跟读练习: Gemini 4 Argon explained in 5min.. - 通过视频学习英语口语

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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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本课的词汇与口语要点

这节口语课以视频“Gemini 4 Argon explained in 5min.”为素材。视频中反复出现的词有：Google, model, benchmark, token, Argon。这段视频共有 56 个句子、877 个单词可供跟读。讲话部分时长为 4:56。说话人语速自然，每分钟约 178 个词，接近日常对话。只有 77% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
token 名词	/ˈtoʊkən/	代幣 /代币, 籌碼 /筹码
subscription 名词	/səbˈskɹɪpʃən/	訂閱 /订阅, 預訂 /预订
output 名词	/ˈaʊtpʊt/	輸出 /输出
capture 动词	/ˈkæp.(t)ʃɚ/	捕獲 /捕获, 佔領 /占领
frontier 名词	/ˈfɹʌntɪə/	邊界 /边界, 邊疆 /边疆
flagship 名词	/ˈflæɡʃɪp/	旗艦 /旗舰
solve 动词	/sɒlv/	解決 /解决
adoption 名词	/əˈdɑp.ʃən/	收養 /收养, 領養 /领养
trajectory 名词	/tɹəˈd͡ʒɛktəɹi/	軌道 /轨道, 彈道 /弹道
earn 动词	/ɝn/	掙 /挣, 賺 /赚
temporary 形容词	/ˈtɛmp(əˌɹ)ɛɹi/	暫時 /暂时
compare 动词	/kəmˈpɛɚ/	比, 比較 /比较
flash 动词	/flæʃ/	閃光 /闪光
compete 动词	/kəmˈpiːt/	競爭 /竞争, 高下
angle 名词	/ˈæŋ.ɡəl/	角, 角度

视频中出现的短语动词

单词	释义
figure out 动词	弄清楚

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	are flawed · was actually released · can be contaminated
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	It's been · hasn't really solved

需要注意的发音

说话人用了 3 次缩略和弱读形式，例如 doesn't, hasn't, they're。请按听到的简短形式来说。

“sh” 和 “zh” 音: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
长单词——注意重音位置: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: solve /sɒlv/, gravity /ˈɡɹævɪti/, developer /dɪˈvɛləpɚ/, incentive /ɪnˈsɛntɪv/, perceive /pɚˈsiv/
词尾辅音 — 要读清楚，后面不要加元音: benchmark /ˈbɛn(t)ʃmɑːk/, output /ˈaʊtpʊt/, lab /læːb/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, flagship /ˈflæɡʃɪp/
/r/ 和 /l/ 的区别: primarily /ˈpɹaɪ.mə.ɹə.li/, regardless /ɹɪˈɡɑɹd.lɪs/, reliable /ɹɪˈlaɪəbl̩/, ultra /ˈʌltɹə/, preliminary /pɹɪˈlɪmɪnɛɹi/

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
录下自己的声音并与原声对比，特别注意 token, subscription, output 这类单词。

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: Gemini 4 Argon explained in 5min.. - 通过视频学习英语口语

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