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.
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Even during Google I .O back in May, I expected Google to drop Gemini 3 .5 Pro.
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But Google has been releasing a lot of flash models instead,
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and a lot of them basically struggle to get market adoption given a variety of other flagship models
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and cheaper alternatives from Chinese open models.
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And Google finally made a flagship model with the codename Argon, and the preliminary benchmarks show a pretty strong number as you can see.
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The first thing you might have noticed here is coding benchmarks, more specifically, scoring 77 .9 % on DeepSuite,
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which would put Gemini 4 Argon above every other model we currently have.
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But it's always good to have some healthy skepticism here.
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Recently, EPOC found issues on DeepSuite Benchmark, finding 23 tasks that are flawed.
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And looking back to when the benchmark was actually released back in May, the entire 113 tasks and their solutions are made public,
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which means labs that have access to these could easily study them during post -training and benchmarks their models around public benchmarks.
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And when we look at other benchmarks that Google reported here, a large majority of them are public.
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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.
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And of course, as users, all of this makes it really difficult to track real progress in AI since
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so many public benchmarks like DeepSuite can be contaminated pretty fast.
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Not only that, benchmarks get saturated a lot faster now than before, which forces benchmarks to be refreshed more frequently.
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So consumers now carry a huge burden keeping track of real scientific progress in AI.
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But regardless of the case, achieving this kind of leap in benchmark is a huge improvement from Google.
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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,
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codecs, or open harnesses when it comes to coding.
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And despite being a strong model, Google still hasn't really solved making their harness anti -gravity a truly competitive option for developers.
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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.
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And on face value, Gemini 4 Argon currently undercuts both Anthropic
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and OpenAI when it comes to the cost of intelligence until the temporary discount ends and the price doubles again.
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But the cost benefit that we find here doesn't actually translate in Pareto Frontier, as you can see.
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Argon falls below the Pareto Frontier, showing you that Anthropic and OpenAI still leads ahead in both the harness and model adoption.
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Now, one angle we should think about, though, is the upcoming IPO for Anthropic and possibly OpenAI.
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While OpenAI and Anthropic have a huge incentive to subsidize the model through subscription pretty heavily to grow their users,
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Google already sits on top of one.
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And given that Google is already a publicly traded company,
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they want to be more careful about how to allocate their existing compute towards actually creating value within their ecosystem,
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which could help increase their perceived revenue.
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Even looking at how Argon is being rolled out in segments, the model is set to be made available to ultra -subscription users
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and paid API users where there's already a huge revenue being
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captured instead of fighting the subscription rates in channeling their compute towards that.
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On a recent Google's Q2 earning call, they said that their API is processing 22 billion tokens per minute,
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which is 6 billion more than the previous quarter.
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That's more than 11 quadrillion tokens per year in just API channel alone.
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And when you start factoring in other channels that Google owns in its distribution,
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we really get a closer picture into what Google really means when they said that they're supply constrained.
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And eventually, OpenAI and Anthropic will have to solve this scaling problem while maintaining healthy margins at the same time,
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not just subsidizing subscription to capture more users.
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So looking at Gemini for Argon, Google is demonstrating their capability in keeping pace at the frontier
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while they also try to figure out how to capture value at scale.
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One interesting note on Argon is how Google extended their output tokens from 64 ,000 to 1 million tokens.
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This might not sound like a big deal, but it's definitely a huge move from Google
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because maintaining coherence as autoregressive models generate longer and longer tokens get increasingly difficult the longer the output tokens get.
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Scientists like Yen Le Koon made criticism on autoregressive models for this very reason,
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where autoregressively generating one token after another can compound errors over long trajectory, let alone 1 million from Argon.
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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.
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Now, of course, that's just in theory, assuming that every token error is independent.
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But the fact
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that Google has enough confidence to extend their commercial product to
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1 million output tokens really opens up door for longer trajectory tasks like code migration
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and long -running research tasks that could benefit from a model that can generate more.
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Bu dersin kelimeleri ve konuşma notları
Bu konuşma dersi “Gemini 4 Argon explained in 5min.” videosuna dayanıyor. En çok tekrarlanan kelimeler: Google, model, benchmark, token, Argon. Bu videoda gölgeleme çalışması için 56 cümle ve 877 kelime var. Konuşma bölümü 4:56 sürüyor. Konuşmacı dakikada yaklaşık 178 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin yalnızca %77’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığı zorlayıcı.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| benchmark isim | /ˈbɛn(t)ʃmɑːk/ | mihenk taşı |
| token isim | /ˈtoʊkən/ | jeton |
| subscription isim | /səbˈskɹɪpʃən/ | abonelik |
| output isim | /ˈaʊtpʊt/ | çıktı |
| capture fiil | /ˈkæp.(t)ʃɚ/ | ele geçirmek |
| flagship isim | /ˈflæɡʃɪp/ | amiral gemisi, bayrak gemisi |
| harness fiil | /ˈhɑɹ.nəs/ | koşmak, koşum takmak |
| solve fiil | /sɒlv/ | çözmek, halletmek |
| extend fiil | /ɛkˈstɛnd/ | genişletmek |
| gravity isim | /ˈɡɹævɪti/ | ağırbaşlılık, ciddiyet |
| adoption isim | /əˈdɑp.ʃən/ | evlat edinme |
| trajectory isim | /tɹəˈd͡ʒɛktəɹi/ | yörünge |
| earn fiil | /ɝn/ | hak etmek |
| temporary sıfat | /ˈtɛmp(əˌɹ)ɛɹi/ | geçici |
| compare fiil | /kəmˈpɛɚ/ | karşılaştırmak, kıyaslamak |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| figure out fiil | açığa çıkarmak |
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | are flawed · was actually released · can be contaminated |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | It's been · hasn't really solved |
Dikkat edilecek telaffuzlar
Konuşmacı doesn't, hasn't, they're gibi kısaltılmış biçimleri 3 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/
Türkçe konuşanların zorlandığı sesler:
- Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: frontier /ˈfɹʌntɪə/, flagship /ˈflæɡʃɪp/, gravity /ˈɡɹævɪti/, trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/
- /æ/ — “e”den daha açık: capture /ˈkæp.(t)ʃɚ/, lab /læːb/, flagship /ˈflæɡʃɪp/, gravity /ˈɡɹævɪti/, flash /flæʃ/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; benchmark, token, subscription gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.