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REVIEW IELTS SPEAKING (THI THẬT)

Mình tổng hợp review của các bạn thi IELTS Speaking cuối tháng 03, đầu tháng 4. Các bạn sắp đi thi thì lướt qua chút nha!
Click vào đây để download bộ đề thi Speaking theo quý: https://m.me/286585161523028?ref=Support1BodeSpeaking
Click vào đây để đặt sách “Câu hỏi & Bài mẫu IELTS Speaking part 123 theo chủ đề”: https://ielts-thanhloan.com/san-pham/ebook-luyen-ielts-speaking
———
(Speaking IDP Triệu Việt Vương 28/3 - Bạn Ngọc Ánh)
Part 1:
- Hỏi về nơi ở ( sống ở nhà hay căn hộ)
+ miêu tả về nhà/ căn hộ đó
+Window view nx
- Hỏi về New Year
+ b thường ở cùng ai/ ở đâu/ làm gì vào năm mới
+ ng VN thường ăn gì vào năm mới
Part 2: Miêu tả về 1 người/ ca sĩ nổi tiếng mà b thích
Part 3: hầu hết chủ đề về celebrity
- Ngoài ca sĩ ra thì còn có những ai liên quan đến celebrity nx
- Những người nổi tiếng họ có khả năng tiếp tục nổi tiếng trong tương lai k
- Có rất nhiều ng nổi tiếng phàn nàn rằng họ hay bị làm phiền, bị chụp trộm oét những nơi công cộng. Theo b họ nên lên tiếng hay chấp nhận im lặng vấn đề này?
- Hiện nay có rất nhiều người nổi tiếng vì họ có tài năng. Nma cx có nhiều ng họ nổi tiếng nma k p do tài năng thực sự của họ. B nghĩ sao về vấn đề này?
- Bên cạnh đó, cx có nhiều người dễ nổi tiếng như làm makeup hoặc bán qa... B nghĩ xao về vấn đề này
————
(Speaking IDP Hải Phòng 27/3 - Bạn Hoàng Chí Đức)
_ Speaking: part 1: mỗi topic ông í hỏi khoảng 2 đến 3 câu
+ Study/Subjects
+ Museum
+ Mornings/Get up early
+ Weekends
- Part 2: skills you can teach other people.
Thực ra đề này em học rồi, nói rồi nên brainstorm được idea trong đầu nhưng em bị hết ý hơi sớm í :)))) hicc mong k bị trừ nhiều huhu
Part3: ôi chắc phải hỏi khoảng 8 9 câu í ạ:)) em nhớ được đúng chính xác 5 câu còn lại là ông ý hỏi thêm
+ What skills are important for jobs sector in your country?
+ What skills are valued most in your country?
+ Which age group is the best age for learning?
+ Which do you think are more important practical skills or academic skills?
+ What skills should teacher have? em có trả lời một số skill nhg ô vẫn hỏi thêm skill nào nữa :))
——
(Speaking BC 187 Nguyễn Lương Bằng 29/3 - Bạn Nam Anh)
PART 1
- Work or Student ?
- What subjects do you study ?
- Is it easy to study those subjects ?
- What do you want to do in the future ?
- Do you want to do what you haven’t done yet?
- Do you make a list before going shopping? Why?
- Why is it important to make a shopping list ?
- Do you make a list ?
- Why don’t some people like making a lists ?
- Do you prefer using a piece of paper or making a list on your phone ?
( Part 1 mình cảm thấy sao mà thầy hỏi lắm vậy, thường mình nghĩ chỉ 4-5 câu thôi ấy, lịch nói của mình vào 16:50 nhưng mình thi sớm hơn so với lịch tầm 30 phút nên cũng chưa chuẩn bị tốt lắm, chị staff hỏi sẵn sàng thi chưa em thì mình trả lời luôn là sẵn sàng rồi ạ, thế là vào thi luôn cho nhanh kiểu nghĩ đằng nào cũng thi thì thôi nhanh còn về, muốn đến đâu thì đến)
PART 2 : Describe the first time when you used a foreign language to communicate .
(Đề này mình cũng thấy có trong bộ dự đoán rồi, nhưng khổ nỗi chưa làm qua tại nó mãi cuối list của bộ đề ấy, toàn làm các đề kia thôi, bạn thi sau mình lại gặp nay đề hay được thi nhiều là “a time when it is important to tell your friend a truth”, mình nói chưa hết 2 phút, không hiểu sao ở nhà nói dài lắm, chỉ sợ đi thi quá 2 phút mà chưa nói hết thôi nhưng nay đi thi lại khác, rồi thầy hỏi tiếp thêm 1 câu cho hết giờ đó là :
- Do people need to learn foreign language ? Why ?
PART 3 : Phần này thầy xoay quanh về part 2 và mặt ngôn ngữ, thầy cứ vậy là hỏi thôi tùy vào câu trả lời trước, mình trả lời kiểu toàn lệch hướng ấy, mình kiểu hay lấy ví dụ từ bản thân ấy nên thầy cứ nhắc mãi suốt là not personally mà là all people, anyway được cái thầy cũng nice rồi chỉ mình ! Mình chỉ nhớ loáng thoáng được 1 vài câu thầy hỏi là :
- Do you think that all children should learn foreign languages at school ?
- What is the best age for a child to learn a foreign language ?
- Câu cuối mình nhớ mang máng thầy hỏi học ngôn ngữ thì Speaking hay Writing khó hơn ?
——
Speaking BC Computer 27/3 - Bạn Dương Tuấn Đạt
Speaking: Mình thi nói ca đầu tiên, examiner rất thân thiện ; không ngắt mình tí nào, tạo tâm lý thoải mái cho thí sinh . À, nhớ chào hỏi cảm ơn mấy thầy nhiệt tình vào nhé:))
+ Part 1: Place where you live; Change; Forget. Phần này mình nói trôi.
+ Part 2: An occasion when you forgot something important. Mình nói phần này hơi cuống và bị lặp idea.
+ Part 3:
Do you forget things often? Why?/Why not?
How can we improve our memory?
Why do people often forget small things?
... mấy câu nữa liên quan đến Topic Quên, mình cũng Quên luôn rồi:))
——
Speaking IDP Triệu Việt Vương 29/3 - Bạn Nguyen QTrang
PART 1:
1. Work/study?
2. Free time? Weekend?
3. Cinema?
4. Sport?
PART 2: An ambition you have in a very long time
PART 3:
1. Young people có ambition giống m không? Why? - m trl là thích làm teacher với doctor hơn
2. Why young ppl muốn thành teacher và doctor?
3. Why young ppl muốn có vị trí cao hơn trong công vc? - m trl là vì muốn kiếm nhiều tiền hơn và có nhiều sự kính trọng hơn
4. M nghĩ có công việc gì mà cần higher position nhất? Why?
5. Ppl còn có ambition gì về science không? Why?
6. Ngoài ambition trong career thì ppl muốn gì trong cuộc sống? Why?
——
Speaking IDP Hải Phòng 27/3 - Bạn Long Tran
PART 1:
(1) Place:
- Bạn đang sống ở đâu?
- Bạn có thích nơi bạn ở không?
- Nơi bạn ở có hợp với gia đình có trẻ nhỏ không?
- Nơi bạn ở có cơ sở vật chất nào cho trẻ em không?
(2) New activities:
- Bạn có thích thử những new activities không?
- Bạn muốn thử new activities nào trong tương lai?
- Lúc còn nhỏ, bạn đã thử new activities nào?
- Bạn thích thử new activities alone hay với người khác?
(3) Changes:
- Bạn có thay đổi nhiều từ lúc bé đến giờ không?
- Bạn có thích thay đổi không?
- Có sự thay đổi nào ở nơi bạn sống không?
PART 2: Describe a time when you had to learn the words of something (poem, song) and then say or sing it from memory
You shoud say:
Where you were?
Who was listening to you?
How you felt about it?
Câu hỏi phụ: Người ta thường làm gì để ghi nhớ words?
PART 3:
(1) Bạn có giỏi ghi nhớ không?
(2) Người già hay người trẻ có thể dễ dàng nhớ song/ poem hơn?
(3) Trẻ con thích và thấy những bài hát trẻ em fun và dễ nhớ vì sao?
(4) Từ poem/ song người ta có thể học được gì?
(5) Người ta nên tìm hiểu về những cái facts của thế giới, bạn có đồng tình với ý kiến này không?
(6) Trong quá khứ người già hay thuộc những bài thơ LONG, LONG, LONG, and super LONG, how and why?
——
Speaking Triệu Việt Vương 26/3 - Bạn San San
P1: work or study/ change/ handwriting.
- Hs hay đi làm, muốn làm gì trong tương lai
- Thay đổi của bản thân trong những năm gần đây
- Dùng handwriting or computer
- Computer có thay thế handwriting k
(Mấy câu nữa trong dự đoán có hết mà t/e k nhớ =))) )
P2: Intelligent person
P3: Tập trung vào p2, t/e hỏi nhiều vì tl khá ngắn
- Bố mẹ thường nói gì với trẻ em để tăng intelligent
- Game cho trẻ em để tăng intellgent (t/e trả lời lego bị hỏi thêm why :>>)
- Còn game nào nữa?
- Job nào? Why? Job nào nữa (t/e trả lời IT job, sau trả lời doctor)
Thầy gầy chắc người Ấn Độ ạ, siêu nice mở cửa cho mình vào và đi ra. Mỗi mh thì nguxi để thầy chịu đựng tầm 15’ thui ạ =))).
——
Speaking Triệu Việt Vương 27/3 - Bạn Nguyen Ngoc Anh
Part 1: Work or study
Changes
Singing
New year
Part 2: a time visit a person’s home which u liked but don’t like to live.
Part 3: polite
Different between local house in city and that in countryside....
P/s: Ncl là thầy hỏi nhiều lắm, mình không nhớ hết, examiner của mình là thầy hơi hói ở trán ạ, mình thi ở tầng 5, không nhớ rõ tên thầy ý, do căng thẳng quá nên mình không nghe được ạ. Lúc nói thì mình kiểu không nghe rõ 3-4 câu, hic, mình đang nói rồi thầy lại hỏi what, why.... không biết có làm sao không ạ!!!

🤓 หลายคนอาจเคยบ่น "เรียนเลขไปทำไม ไม่เห็นได้ใช้เลย"
อันนี้เป็นแค่ตัวอย่าง เพื่อให้รู้ว่าเลขที่เราเรียนตอนม.ปลาย
ไม่ควรทิ้งถ้าคิดจะเรียนคอมพิวเตอร์ ในระดับสูง
.
👉 1) สมการเชิงเส้น
เริ่มต้นจากสมการเส้นตรง ที่มีหน้าตาดังนี้ y=mx+c เรียกว่ารูปมาตรฐาน
- เมื่อ m เป็นความชัน
-ส่วน c เป็นจุดตัดแกน y
.
สมการเชิงเส้นเราจะได้เรียนในระดับ ม 4
พอในม.5 วิชา วิทยาการคำนวณ
ก็จะเห็นประโยชน์ของสมการเส้นตรงถูกนำไปใช้ในงาน data science (วิทยาการข้อมูล)
นำไปใช้วิเคราะห์ข้อมูลแบบ linear regression
.
กล่าวคือเมื่อเรามีข้อมูลย้อนหลังในอดีต
แล้วสามารถนำไปพล็อตลงบนกราฟแกน x กับ y
ผลปรากฏว่าข้อมูลมีความสัมพันธ์เป็นเส้นตรง
ในกรณีเราสามารถหาสมการเส้นตรงที่เหมาะสมสุด (optimize)
นำมาใช้พยากรณ์ข้อมูลล่วงหน้าในอนาคตได้
.
แต่ในกรณีที่ความสัมพันธ์ของข้อมูลพบว่าไม่ใช่เส้นตรง
เราสามารถใช้สมการที่ไม่ใช่เส้นตรง มาใช้พยากรณ์ข้อมูลก็ได้เช่นกัน
.
👉 2) เมทริกซ์
คือกลุ่มของจำนวนตัวเลข ที่เขียนเรียงกันเป็นรูปสี่เหลี่ยมผืนผ้าหรือจัตุรัส
นอกจากใช้แก้สมการหลายตัวแปรแล้ว
จะมีประโยชน์เวลานำไปประมวลภาพ (Image processing)
หรืองานพวกคอมพิวเตอร์วิชั่น (computer vision)
.
ต้องบอกอย่างนี้ว่า รูปภาพดิจิตอลที่เราเห็นเป็นสีสันสวยงาม
แต่ทว่าคอมไม่ได้มองเห็นเหมือนคน
มันมองเห็นเป็นเมทริกซ์ โดยข้างในเมทริกซ์ก็คือตัวเลขของค่าสี
และเราสามารถกระทำการคณิตศาสตร์กับรูปภาพได้
เช่น บวกลบ คูณหาร กับรูปภาพดิจิตอล ในมุมของเมทริกซ์
.
👉 3) ความน่าจะเป็น
ยกตัวอย่างเช่น ทฤษฏี Bayes' theorem
ทฤษฏีหนึงของความน่าจะเป็น
จะใช้หาว่าสมมติฐานใดน่าจะถูกต้องที่สุด โดยใช้ความรู้ก่อนหน้า (Prior Knowledge)
.
ทฤษีนี้ถูกนำไปใช้ในงานวิเคราะห์ข้อมูล รวมทั้งการเรียนรู้ของเครื่อง
เช่น จงหาความน่าจะเป็นที่ชาเขียวขวดนั้นจะผลิตจากโรงงานจากประเทศไทย
จงหาความน่าจะเป็นว่าผู้ป่วยจะเป็นโรคมะเร็ง เมื่อหายจากการติดเชื้อไวรัสโคโรนา
เป็นต้น
.
👉 4) แคลคูลัส
ตัวอย่างเช่น ถูกนำมาใช้ใน neural network
ซึ่งก็เครือข่ายประสาทเทียมที่เลียนแบบเซลล์สมอง
แต่จริงๆ ข้างในเครือข่ายจะประกอบไปด้วยน้ำหนัก
.
น้ำหนักที่ว่านี้มันก็คือตัวเลขจำนวนจริง ที่เริ่มต้นสุ่มขึ้นมา
แล้วเวลาจะหาค่าน้ำหนักที่เหมาะสม (optimize)
มันจะถูกปรับทีละเล็กทีละน้อย
โดยอาศัยหลักการเรื่องอนุพันธ์ หรือดิฟนั่นแหละ
.
👉 5) ตรรกศาสตร์
วิชานี้พูดถึง "ประพจน์" หมายถึงประโยคที่ให้ค่าออกมาเป็น True หรืด False
รวมถึงการใช้ตัวเชื่อมประพจน์แบบต่างๆ ไม่ว่าจะเป็น "และ" "หรือ" "ก็ต่อเมื่อ" เป็นต้น
.
ศาสตร์ด้านนี้เป็นพื้นฐานของระบบคอมพิวเตอร์
เพราะวงจรคอมพิวเตอร์พื้นฐาน มีแต่ตัวเลข 0 หรือ 1
จึงสามารถแทนด้วย False หรือ True ในทางตรรกศาสตร์
ไม่เพียงเท่านั้นวงจรอิเลคทรอนิกส์ ก็มีการดำเนินทางตรรกศาสตร์อีกด้วย
ไม่ว่าจะเป็น "และ" "หรือ" "ไม่" เป็นต้น
.
ยิ่งการเขียนโปรแกรม ยิ่งใช้เยอะ
เพราะต้องเปรียบเทียบเงื่อนไข True หรือ False
ในการควบคุมเส้นทางการทำงานของโปรแกรม
.
👉 6) ฟังก์ชัน
ฟังก์ชันคือความสัมพันธ์ จากเซตหนึ่งที่เรียกว่า 'โดเมน' ไปยังอีกเซตหนึ่งที่เรียกว่า 'เรนจ์' โดยที่สมาชิกตัวหน้าไม่ซ้ำกัน
ซึ่งคอนเซปต์ฟังก์ชันในทางคณิตศาสตร์
ก็ถูกนำไปใช้ในการเขียนโปรแกรมแบบ functional programming
.
👉 7) เรขาคณิตวิเคราะห์
ถูกนำไปใช้ในวิชาคอมกราฟิก หรือเกมส์
ในมุมมองของคนที่ใช้โปรแกรมวาดรูปต่างๆ หรือโปรแกรมสร้างแอนนิมเชั่นต่างๆ
เราก็แค่คลิกๆ ลากๆ ก็สร้างเสร็จแล้วใช่มั๊ยล่ะ
.
แต่หารู้หรือไม่ว่า เบื้องเวลาโปรแกรมจะวาดรูปทรง เช่น สี่เหลี่ยม วงรี ภาพตัดกรวยต่างๆ
ล้วนอาศัย เรขาคณิตวิเคราะห์ พล็อตวาดรูปทีละจุดออกมาให้เราใช้งาน
.
👉 8) ปีทาโกรัส
ทฤษฏีสามเหลี่ยมอันโด่งดังถูกนำไปใช้วัดระยะทางระหว่างจุดได้
ซึ่งจะมีประโยชน์ในการแยกแยะข้อมูล โดยใช้อัลกอริทึม
K-Nearest Neighbors (KNN)
ชื่อไทยก็คือ "ขั้นตอนวิธีการเพื่อนบ้านใกล้ที่สุด "
มันจะถูกนำไปใช้งานวิเคราะห์ข้อมูล รวมทั้งการเรียนรู้ของเครื่องอีกด้วย
ไม่ขอพูดเยอะเดี่ยว ม.5 ก็จะได้รู้จัก KNN ในวิชาวิทยาการคำนวณ
.
👉 9) ทฤษฏีกราฟเบื้องต้น
อย่างทฤษฏีกราฟออยเลอร์ (Eulerian graph)
ที่ได้เรียนกันในชั้น ม.5 จะมีประโยชน์ในวิชาคอม
เช่น ตอนเรียนในวิชา network ของคอมพิเตอร์ เพื่อหาเส้นทางที่ดี่สุดในการส่งข้อมูล
หรือจะมองโครงสร้างข้อมูลเป็นแบบกราฟก็ได้ ก็ลองนึกถึงลิงค์ต่างในเว็บไซต์ สามารถจับโยงเป็นกราฟได้ด้วยนะ
.
👉 10) เอกซ์โพเนนเชียล และลอการิทึม
เราอาจไม่เห็นการประยุกต์ใช้ตรงๆ นะครับ
แต่ในการประเมินประสิทธิภาพของอัลกอริทึม เวลาเขียนโปรแกรม
เขาจะใช้ Big O ขอไม่อธิบายเยอะแล้วกันเนอะ
เรื่องนี้มีเขียนอยู่ตำราวิทยาการคำนวณชั้นม.4 (ไปหาอ่านเอาได้)
.
ซึ่งเทอม Big O บางครั้งก็อาจเห็นอยู่ในรูปเอกซ์โพเนนเซียล หรือลอการิทึมนั่นเอง
ถ้าไม่เข้าใจว่า เอกซ์โพเนนเซียล หรือลอการิทึม คืออะไร
ก็ไม่จะอธิบายได้ว่าประสิทธิภาพของอัลอริทึมเราดีหรือแย่
.
+++++++
เป็นไงยังครับ สนใจอยากรู้ว่า เลข ม.ปลาย
สามารถนำไปใช้ศึกษาต่ออะไรอีกบ้างไหมเนี่ย
ถ้าอยากรู้ ผมเลยขอแนะนำหนังสือ (ขายของหน่อย)
.
หนังสือ "ปัญญาประดิษฐ์ (AI) ไม่ยาก"
เข้าใจได้ด้วยเลขม. ปลาย เล่ม 1 (เนื้อหาภาษาไทย)
ติดอันดับ Best seller ในหมวดหนังสือคอมพิวเตอร์ ของ MEB
.
เนื้อหาจะอธิบายปัญญาประดิษฐ์ (A) ในมุมมองเลขม.ปลาย
โดยปราศจากการโค้ดดิ้งให้มึนหัว
พร้อมภาพประกอบสีสันให้ดูอ่านง่าย
.
สนใจสั่งซ์้อได้ที่
👉 https://www.mebmarket.com/web/index.php…
.
ส่วนตัวอย่างหนังสือ ก็ดูได้ลิงค์นี้
👉 https://www.dropbox.com/s/fg8l38hc0k9b…/chapter_example.pdf…
.
ขออภัยเล่มกระดาษตอนนี้ยังไม่มี โทดทีนะครัชชช
.
✍เขียนโดย โปรแกรมเมอร์ไทย thai progammer
🤓 Many people may have complained about ′′ learning the number, why I didn't get to use it
This one is just an example to know the number we studied in high school. The end.
Don't leave if you want to learn computer at high level.
.
👉 1) Linear equation
Start from a straight line equation that looks like y=mx+c called standard photo
- When m is action
- c section is a cutting point y axis
.
Linear equation. We will learn in grade 4
Enough in the university. 5 Computational Science
It will see the benefits of straight line equation being applied to data science (data science) work.
Linear regression data analytics
.
i.e. when we have data back in the past
Then can be taken to plot on the x and y graph.
The result appears that the information is in a straight line.
In the case, we can find the most suitable straight line equation (optimize)
Advance future forecasts
.
But in case the relationship of information found out is not a straight line.
We can also use an equation that is not a straight line to propose information.
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👉 2) Matrix
Is a group of numbers written in a square or square.
Besides using to solve several variables.
It will be useful when it's leading to the image (Image processing)
Or computer vision jobs (computer vision)
.
I have to say this. The digital photos we see are colorful.
But the computer is not visible as a person.
It's seen as a matrix inside. The matrix is the number of colors.
And we can do math with pictures
Like, plus, multiply, multiply with digital photos in the corner of the matrix.
.
👉 3) Probability
For example, Bayes ' theorem theory.
Theory of probability
I will use which hypothesis is most accurate using previous knowledge (Prior Knowledge)
.
This theory is implemented in data analysis including machine learning.
For example, find the probability that green tea will be manufactured from factories from Thailand.
Find out the probability that patients will have cancer when they recover from Coronavirus infection.
Etc.
.
👉 4) Calculus
For example, being used in neural network.
Which is also an artificial neural network that imitates brain cells.
But really, the network is composed of weight.
.
This weight is also a random number of real numbers.
Time to find the right weight (optimize)
It will be slightly fined.
By living the principle of derivative or divative.
.
👉 5) Logic
This subject speaks of ′′ pronouncement ′′ meaning True or False sentence.
Including using different plural connectors, whether it's ′′ and or or if etc.
.
This aspect of science is the basis of computer system.
Because the basic computer circuit is only 0 or 1 numbers.
So it can be replaced with False or True in logic.
Not only that, the electronic circuit also has logical action.
Whether it's ′′ and or or no etc.
.
The more the programming, the more you use.
Because we have to compare True or False conditions.
In control of the programming path
.
👉 6) function
A function is a relationship from one set called ' domain ' to another set called ' Range ' by a unique face member.
Which concept function in mathematics.
It's been applied to functional programming.
.
👉 7) Analytical Geometry
Being applied in a graphic or games class
In view of people using various drawing programs or Animation Builders.
I'm just a click and drag and it's done. Aren't we?
.
But do you know that in time, the program will draw shapes like a square, rectangular, cone collage.
All living in geometry, analyzing the plot, drawing one at a time. Let us use it.
.
👉 👉 8) Tacorus
The famous triangle theory is implemented to measure the distance between points.
Which would be useful to digest data using algorithms.
K-Nearest Neighbors (KNN)
Thai name is ′′ The closest neighborhood process
It will also be implemented for data analysis including machine learning.
I don't want to talk too much. 5 to know KNN in computational science.
.
👉 9) Preliminary Graph Theory
Theoretically, Graphite Oler (Eulerian Graph)
I have studied in the middle school class. 5 will come in handy in computer class
For example, in a computer network class to find the best way to send information.
Or look at the data structure as a graph. Think about the different links on the website. They can be linked as graphics.
.
👉 10) m AND LOGARIETYM
We may not see the application straight away.
But in assessing the performance of programming time algorithm.
He's going to use Big O. Let's not explain a lot.
This story is written in the textbook. Calculating class. 4 (go to read)
.
The Big O term may sometimes be seen in an ex-ponytail or a logic.
If you don't understand what is Exponity or Logarithum?
It doesn't explain whether our algorithm performance is good or bad.
.
+++++++
How is it? If interested, I want to know the number. The end.
Can I study anything else?
If you want to know, I recommend a book (selling stuff)
.
Book ′′ Artificial Intelligence (AI) is not difficult ′′
You can understand by the number of km. End of book 1 (Thai content)
Best seller in MEB computer book category
.
Content describes Artificial Intelligence (A) in the view of the number. The end.
Without a coding dizzy
With colorful illustrations to be seen. Easy to read.
.
If interested, order at.
👉 https://www.mebmarket.com/web/index.php?action=BookDetails&data=YToyOntzOjc6InVzZXJfaWQiO3M6NzoiMTcyNTQ4MyI7czo3OiJib29rX2lkIjtzOjY6IjEwODI0NiI7fQ&fbclid=IwAR11zxJea0OnJy5tbfIlSxo4UQmsemh_8TuBF0ddjJQzzliMFFoFz1AtTo4
.
As private as a book, you can see this link.
👉 https://www.dropbox.com/s/fg8l38hc0k9b0md/chapter_example.pdf?dl=0
.
Sorry for paper book. I haven't got it yet. I'm sorry.
.
✍ Written by Thai programmer thai progammerTranslated

[時事英文] Jack Ma vs. Elon Musk on AI (馬雲對話馬斯克：人工智能)

What usually happens is a balance between two extremes but I’m leaning towards Mr. Musk’s views. What are your thoughts?

1. a balance between two extremes 兩個極端之間的平衡
2. lean towards 傾向

Full video: https://youtu.be/HJcwewYlmZQ

Related video: https://youtu.be/WSKi8HfcxEk

科技詞彙: https://wp.me/p44l9b-1ox (+mp3)

★★★★★★★★★★★★

Alibaba's Jack Ma and Tesla's Elon Musk took opposing views of the risks and potential rewards of artificial intelligence at an event in Shanghai.

阿里巴巴的馬雲和特斯拉（Tesla）的伊隆·馬斯克（Elon Musk）在上海進行了對話，二人對於人工智能（AI）技術的風險和潛在益處有截然不同的看法。

3. take an opposing view 採取相反的觀點
4. the potential risks and rewards 潛在的風險和回報
5. artificial intelligence 人工智能

★★★★★★★★★★★★

The Chinese entrepreneur said he was "quite optimistic" about AI and thought it was nothing for "street smart" people like them to be scared of. "I don't know man, that's like famous last words," responded Tesla's chief.

在上海舉行的世界人工智能大會上，馬雲表示，他對於人工智能「較為樂觀」，並且認為像他們這樣有「生活智慧」的人不應該害怕。而特斯拉創始人馬斯克卻說：「兄弟，我不確定，但是這聽起來像那種著名的最後遺言。」

6. remain optimistic 保持樂觀
7. street smart 適應都市生活的，具有都市生存智慧的 ; 在實際的生活經歷中得來的智慧
8. famous last words 著名的最後遺言

★★★★★★★★★★★★

The two did, however, agree on one topic: that one of the biggest problems the world is facing is population collapse. Their 45-minute conversation kicked off the World AI Conference (WAIC), which ties into China's goal of overtaking the US to become the world's leading artificial intelligence innovator by 2030.

不過，作為當今世界其中兩個最有影響力的科技產業領袖，兩人在一個問題上倒是有共識：世界面監的其中一個最大問題是人口的萎縮。馬雲和馬斯克進行的45分鐘對話，成為世界人工智能大會的開場。這次會議契合了中國想要在2030年之前取代美國領導世界人工智能革新的目標。

9. population decline 人口下降
10. kick off 開始
11. tie into 結合成一體，（使）配合得當，（使）有聯繫
12. leading artificial intelligence innovator 領先/領導的人工智能創新者

★★★★★★★★★★★★

Mr. Ma focused much of his comments on how machine learning could act as a force for good. He said it was something "to embrace" and would deliver fresh insights into how people think. "When human beings understand ourselves better, then we can improve the world better," he explained. Furthermore, he predicted AI would help create new kinds of jobs, which would require less of our time and be centred on creative tasks

馬雲認為，有學習功能的機器如何能夠成為一種正面的力量。他說，這是一件值得「歡迎」的事情，能夠給人們帶來新的想法。「當我們人類更加了解自己的時候，我們就能夠讓世界變得更好，」他說。除此之外，馬雲預言，人工智能將會創造更多新的工種，從而節省我們的時間，將人類可以更集中於創造性的工作。

13. focus (sth) on sb/sth 集中，特別關注
14. a force for good 善良力量
15. insight into 對….深刻的見解

★★★★★★★★★★★★

By contrast, Mr. Musk suggested that mass unemployment was a real concern. "AI will make jobs kind of pointless," he claimed. "Probably the last job that will remain will be writing AI, and then eventually, the AI will just write its own software."He added that there was a risk that human civilization could come to an end and ultimately be seen as a staging post for a superior type of life.

相反，馬斯克則認為，大規模失業將會是一個實實在在的問題。「人工智能會令職位變得有點無意義，」馬斯克說。「很可能最後剩下的工種就是給AI寫程序，然後最終，AI還是自己編寫自己的軟件。」馬斯克還表示，人類文明可能因此而終結，並且最終將成為更高級生命體的墊腳石。

16. mass unemployment 大規模失業
17. a real concern 實實在在的問題
18. make…pointless 令…變得無意義
19. come to an end 終結
20. staging post （長途旅行的）中途站

★★★★★★★★★★★★

To avoid such a fate, he said we needed to find a way to connect our brains to computers so that we could "go along for the ride with AI" - something he is trying to achieve via one of his latest start-ups. Otherwise, he cautioned, AI would become weary of trying to communicate with humans, as we would be much slower thinkers in comparison.

馬斯克表示，為了避免這樣一種命運，我們需要找到一種方法將我們的大腦連接到電腦上，讓我們能夠「跟著AI走」——他正在試圖通過一家他最新建立的初創公司做到這一點。否則，他警告說，AI將會對試圖與人類溝通感到厭倦，因為相比之下我們的思考會比電腦慢得多。

21. to avoid such a fate 避免這樣一種命運
22. go along for the ride 跟著走；搭順風車；湊熱鬧
23. be weary of 厭煩，不耐煩；倦

★★★★★★★★★★★★

By contrast, Mr. Ma acknowledged that AI could now beat humans at games like chess and Go, but claimed computers would only be one of several intelligent tools that we would develop in time. Although Mr. Ma acknowledged that we needed to find ways to become "more creative and constructive", he concluded that "my view is that [a] computer may be clever, but human beings are much smarter".

相反，馬雲則認為，雖然AI現在可能在象棋和「精靈寶可夢」（Pokemon Go）等遊戲當中擊敗人類，但是他堅持認為，電腦最終只會是我們發展的多種智能工具之一。雖然馬雲承認，我們需要尋找方法來變得「更加有創造力和建設性」，但是他斷定，「我的觀點是電腦可能很聰明，但是人類還是會聰明得多。

24. acknowledge 承認；認可…屬實或存在
25. more creative and constructive 更具創造性和建設性

Mr. Musk responded: "Yeah, definitely not.
馬斯克對此回應說：「好吧，肯定不是。」

★★★★★★★★★★★★

Towards the end of the event the two men came together on one point - that concerns about overpopulation were misguided." Assuming... there's a benevolent future with AI, I think that the biggest problem the world will face in 20 years is population collapse," said Mr. Musk."I want to emphasize this, the biggest issue in 20 years will be population collapse, not explosion collapse." Mr. Ma said he was absolutely in agreement.

到最後，馬斯克和馬雲在一件事情上的觀點是一致的——對人口過剩的擔憂是一種誤解。馬斯克說：「假設……AI能帶來一個美好的未來的話，我認為20年後的世界面臨的最大問題將會是人口銳減。」「我想強調這一點，20年後的最大問題將會是人口銳減，不是人口爆炸。」馬雲表示，他對此完全同意。

26. come together on... 在…的觀點是一致的
27. misguide 誤導
28. a benevolent future with 美好/仁慈的未來
29. population collapse 人口銳減
30. be in agreement 同意

★★★★★★★★★★★★

Last, consider the purpose of this conference. How does the information presented relate to you? What can you do with it?

Sources:
https://www.bbc.com/news/technology-49508091

https://www.bbc.com/zhongwen/trad/science-49520345

https://www.nytimes.com/reuters/2019/08/29/technology/29reuters-china-tech.html

https://www.businessinsider.com/elon-musk-reiterates-global-population-is-headed-for-collapse-2019-6

Photo: Businesstimes.com — https://www.businesstimes.com.sg/technology/tech-tycoons-jack-ma-elon-musk-spar-on-future-of-artificial-intelligence