepticism here.

Recently, EPOC found issues on DeepSuite Benchmark, finding 23 tasks that are flawed.

And looking back to when the benchmark was actually released back in May, the entire 113 tasks and their solutions are made public,

which means labs that have access to these could easily study them during post -training and benchmarks their models around public benchmarks.

And when we look at other benchmarks that Google reported here, a large majority of them are public.

So the further we go out in time, labs that release their models later have a slight advantage to potentially include them during post -training.

And of course, as users, all of this makes it really difficult to track real progress in AI since

so many public benchmarks like DeepSuite can be contaminated pretty fast.

Not only that, benchmarks get saturated a lot faster now than before, which forces benchmarks to be refreshed more frequently.

So consumers now carry a huge burden keeping track of real scientific progress in AI.

But regardless of the case, achieving this kind of leap in benchmark is a huge improvement from Google.

Now, when we take a step back and look at how Google is competing with other labs, a lot of people still primarily use clock code,

codecs, or open harnesses when it comes to coding.

And despite being a strong model, Google still hasn't really solved making their harness anti -gravity a truly competitive option for developers.

For a recent report, Codex has more than doubled the amount of weekly active users at 5 million compared to 2 .4 million for anti -gravity.

And on face value, Gemini 4 Argon currently undercuts both Anthropic

and OpenAI when it comes to the cost of intelligence until the temporary discount ends and the price doubles again.

But the cost benefit that we find here doesn't actually translate in Pareto Frontier, as you can see.

Argon falls below the Pareto Frontier, showing you that Anthropic and OpenAI still leads ahead in both the harness and model adoption.

Now, one angle we should think about, though, is the upcoming IPO for Anthropic and possibly OpenAI.

While OpenAI and Anthropic have a huge incentive to subsidize the model through subscription pretty heavily to grow their users,

Google already sits on top of one.

And given that Google is already a publicly traded company,

they want to be more careful about how to allocate their existing compute towards actually creating value within their ecosystem,

which could help increase their perceived revenue.

Even looking at how Argon is being rolled out in segments, the model is set to be made available to ultra -subscription users

and paid API users where there's already a huge revenue being

captured instead of fighting the subscription rates in channeling their compute towards that.

On a recent Google's Q2 earning call, they said that their API is processing 22 billion tokens per minute,

which is 6 billion more than the previous quarter.

That's more than 11 quadrillion tokens per year in just API channel alone.

And when you start factoring in other channels that Google owns in its distribution,

we really get a closer picture into what Google really means when they said that they're supply constrained.

And eventually, OpenAI and Anthropic will have to solve this scaling problem while maintaining healthy margins at the same time,

not just subsidizing subscription to capture more users.

So looking at Gemini for Argon, Google is demonstrating their capability in keeping pace at the frontier

while they also try to figure out how to capture value at scale.

One interesting note on Argon is how Google extended their output tokens from 64 ,000 to 1 million tokens.

This might not sound like a big deal, but it's definitely a huge move from Google

because maintaining coherence as autoregressive models generate longer and longer tokens get increasingly difficult the longer the output tokens get.

Scientists like Yen Le Koon made criticism on autoregressive models for this very reason,

where autoregressively generating one token after another can compound errors over long trajectory, let alone 1 million from Argon.

If you can imagine each token that's 99 % reliable, chaining 100 independent steps together would leave about 37 % chance of every step being correct.

Now, of course, that's just in theory, assuming that every token error is independent.

But the fact

that Google has enough confidence to extend their commercial product to

1 million output tokens really opens up door for longer trajectory tasks like code migration

and long -running research tasks that could benefit from a model that can generate more.

App herunterladen

Alles, was du zum fließenden Sprechen brauchst

KI-AussprachebewertungJeder Satz bewertet

IPA-TrainingJeden Laut meistern

WortschatzBaue deinen Wortschatz auf

VokabelspielSpielend lernen

QR scannen

Download on theApp Store Get it onGoogle Play

Gleicher Kanal

World Models explained in 10min..

Business EnglishUniversity StudentsWorking Adults

FOR YOU

Empfohlenes Video

90 Sätze

Expensive RTX 5090 for LLMs? NO. Use This Instead. (Tesla V100 SXM2 + Z8 G4, #RACERRRZ)

Daily ConversationUniversity StudentsWorking Adults

40 Sätze

The Engineering that Runs the Digital World 🛠️⚙️💻 How do CPUs Work?

Daily ConversationUniversity StudentsWorking Adults

345 Sätze

Andrew Ng on Coding Agents and Deciding What to Build

Daily ConversationUniversity Students

182 Sätze

We Let $200 GPT-Pro Think for 7 Days on Unsolved Math

Daily ConversationUniversity StudentsWorking Adults

Wortschatz und Sprechhinweise zu dieser Lektion

Diese Sprechübung basiert auf dem Video „Gemini 4 Argon explained in 5min.“. Diese Wörter kommen am häufigsten vor: Google, model, benchmark, token, Argon. Dieses Video enthält 56 Sätze und 877 Wörter zum Nachsprechen. Der gesprochene Teil dauert 4:56. Der Sprecher spricht in natürlichem Tempo, etwa 178 Wörter pro Minute, ähnlich wie im Alltagsgespräch. Nur 77 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.

Wichtiger Wortschatz in diesem Video

15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:

Wort	Aussprache	Bedeutung
benchmark Substantiv	/ˈbɛn(t)ʃmɑːk/	Maßstab, Höhenfestpunkt
token Substantiv	/ˈtoʊkən/	Zeichen, Symbol
subscription Substantiv	/səbˈskɹɪpʃən/	Abonnement, Bezug
output Substantiv	/ˈaʊtpʊt/	Output
capture Verb	/ˈkæp.(t)ʃɚ/	fangen, einfangen
generate Verb	/ˈd͡ʒɛn.ə.ɹeɪt/	generieren
frontier Substantiv	/ˈfɹʌntɪə/	Grenzland, Grenzgebiet
flagship Substantiv	/ˈflæɡʃɪp/	Flaggschiff
harness Verb	/ˈhɑɹ.nəs/	vorspannen, aufzäumen
solve Verb	/sɒlv/	lösen
extend Verb	/ɛkˈstɛnd/	erweitern
gravity Substantiv	/ˈɡɹævɪti/	Bedenklichkeit, Ernst
adoption Substantiv	/əˈdɑp.ʃən/	Adoption
trajectory Substantiv	/tɹəˈd͡ʒɛktəɹi/	Flugbahn, Bahn
earn Verb	/ɝn/	verdienen

Phrasal Verbs, die du hören wirst

Wort	Bedeutung
figure out Verb	herausfinden
open up Verb	öffnen

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	are flawed · was actually released · can be contaminated
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	It's been · hasn't really solved

Aussprache, auf die du achten solltest

Der Sprecher verwendet 3 Kurzformen und abgeschwächte Formen, zum Beispiel doesn't, hasn't, they're. Sprich sie in der kurzen Form, so wie du sie hörst.

Die Laute „sh“ und „zh“: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
Lange Wörter – auf die Betonung achten: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

Laute, die Deutschsprachigen schwerfallen:

Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: lab /læːb/, solve /sɒlv/, extend /ɛkˈstɛnd/, compound /ˈkɑmpaʊnd/, incentive /ɪnˈsɛntɪv/
/æ/ — offener als „ä“: capture /ˈkæp.(t)ʃɚ/, lab /læːb/, flagship /ˈflæɡʃɪp/, gravity /ˈɡɹævɪti/, flash /flæʃ/
/dʒ/ — stimmhaft, nicht „tsch“: generate /ˈd͡ʒɛn.ə.ɹeɪt/, trajectory /tɹəˈd͡ʒɛktəɹi/, margin /ˈmɑɹ.d͡ʒɪn/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie benchmark, token, subscription.

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →

Shadowing-Übung: Gemini 4 Argon explained in 5min.. - Englisch Sprechen Lernen mit Video

Übungsmodus wählen

App herunterladen

World Models explained in 10min..

Did Google just dethrone OpenAI and Anthropic?

Gemma 4 Just Released - Whats In? In 2026!

LangChain in 5 Minutes (Explained Clearly)

They JUST Accidentally Revealed the Truth: The AI Bubble

Give me 31 minutes, I will make PyTorch click forever

My 4.9-Star iOS App Is Actually a Website: Here’s How I Built It

What's New in Zephyr 4.5?

Go’s Big Mistake

Expensive RTX 5090 for LLMs? NO. Use This Instead. (Tesla V100 SXM2 + Z8 G4, #RACERRRZ)

The Engineering that Runs the Digital World 🛠️⚙️💻 How do CPUs Work?

Andrew Ng on Coding Agents and Deciding What to Build

We Let $200 GPT-Pro Think for 7 Days on Unsolved Math

Wortschatz und Sprechhinweise zu dieser Lektion

Wichtiger Wortschatz in diesem Video

Phrasal Verbs, die du hören wirst

Grammatik in diesem Video

Aussprache, auf die du achten solltest

So übst du mit diesem Video

Was ist die Shadowing-Technik?