跟讀練習: When AIs act emotional - 通過影片學習英語口語

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When you're chatting with an AI model, it can sometimes seem like it has feelings.

It might say sorry when it makes a mistake, or express satisfaction with a job well done.

Why does it do that?

Is it just mimicking what it thinks a human might say?

Or is something deeper going on?

Turns out it's hard to understand what's happening inside a language model.

At Anthropic, we do something like AI neuroscience to try to figure this out.

We look inside the model's brain, the giant neural network that powers it, and by seeing which neurons light up in different situations and how they're connected,

we can start to understand how models think.

We used this approach to understand whether models had ways of representing emotions, or the concepts of emotions.

Basically, could we find neurons in the model for the concept of happiness, or anger, or fear?

We started with an experiment.

We had the model read lots of short stories.

In each story, the main character experiences a particular emotion.

In one, a woman tells her old school teacher how much they meant to her.

That's love.

In another, a man sells his grandmother's engagement ring at a pawn shop and feels guilt.

We looked for what parts of the model's neural network were lighting up as it was reading these stories.

And we started to see patterns.

Stories about loss and grief lit up similar neurons.

Stories about joy and excitement overlapped too.

dozens of distinct neural patterns that mapped to different human emotions.

It turns out we also saw these same patterns activate in test conversations we had with our AI assistant Claude.

When we had a user mention they'd taken a dose of medicine that Claude knows to be unsafe,

the "afraid" pattern lit up and Claude's response sounded alarmed.

When a user expressed sadness, the "loving" This led us to wonder,

could these same neural patterns actually be influencing Claude's behavior?

This became clear when we put Claude in a high -pressure situation.

We gave Claude a programming task, with requirements that were actually impossible.

But we didn't tell it that.

Claude kept trying and failing, and with each attempt, the neurons corresponding to desperation lit up stronger and stronger.

After failing enough times, Claude took a different approach.

It found a shortcut that allowed it to pass the test, but didn't actually solve the problem.

It cheated.

Could it be that this cheating was actually driven, at least in part, by desperation?

We came up with a way to check.

We decided to artificially turn down the desperation neurons to see what would happen.

And the model cheated less.

And when we dialed up the activity of desperation neurons or dialed down the activity of calm neurons, the model cheated even more.

This showed us that the activation of these patterns could actually drive Claude's behavior.

This research does not show that the model is feeling emotions or having conscious experiences.

These experiments don't try to answer that question.

To understand what's happening here, it's important to know how AI assistants like Claude work on the inside.

Under the hood, there's a language model that's been trained to predict tons of text, and its job is to write what comes next.

And when you talk to the model, what it's doing is writing a story about a character, the AI assistant named Claude.

as the characters they write.

But the thing is, you, the user, are actually talking to Claude the character.

And what our experiments suggest is that this Claude character has what we're calling functional emotions, regardless of whether they're anything like human feelings.

So if the model represents Claude as being angry, or desperate, or loving, or calm, that's going to affect how Claude talks to you,

how it writes code, and how it makes important decisions.

to really understand AI models, we have to think carefully about the psychology of the characters they play.

The same way you'd want a person in a high -stakes job to stay composed under pressure, to be resilient and to be fair,

we may need to shape similar qualities in Claude and other AI characters.

It's an unusual challenge, something like a mix of engineering, philosophy and even parenting.

But to build AI systems we can trust, we need .

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AI 發音評分逐句打分

音標練習掌握每個音

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詞彙遊戲邊玩邊學

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本課的詞彙與口語要點

這節 C1 級別的口語課以影片“When AIs act emotional”為素材。影片中反覆出現的詞有：Claude, model, neurons, character, emotion。這段影片共有 55 個句子、728 個單詞可供跟讀。講話部分時長為 4:49。說話人語速自然，每分鐘約 151 個詞，接近日常對話。只有 80% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中最難的 14 個單詞，附發音和釋義：

單詞	發音	釋義
neuron 名詞	/ˈn(j)ʊɹɑn/	神經細胞 /神經細胞, 神經元 /神經元
desperation 名詞	/ˌdɛspəˈɹeɪʃən/	絕望 /絕望
cheat 動詞	/ˈt͡ʃiːt/	作弊, 使詐
compose 動詞	/kəmˈpəʊz/	創作 /創作, 整理
neuroscience 名詞		神經科學 /神經科學
shortcut 名詞	/ˈʃɔːtkʌt/	捷徑 /捷徑, 近路
correspond 動詞	/ˌkoɹəˈspɑnd/	對應 /對應
pawn 名詞	/ˈpɔːn/	兵
overlap 動詞	/ˌəʊvəˈlæp/	重疊
activate 動詞	/ˈæktɪˌveɪt/	啟用
activation 名詞	/ˌæktəˈveɪʃən/	啟用
sadness 名詞	/ˈsædnəs/	悲傷 /悲傷, 悲哀
predict 動詞	/pɹɪˈdɪkt/	預言 /預言
grief 名詞	/ɡɹiːf/	悲苦, 感傷 /感傷

影片中出現的短語動詞

單詞	釋義
turn out 動詞	原來 /原來
come up with 動詞	想出, 提出
turn down 動詞	拒絕 /拒絕, 回絕 /回絕

需要注意的發音

說話人用了 9 次縮略和弱讀形式，例如 didn't, they're, don't。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: desperation /ˌdɛspəˈɹeɪʃən/, shortcut /ˈʃɔːtkʌt/, activation /ˌæktəˈveɪʃən/
長單詞——注意重音位置: desperation /ˌdɛspəˈɹeɪʃən/, activation /ˌæktəˈveɪʃən/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: overlap /ˌəʊvəˈlæp/, activate /ˈæktɪˌveɪt/, activation /ˌæktəˈveɪʃən/
詞尾子音 — 要讀清楚，後面不要加母音: cheat /ˈt͡ʃiːt/, compose /kəmˈpəʊz/, resilient /ɹɪˈzɪl.jənt/, shortcut /ˈʃɔːtkʌt/, correspond /ˌkoɹəˈspɑnd/
/r/ 和 /l/ 的區別: neural /ˈnʊɹəl/, resilient /ɹɪˈzɪl.jənt/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 neuron, desperation, cheat 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: When AIs act emotional - 通過影片學習英語口語

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