ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: AI for Everyone by Andrew Ng

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Welcome to AI for Everyone.
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AI is changing the way we work and live.
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This non-technical course will teach you how to navigate the rise of AI.
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Whether you want to know what's behind the buzzwords, or whether you want to perhaps use AI yourself,
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either in the personal context or in a corporation or other organization, this course will teach you how.
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And if you want to understand how AI is affecting society and how you can navigate that, you also learn that from this course.
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In this first week, we'll start by cutting through the hype and giving you a realistic view of what AI really is.
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Let's get started.
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You've probably seen news articles about how much value AI is creating.
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According to a study by McKinsey Global Institute,
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AI is estimated to create an additional 13 trillion US dollars of value annually by the year 2030.
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Even though AI is already creating tremendous amounts of value in the software industry,
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a lot of the value to be created in the future lies outside the software industry in sectors such as retail,
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travel, transportation, automotive, materials, manufacturing, and so on.
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I have a hard time thinking of an industry
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that I don't think AI will have a huge impact on in the next several years.
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My friends
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and I used to challenge each other to name an industry where we don't think AI will have a huge impact.
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And my best example was the hairdressing industry because you know how to use AI robotics to automate hairdressing.
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But I once said this on stage
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and one of my friends who is a robotics professor was in the audience
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that day that day and she actually stood up
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and she looked at me in the eye
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and she said you know Andrew most people's hairstyles I couldn't get a robot to cut their hair that way.
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But she looked at me and said, your hairstyle, Andrew, that a robot can do.
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There is a lot of excitement, but also a lot of unnecessary hype about AI.
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One of the reasons for this is because AI is actually two separate ideas.
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Almost all the progress we are seeing in AI today is artificial narrow intelligence.
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These are AIs that do one thing, such as a smart speaker or self-driving car
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or AI to do web search or AI applications in farming or in a factory.
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These types of AI are one-trick ponies, but when you find the appropriate trick, this can be incredibly valuable.
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Unfortunately, AI also refers to a second concept of AGI or artificial general intelligence.
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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.
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I'm seeing tons of progress in ANI, artificial narrow intelligence, and almost no progress toward AGI, or artificial general intelligence.
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Both of these are worthy goals, and unfortunately, the rapid progress in ANI, which is incredibly valuable.
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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
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that there might be a lot of progress in AGI as well,
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which is leading to some irrational fears about evil color robots coming over to take over humanity anytime now.
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I think AGI is an exciting goal for researchers to work on, but it'll take multiple technological breakthroughs before we get there.
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And it may be decades or hundreds of years or even even thousands of years away.
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Given how far away AGI is, I think there is no need to unduly worry about it.
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In this week, you will learn what ANI can do and how to apply them to your problems.
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Later in this course, you also see some case studies of how ANI,
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these one-trick ponies, can be used to build really valuable applications such as smart speakers and self-driving cars.
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In this week, you will learn what is AI.
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You may have heard of machine learning and the next video will teach you what is machine learning.
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You also learn what is data and what types of data are valuable, but also what types of data are not valuable.
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You learn what it is that makes a company an AI company or an AI first company,
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so that perhaps you can start thinking if there are ways to improve your company or other organization's ability to use AI.
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And importantly, you also learned this week what machine learning can and cannot do.
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In our society, newspapers as well as research papers tend to talk only about the success stories of machine learning and AI.
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And we hardly ever see any failure stories because they just aren't as interesting to report on.
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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
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so they can make more accurate judgments about what you may and maybe should not try to use these technologies for.
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Finally, a lot of the recent rise of machine learning has been driven through the rise of deep learning, sometimes also called neural networks.
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In the final two optional videos of this week,
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you can also see an intuitive explanation of deep learning so that you will better understand what they can do,
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particularly for a set of narrow ANI tasks.
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So that's what you learned this week.
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And by the end of this week, you have a sense of AI technologies and what they can and cannot do.
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In the second week, you learn how these AI technologies can be used to build valuable projects.
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You learn what it feels like to build an AI project
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as well as what you should do to make sure you select projects that are technically feasible
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as well as valuable to you or your business or other organization.
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After learning what it takes to build AI projects, in the third week, you learn how to build AI in your company.
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In particular, if you want to take a few steps toward making your company good at AI,
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you see the AI transformation playbook and learn how to build AI teams and also build complex AI products.
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Finally, AI is having a huge impact on society.
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In the fourth and final week, you learn about how AI systems can be biased and how to diminish or eliminate such biases.
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You also learn how AI is affecting developing economies and how AI is affecting jobs,
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and be better able to navigate this rise of AI for yourself and for your organization.
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By the end of this four-week course, you'll be more knowledgeable
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and better qualified than even the CEOs of most large companies in terms of your understanding of AI technology,
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as well as your ability to help yourself or help your company or other organization navigate the rise of AI.
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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.
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Now, one of the major technologies driving the recent rise of AI is machine learning.
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But what is machine learning?
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Let's take a look in the next video.

ดาวน์โหลดแอป

ทุกอย่างที่ต้องใช้เพื่อพูดได้คล่อง

AI ให้คะแนนการออกเสียงให้คะแนนทุกประโยค
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คำศัพท์สร้างคลังคำของคุณ
เกมคำศัพท์เล่นไปเรียนไป

คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

วิดีโอนี้มี 78 ประโยค และ 1231 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 7:35 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 162 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 85% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก

คำศัพท์สำคัญในวิดีโอนี้

คำที่พบไม่บ่อย 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:

  • artificial /ˌɑː.tɪˈfɪʃ.əl/ (คำคุณศัพท์) — เทียม. Man-made; made by humans; of artifice.
  • narrow /ˈnæɹəʊ/ (คำคุณศัพท์) — แคบ. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
  • robot /ˈɹoʊ.bɑt/ (คำนาม) — หุ่นยนต์, โรบอต. An intelligent mechanical being designed to look like a human or other creature, and usually made from metal.
  • bias /ˈbaɪ.əs/ (คำนาม) — อคติ, ความลำเอียง. Inclination towards something.
  • realistic /ˌɹɪəˈlɪs.tɪk/ (คำคุณศัพท์) — สมจริง. Expressed or represented as being accurate, practicable, or not idealistic.
  • robotics /ɹoʊˈbɑ.tɪks/ (คำนาม) — วิทยาการหุ่นยนต์. The science and technology of robots, their design, manufacture, and application
  • select /sɪˈlɛkt/ (คำกริยา) — คัดเลือก. To choose one or more elements of a set, especially a set of options.
  • explanation /ˌɛkspləˈneɪʃən/ (คำนาม) — การอธิบาย. The act or process of explaining.
  • humanity /hjuˈmænɪti/ (คำนาม) — มนุษยชาติ. Humankind; human beings as a group.
  • intelligent /ɪnˈtɛlɪd͡ʒənt/ (คำคุณศัพท์) — ฉลาด. Of high or especially quick cognitive capacity, bright.
  • ton /tʌn/ (คำนาม) — ตัน, โตน. The short ton of 2000 pounds (about 907 kg), 20 hundredweights of 100 pounds avoirdupois each.
  • excitement /ɪkˈsaɪtmənt/ (คำนาม) — ความตื่นเต้น. The state of being excited (emotionally aroused).
  • technically /ˈtɛk.nɪˌkəl.i/ (คำกริยาวิเศษณ์) — วิชาการ. Based on precise facts, which, however, may be contrary to common belief or casual terminology.
  • researcher /ˈɹiˌsɝ.t͡ʃɚ/ (คำนาม) — นักวิจัย. One who researches.
  • conclude /kənˈkluːd/ (คำกริยา) — ปิดท้าย. To end; to come to an end.

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 10 ครั้ง เช่น don't, you'll, aren't ให้พูดแบบสั้นตามที่ได้ยิน

  • เสียง “th”: worthy /ˈwɝ.ði/, breakthrough /ˈbɹeɪk.θɹuː/
  • เสียง “sh” และ “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, explanation /ˌɛkspləˈneɪʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, optional /ˈɑp.ʃə.nəl/, irrational /ɪˈɹæʃ.(ə.)nəl/
  • คำยาว — ลงเสียงหนักให้ถูกพยางค์: artificial /ˌɑː.tɪˈfɪʃ.əl/, incredibly /ɪŋˈkɹɛdɪbli/, explanation /ˌɛkspləˈneɪʃən/, humanity /hjuˈmænɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/

วิธีฝึกกับวิดีโอนี้

  1. ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
  2. เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
  3. อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง artificial, narrow, robot เป็นพิเศษ

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้างในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำis estimated · be created · has been driven
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบันYou've probably seen · has caused · has been driven
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของfriends who is · ANI, which is

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →