中級 美國腔 66178 分類 收藏
So this is my niece.
Her name is Yahli.
She is nine months old.
Her mom 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.
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.
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.
Now, given the right data,
machines are gonna 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.
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.
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.
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.
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 path-breaking litigation.
But machines will shrink their ranks
and make these jobs harder to come by.
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.
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.
Thank you.



【TED】你的工作會被機器取代嗎?(The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom)

66178 分類 收藏
clara.english.0001 發佈於 2017 年 10 月 28 日   Su Kids 翻譯   Mandy Lin 審核


時代和科技的進步,讓越來越多工作逐漸被機器取代。我們必須去思考自身的工作價值應該要如何提升,如何在科技的洪流中,依然保持不墜的存在價值,現在讓我們一起聽聽 Anthony Goldbloom 對於這個議題的分享吧!

1dramatically   0:24
這是一個副詞,字面意義為「戲劇化地;很大程度地」。在影片中它是放在形容詞 different 前面,這是「副詞修飾形容詞」的用法。

副詞可以修飾形容詞、動詞和副詞。修飾形容詞時,它用來增加形容詞的強度,如同我們常見的 very beautiful 的用法,藉由副詞 very 修飾後,增強了形容詞「美麗」的程度。

Over the past few years, the profit of semiconductor industry has dropped dramatically.

Last year, the government spending increased very dramatically.

比爾蓋茲專訪 (Exclusive interview of Bill Gates - co-founder & chairman of microsoft)

2mimic     0:50
mimic 作為動詞,解釋為「模仿」。但如果大家有看電影過《模仿遊戲》,也許會覺得奇怪,為什麼英文片名是 The Imitation Game,而不是 The Mimic Game 呢?

mimicimitation 雖然字義上都是「模仿」,但是 mimic 通常指較不精確且粗淺的模仿,而 imitate 則是面面俱到的重現對方的行為。以下提供兩個句子讓各位體會一下它的些微不同:
The naughty girl mimicked her teacher's southern accent.

The technological advancement has enabled computers to imitate the complex functions of human brains.

【TED-Ed】安慰劑效應的力量 (The power of the placebo effect - Emma Bryce)

3making one's way      1:10
這個片語字面意義可以作為「前進/前往」的意思,但是和一般常聽到的 go ahead 或是 go forward 是不太一樣的,因為它的受詞通常都會是特定的目的地,而它的介系詞通常是 to、into 或 through 等表示方向的介系詞。
We made our way to the mountaintop at the end of the day.

How are we going to make our way through that crowd?

但是這個片語在象徵性的意義上也可以解釋為「有進展、成功」,通常是指個人職涯上的進展,而後面都會加上 in lifein the world
By getting a master's degree, I believe I can make my way in the world.

4be capable of   1:28
這個片語的意思是「有...能力」,而它所指的能力通常是尚未表現出來,但是具備該種能力的潛力。用法是 be capable of 的後面必須加上動名詞 (v-ing)
I want to know what you are capable of.

He is capable of leading this team.

此時,capable是形容詞,而它的名詞是 capability。因此,be capable of 也可寫成 have capability to + 原形動詞 或是 have capability of + 動名詞
David has the capability of becoming an excellent teacher.

The professional manager has the capability to do accurate market forecast.

同樣表達「有...能力」的用法還有 be able tohave ability to,但不同的是,它所講的能力偏向本來就有的能力,而介系詞 to 的後面一樣加上原形動詞。
He is able to find out the answer of this question.

I have the ability to get the job done quickly.

【TED】使機器人更聰明 (Make robots smarter - Ayanna Howard)

5tackle   2:25
tackle 這個動詞在解釋為「處理、解決」時,作為及物動詞,與其相同意思並可交換使用的還有 deal with

但是 tackle the problemdeal with the problem 意義有點不太一樣。tackle the problem 通常是說這個問題很難解決,依舊在解決問題這件事上掙扎著;而 deal with the problem 通常這個問題是有能力去解決且並不困難的。
The government is trying to tackle the problem of air pollution.

How to tackle the problem of bullying within campus has been the priority for school authority.

tackle 也有「交涉問題」的意思,用法 tackle sb. about sth. 即表示「與某人交涉」。
When I tackled him about it, he admitted he had made a mistake.

終止兒童新娘-你我有責 (The world we want: End child marriage)

看完這個影片,你是不是跟小 V 一樣開始為未來擔憂呢?為了在工作上擁有不被機器取代的優勢,從現在開始努力充實自己也不算晚啊!我們一起奮鬥吧!

文/ Sunny Yang




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