Anthony Goldbloom: The jobs we’ll lose to machines and the ones we won’t机器会抢走我们的饭碗吗？
难度级别：★★★★★
燕山大学 刘立军 宋葳 编写
◇INTRODUCTION
Machine learning isn’t just for simple tasks like assessing credit risk and sorting mail anymore today, it’s capable of far more complex applications, like grading essays and diagnosing diseases. With these advances comes an uneasy question: Will a robot do your job in the future?
◇BEFORE VIEWING
TASK 1: VOCABULARY PREVIEW
1. automate v. to use machines and computers instead of people to do a job or task 使自动化。例如：
l The entire manufacturing process has been automated. 整个生产过程已自动化。
l The factory is now fully automated . 这家工厂现在是全自动化。
2. academia n. the world of learning, teaching, research, etc. at universities, and the people involved in it 学术界
3. algorithm n. (especially computing 计) a set of rules that must be followed when solving a particular problem 算法；计算程序
4. diabetic adj. having or connected with diabetes 糖尿病的；患糖尿病的。例如：
l She's diabetic. 她患有糖尿病。
l a diabetic patient糖尿病患者
l diabetic complications糖尿病并发症
5. retinopathy n. 视网膜病
6. ophthalmologist n. a doctor who studies and treats the diseases of the eye 眼科医生
7. cross-pollinate v. (biology 生) to move pollen from a flower or plant onto another flower or plant so that it produces seeds 使异花传粉；为（植物）异花授粉
8. boilerplate n. (North Amercian English) a standard form of words that can be used as a model for writing parts of a business document, legal agreement, etc. （可供模仿的）样板文件，文件范例
9. pathbreaking adj. 开创性的
10. litigation n. (law 律) the process of making or defending a claim in court 诉讼；
打官司。例如：
l The company has been in litigation with its previous auditors for a full year. 那家公司与前任审计员已打了整整一年的官司。
TASK 2: TOPIC PREVIEW
Work in pairs and discuss the question.
Introduce something about robots. Will a robot do your job in the future?
◇VIEWING
TASK 3:
Read the table. Then watch the video and complete the table with the words you hear.
Topic | The jobs we’ll lose to machines and the ones we won’t |
Introduction | Opening | By the time Yahli goes to college, the jobs her parents do are going to look dramatically different. |
Statement | In 2013, researchers at Oxford University did a study on the future of work. They concluded that almost ____________________ jobs have a high risk of ______________________ by machines. |
Transition | | This gives us _____________________ on what machines can do, what they can’t do and what jobs they might automate or threaten. |
Example | My company, Kaggle, operates on the cutting edge of machine learning. |
Body | Main point 1 | We have no chance of competing against machines on ________________________________________. |
Example | Simple tasks l Assessing credit risk l Sorting the mail |
Complex tasks l Grading essays l Diagnosing an eye disease |
Main point 2 | But there are things we can do that machines can’t do. Machines cannot compete with us when it comes to ____________________, and this puts ______________________________ on the human tasks that machines will automate. |
Main point 3 | The future state of any single job lies in the answer to a single question: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations? On frequent, high-volume tasks, machines are getting ____________________________________. |
Example | l Grading essays |
l Diagnosing diseases |
l Conducting our audits |
Conclusion | 1 | It will be humans that are creating the copy behind our ____________________________, and it will be humans that are developing our ______________________________. |
2 | Whatever you decide to do, let every day bring you a new challenge. If it does, then you will __________________________________. |
TASK 4:
Read the table. Then watch the video and complete the table with the words you hear.
Development of machine learning
Time | Characteristics | Examples |
In the early ’90s | It started with relatively _____________. | It started with things like ____________________ from loan applications, sorting the mail by _________________________ from zip codes. |
Over the past few years | We have made dramatic breakthroughs. Machine learning is now capable of far, far more __________________________. | In 2012, Kaggle challenged its community to build an algorithm that could ___________________. The winning algorithms were able to match the grades given by human teachers. |
Last year, we issued _________________________. Can you take images of the eye and diagnose an eye disease called diabetic retinopathy? Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists. |
Now | Now, given the right data, machines are going to ____________________ humans at tasks like this. | A teacher might read _________ essays over _______________. An ophthalmologist might see 50,000 eyes. A machine can read ____________________ essays or see millions of eyes within minutes. |
◇AFTER VIEWING
TASK 5: DISCUSSION
Work in pairs and discuss the questions.
What’s the main idea of this speech? Do you agree or disagree with the speaker?
SUGGESTED ANSWERS
◇BEFORE VIEWING
TASK 2: TOPIC PREVIEW
We’re on the verge of （接近于，行将）the Fourth Industrial Revolution, meaning that Artificial Intelligence (AI 人工智能) and quantum computing （量子计算）are fast becoming a reality.
Robots are providing customer service, driving cars, manning hotel reception desks, helping kids with autism （自闭症）, serving up martinis （马提尼酒）and even fighting cancer.
IBM’s supercomputer, Watson （是认知计算系统的杰出代表，也是一个技术平台。认知计算代表一种全新的计算模式，它包含信息分析，自然语言处理和机器学习领域的大量技术创新，能够助力决策者从大量非结构化数据中揭示非凡的洞察 。）, has gone from being a contestant on Jeopardy (that absolutely obliterated its human competition) to powering Ross, a cognitive megacomputer that’s helping lawyers sift through tens of thousands of legal documents to prepare for cases.
Then there’s Google’s AI bot AlphaGo （阿尔法围棋，是一款围棋人工智能程序。这个程序利用“价值网络”去计算局面，用“策略网络”去选择下子。）which has been programmed to use deep learning algorithms to master the ancient Korean game of Go - a game that’s widely accepted to be far more complex than chess. AlphaGo’s become so smart that it recently beat legendary Go world champion Lee Sedol by a convincing 4-1.
It’s safe to say that robots are here to stay, and they want our jobs.
Adapted from http://www.careerfaqs.com.au/news/news-and-views/future-of-work-how-to-stop-a-robot-from-stealing-your-job
◇VIEWING
TASK 3:
one in every two
being automated
a unique perspective
frequent, high-volume tasks
tackling novel situations
a fundamental limit
smarter and smarter
marketing campaigns
business strategy
stay ahead of the machines
TASK 4:
simple tasks
assessing credit risk
reading handwritten characters
complex tasks
grade high-school essays
an even more difficult challenge
outperform
10,000
a 40-year career
millions of
◇AFTER VIEWING
TASK 5:
The main idea of this TED talk is about what jobs will be taken by machines and the ones that won’t in the near future.
My opinion about this Ted talk is that I think that it’s cool that we have developed as a planet and made some great progress that we humans don’t have to do work anymore, but I also think that it is scary because it would end up in a lot of people losing their jobs, which is miserable. Machines taking over our jobs could potentially impact the world in a negative way because a lot of people could lose their jobs and will be under pressure undoubtedly. There’s no doubt about it that machines can do surprising things and that in the years to come, the abilities of robots will still increase. The problem is how to get prepared for the future great unemployment because of the increasingly development of machine learning?
附件：TRANSCRIPT
Anthony Goldbloom: The jobs we’ll lose to machines and the ones we won’t
0:11
So this is my niece. Her name is Yahli. She is nine months old. Her mum is a doctor, and her dad is a lawyer. By the time Yahli goes to college, the jobs her parents do are going to look dramatically different.
0:26
In 2013, researchers at Oxford University did a study on the future of work. They concluded that almost one in every two jobs have a high risk of being automated by machines. Machine learning is the technology that’s responsible for most of this disruption. It’s the most powerful branch of artificial intelligence. It allows machines to learn from data and mimic some of the things that humans can do. My company, Kaggle, operates on the cutting edge of machine learning. We bring together hundreds of thousands of experts to solve important problems for industry and academia. This gives us a unique perspective on what machines can do, what they can’t do and what jobs they might automate or threaten.
1:08
Machine learning started making its way into industry in the early ’90s. It started with relatively simple tasks. It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes. Over the past few years, we have made dramatic breakthroughs. Machine learning is now capable of far, far more complex tasks. In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays. The winning algorithms were able to match the grades given by human teachers. Last year, we issued an even more difficult challenge. Can you take images of the eye and diagnose an eye disease called diabetic retinopathy? Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists.
1:56
Now, given the right data, machines are going to outperform humans at tasks like this. A teacher might read 10,000 essays over a 40-year career. An ophthalmologist might see 50,000 eyes. A machine can read millions of essays or see millions of eyes within minutes. We have no chance of competing against machines on frequent, high-volume tasks.
2:19
But there are things we can do that machines can’t do. Where machines have made very little progress is in tackling novel situations. They can’t handle things they haven’t seen many times before. The fundamental limitations of machine learning is that it needs to learn from large volumes of past data. Now, humans don’t. We have the ability to connect seemingly disparate threads to solve problems we’ve never seen before.
2:45
Percy Spencer was a physicist working on radar during World War II, when he noticed the magnetron was melting his chocolate bar. He was able to connect his understanding of electromagnetic radiation with his knowledge of cooking in order to invent - any guesses? - the microwave oven.
3:02
Now, this is a particularly remarkable example of creativity. But this sort of cross-pollination happens for each of us in small ways thousands of times per day. Machines cannot compete with us when it comes to tackling novel situations, and this puts a fundamental limit on the human tasks that machines will automate.
3:21
So what does this mean for the future of work? The future state of any single job lies in the answer to a single question: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations? On frequent, high-volume tasks, machines are getting smarter and smarter. Today they grade essays. They diagnose certain diseases. Over coming years, they’re going to conduct our audits, and they’re going to read boilerplate from legal contracts. Accountants and lawyers are still needed. They’re going to be needed for complex tax structuring, for pathbreaking litigation. But machines will shrink their ranks and make these jobs harder to come by.
3:59
Now, as mentioned, machines are not making progress on novel situations. The copy behind a marketing campaign needs to grab consumers’ attention. It has to stand out from the crowd. Business strategy means finding gaps in the market, things that nobody else is doing. It will be humans that are creating the copy behind our marketing campaigns, and it will be humans that are developing our business strategy.
4:20
So Yahli, whatever you decide to do, let every day bring you a new challenge. If it does, then you will stay ahead of the machines.
4:30
Thank you.
4:31
(Applause)