シャドーイング練習: AI for Everyone by Andrew Ng - 動画で英語スピーキングを学ぶ

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Welcome to AI for Everyone.

AI is changing the way we work and live.

This non-technical course will teach you how to navigate the rise of AI.

Whether you want to know what's behind the buzzwords, or whether you want to perhaps use AI yourself,

either in the personal context or in a corporation or other organization, this course will teach you how.

And if you want to understand how AI is affecting society and how you can navigate that, you also learn that from this course.

In this first week, we'll start by cutting through the hype and giving you a realistic view of what AI really is.

Let's get started.

You've probably seen news articles about how much value AI is creating.

According to a study by McKinsey Global Institute,

AI is estimated to create an additional 13 trillion US dollars of value annually by the year 2030.

Even though AI is already creating tremendous amounts of value in the software industry,

a lot of the value to be created in the future lies outside the software industry in sectors such as retail,

travel, transportation, automotive, materials, manufacturing, and so on.

I have a hard time thinking of an industry

that I don't think AI will have a huge impact on in the next several years.

My friends

and I used to challenge each other to name an industry where we don't think AI will have a huge impact.

And my best example was the hairdressing industry because you know how to use AI robotics to automate hairdressing.

But I once said this on stage

and one of my friends who is a robotics professor was in the audience

that day that day and she actually stood up

and she looked at me in the eye

and she said you know Andrew most people's hairstyles I couldn't get a robot to cut their hair that way.

But she looked at me and said, your hairstyle, Andrew, that a robot can do.

There is a lot of excitement, but also a lot of unnecessary hype about AI.

One of the reasons for this is because AI is actually two separate ideas.

Almost all the progress we are seeing in AI today is artificial narrow intelligence.

These are AIs that do one thing, such as a smart speaker or self-driving car

or AI to do web search or AI applications in farming or in a factory.

These types of AI are one-trick ponies, but when you find the appropriate trick, this can be incredibly valuable.

Unfortunately, AI also refers to a second concept of AGI or artificial general intelligence.

That is the goal to build AI that can do anything a human can do, or maybe even be super intelligent and do even more things than any human can.

I'm seeing tons of progress in ANI, artificial narrow intelligence, and almost no progress toward AGI, or artificial general intelligence.

Both of these are worthy goals, and unfortunately, the rapid progress in ANI, which is incredibly valuable.

That has caused people to conclude that there's a lot of progress in AI, which is true, but that has caused people to falsely think

that there might be a lot of progress in AGI as well,

which is leading to some irrational fears about evil color robots coming over to take over humanity anytime now.

I think AGI is an exciting goal for researchers to work on, but it'll take multiple technological breakthroughs before we get there.

And it may be decades or hundreds of years or even even thousands of years away.

Given how far away AGI is, I think there is no need to unduly worry about it.

In this week, you will learn what ANI can do and how to apply them to your problems.

Later in this course, you also see some case studies of how ANI,

these one-trick ponies, can be used to build really valuable applications such as smart speakers and self-driving cars.

In this week, you will learn what is AI.

You may have heard of machine learning and the next video will teach you what is machine learning.

You also learn what is data and what types of data are valuable, but also what types of data are not valuable.

You learn what it is that makes a company an AI company or an AI first company,

so that perhaps you can start thinking if there are ways to improve your company or other organization's ability to use AI.

And importantly, you also learned this week what machine learning can and cannot do.

In our society, newspapers as well as research papers tend to talk only about the success stories of machine learning and AI.

And we hardly ever see any failure stories because they just aren't as interesting to report on.

But for you to have a realistic view of what AI and what machine learning can and cannot do, I think it's important that you see examples of both

so they can make more accurate judgments about what you may and maybe should not try to use these technologies for.

Finally, a lot of the recent rise of machine learning has been driven through the rise of deep learning, sometimes also called neural networks.

In the final two optional videos of this week,

you can also see an intuitive explanation of deep learning so that you will better understand what they can do,

particularly for a set of narrow ANI tasks.

So that's what you learned this week.

And by the end of this week, you have a sense of AI technologies and what they can and cannot do.

In the second week, you learn how these AI technologies can be used to build valuable projects.

You learn what it feels like to build an AI project

as well as what you should do to make sure you select projects that are technically feasible

as well as valuable to you or your business or other organization.

After learning what it takes to build AI projects, in the third week, you learn how to build AI in your company.

In particular, if you want to take a few steps toward making your company good at AI,

you see the AI transformation playbook and learn how to build AI teams and also build complex AI products.

Finally, AI is having a huge impact on society.

In the fourth and final week, you learn about how AI systems can be biased and how to diminish or eliminate such biases.

You also learn how AI is affecting developing economies and how AI is affecting jobs,

and be better able to navigate this rise of AI for yourself and for your organization.

By the end of this four-week course, you'll be more knowledgeable

and better qualified than even the CEOs of most large companies in terms of your understanding of AI technology,

as well as your ability to help yourself or help your company or other organization navigate the rise of AI.

And so I hope that after this course, you'll be in a position to provide leadership to others as well as they navigate these issues.

Now, one of the major technologies driving the recent rise of AI is machine learning.

But what is machine learning?

Let's take a look in the next video.

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このレッスンの語彙とスピーキングのポイント

この動画には、シャドーイング用の文が78文、単語が1231語あります。音声の長さは7:35です。話す速さは1分あたり約162語で、日常会話に近い自然なペースです。単語の85%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくるやや難しい単語15語を、発音と意味つきで紹介します。

artificial /ˌɑː.tɪˈfɪʃ.əl/ (形容詞) — 人工の. Man-made; made by humans; of artifice.
trick /ˈtɹɪk/ (名詞) — 欺き, 企み. Something designed to fool, dupe, outsmart, mislead or swindle.
narrow /ˈnæɹəʊ/ (形容詞) — 狭い. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
robot /ˈɹoʊ.bɑt/ (名詞) — ロボット, 人造人間. An intelligent mechanical being designed to look like a human or other creature, and usually made from metal.
bias /ˈbaɪ.əs/ (名詞) — 偏り, 傾斜. Inclination towards something.
realistic /ˌɹɪəˈlɪs.tɪk/ (形容詞) — 現実的. Expressed or represented as being accurate, practicable, or not idealistic.
pony /ˈpoʊni/ (名詞) — 子馬, 仔馬. A small horse; specifically, any of several small breeds of horse under 14.2 hands at the withers.
robotics /ɹoʊˈbɑ.tɪks/ (名詞) — ロボット工学, ロボティックス. The science and technology of robots, their design, manufacture, and application
refer /ɹɪˈfɜː/ (動詞) — 問い合わせる. To direct the attention of (someone toward something).
select /sɪˈlɛkt/ (動詞) — 選ぶ. To choose one or more elements of a set, especially a set of options.
explanation /ˌɛkspləˈneɪʃən/ (名詞) — 説明. The act or process of explaining.
rapid /ˈɹæp.ɪd/ (形容詞) — 速い, 急な. Very swift or quick.
humanity /hjuˈmænɪti/ (名詞) — 人類. Humankind; human beings as a group.
estimate /ˈɛs.tɪ.mət/ (名詞) — 推定. A rough calculation or assessment of the value, size, or cost of something.
intelligent /ɪnˈtɛlɪd͡ʒənt/ (形容詞) — 聡明な, 賢い. Of high or especially quick cognitive capacity, bright.

動画に出てくる句動詞

stand up (動詞) — 立ち上がる. To rise from a lying or sitting position.
take over (動詞) — 引き受ける. To assume control of something, such as a business or enterprise, and sometimes by force.

注意したい発音

話し手はdon't, you'll, aren'tなど、短縮形や弱形を10回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: worthy /ˈwɝ.ði/, breakthrough /ˈbɹeɪk.θɹuː/
「sh」と「zh」の音: artificial /ˌɑː.tɪˈfɪʃ.əl/, explanation /ˌɛkspləˈneɪʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, optional /ˈɑp.ʃə.nəl/, irrational /ɪˈɹæʃ.(ə.)nəl/
長い単語（アクセントの位置に注意）: artificial /ˌɑː.tɪˈfɪʃ.əl/, incredibly /ɪŋˈkɹɛdɪbli/, explanation /ˌɛkspləˈneɪʃən/, humanity /hjuˈmænɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。artificial, trick, narrowなどの単語に特に注意しましょう。

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is estimated · be created · has been driven
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	You've probably seen · has caused · has been driven
関係詞節 who / which + 節 — 人や物について情報を加える	friends who is · ANI, which is

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: AI for Everyone by Andrew Ng - 動画で英語スピーキングを学ぶ

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