Shadowing Practice: Gemini 4 Argon explained in 5min.. - Learn English Speaking with Video

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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.

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Woordenschat en spreektips bij deze les

Deze spreekles is gebaseerd op de video “Gemini 4 Argon explained in 5min.”. Deze woorden komen het vaakst terug: Google, model, benchmark, token, Argon. Deze video bevat 56 zinnen en 877 woorden om na te spreken. Het gesproken deel duurt 4:56. De spreker praat in een natuurlijk tempo van ongeveer 178 woorden per minuut, dicht bij een alledaags gesprek. Slechts 77% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is pittig.

Belangrijke woorden in deze video

15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:

Woord	Uitspraak	Betekenis
benchmark zelfstandig naamwoord	/ˈbɛn(t)ʃmɑːk/	ijkpunt, maatstaf
token zelfstandig naamwoord	/ˈtoʊkən/	teken
subscription zelfstandig naamwoord	/səbˈskɹɪpʃən/	abonnement
output zelfstandig naamwoord	/ˈaʊtpʊt/	productie
frontier zelfstandig naamwoord	/ˈfɹʌntɪə/	grens, landgrens
flagship zelfstandig naamwoord	/ˈflæɡʃɪp/	vlaggenschip
harness werkwoord	/ˈhɑɹ.nəs/	inspannen, optuigen
solve werkwoord	/sɒlv/	oplossen
extend werkwoord	/ɛkˈstɛnd/	uitbreiden
adoption zelfstandig naamwoord	/əˈdɑp.ʃən/	adoptie
trajectory zelfstandig naamwoord	/tɹəˈd͡ʒɛktəɹi/	baan, traject
earn werkwoord	/ɝn/	verdienen
regardless bijwoord	/ɹɪˈɡɑɹd.lɪs/	desondanks, niettemin
temporary bijvoeglijk naamwoord	/ˈtɛmp(əˌɹ)ɛɹi/	tijdelijk
compare werkwoord	/kəmˈpɛɚ/	vergelijken

Phrasal verbs die je zult horen

Woord	Betekenis
figure out werkwoord	uitvogelen, er achter komen

Grammatica in deze video

De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:

Structuur	In de video
Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet	are flawed · was actually released · can be contaminated
Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt	It's been · hasn't really solved

Uitspraak om op te letten

De spreker gebruikt 3 samentrekkingen en verkorte vormen, zoals doesn't, hasn't, they're. Spreek ze kort uit, zoals je ze hoort.

De klanken “sh” en “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
Lange woorden — let op de klemtoon: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

Klanken die Nederlandstaligen lastig vinden:

Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: lab /læːb/, solve /sɒlv/, extend /ɛkˈstɛnd/, compound /ˈkɑmpaʊnd/, incentive /ɪnˈsɛntɪv/
/æ/ — opener dan de Nederlandse “e”: capture /ˈkæp.(t)ʃɚ/, lab /læːb/, flagship /ˈflæɡʃɪp/, gravity /ˈɡɹævɪti/, flash /flæʃ/
/g/ — een harde plofklank, geen Nederlandse “g”: flagship /ˈflæɡʃɪp/, gravity /ˈɡɹævɪti/, regardless /ɹɪˈɡɑɹd.lɪs/, angle /ˈæŋ.ɡəl/, segment /ˈsɛɡmɛnt/

Zo oefen je met deze video

Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als benchmark, token, subscription.

Wat is de Shadowing-techniek?

Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.

Shadowing-techniek: lees de volledige stap-voor-stap-gids →

Shadowing Practice: Gemini 4 Argon explained in 5min.. - Learn English Speaking with Video

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