中級 美國腔 63879 分類 收藏
開始影片後,點擊或框選字幕可以立即查詢單字
字庫載入中…
回報字幕錯誤
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.
    您必須登入才有此功能
提示:點選文章或是影片下面的字幕單字,可以直接快速翻譯喔!

載入中…

載入中…

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)

63879 分類 收藏
clara.english.0001 發佈於 2017 年 10 月 28 日   Su Kids 翻譯   Mandy Lin 審核

影片簡介

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。

【副詞修飾動詞】
Over the past few years, the profit of semiconductor industry has dropped dramatically.
半導體產業的利潤在過去幾年裡大幅下跌。


【副詞修飾副詞】
Last year, the government spending increased very dramatically.
政府支出在去年巨幅增加。


*同場加映:
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.
那淘氣的女孩搞笑地模仿老師的南方口音。

The technological advancement has enabled computers to imitate the complex functions of human brains.
科技的進步使電腦可以模仿人類大腦的複雜功能。


*同場加映:
【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.
我們在今天結束前,終於抵達了山頂。

How are we going to make our way through that crowd?
我們要怎麼擠過那些人群?


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.
我相信擁有碩士學歷,我可以能夠有所成就。


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.
我想知道你有什麼能力。

He is capable of leading this team.
他俱有領導這個團隊的能力。


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.
大衛有成為一位優秀老師的潛能。

The professional manager has the capability to do accurate market forecast.
專業經理人能夠做出精準的市場預測。


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.
他能夠找出這個問題的答案。

I have the ability to get the job done quickly.
我可以很快完成這個工作。


*同場加映:
【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.
政府正在試圖解決空氣污染的問題。

How to tackle the problem of bullying within campus has been the priority for school authority.
如何處理校園中的霸凌問題是校方的首要事項。


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.
當我與他交涉這件事時,他承認他犯了錯。


*同場加映:
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

影片學習單字重點

loading

影片討論

載入中…
  1. 1. 單字查詢佳句收藏

    選取單字或佳句,可即時查詢字典及收藏!

  2. 2. 單句重複播放

    可重複聽取一句單句,加強聽力!

  3. 3. 使用快速鍵

    使用影片快速鍵,讓學習更有效率!

  4. 4. 關閉語言字幕

    進階版練習可關閉字幕純聽英文哦!

  5. 5. 內嵌播放器

    可以將英文字幕學習播放器內嵌到部落格等地方喔

  6. 6. 展開播放器

    可隱藏右方全文及字典欄位,觀看影片更舒適!

  1. 英文聽力測驗

    挑戰字幕英文聽力測驗!

  1. 點擊展開筆記本讓你看的更舒服

  1. UrbanDictionary 俚語字典整合查詢。一般字典查詢不到你滿意的解譯,不妨使用「俚語字典」,或許會讓你有滿意的答案喔