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  • So this is my niece.

    這是我的小侄女

  • Her name is Yahli.

    她叫做雅里

  • She is nine months old.

    她現在九個月大

  • Her mom is a doctor, and her dad is a lawyer.

    她的媽媽是個醫生,爸爸則是一名律師

  • By the time Yahli goes to college,

    等到她上大學時,

  • the jobs her parents do are going to look dramatically different.

    她爸媽所做的工作,看起來會跟現在非常不同

  • In 2013, researchers at Oxford University did a study on the future of work.

    在2013年,牛津大學的研究人員對未來的工作進行調查

  • They concluded that almost one in every two jobs have a high risk of being automated by machines.

    他們得出結論,未來每兩個工作中,就有一個面臨被機器自動化接管的高風險

  • Machine learning is the technology that's responsible for most of this disruption.

    機器學習這項科技是導致大部分工作斷裂的主因

  • It's the most powerful branch of artificial intelligence.

    它是人工智慧最強大的一個分支

  • It allows machines to learn from data,

    它使機器可以從數據中學習,

  • and mimic some of the things that humans can do.

    並模仿人類可以做的一些事情

  • My company, Kaggle, operates on the cutting edge of machine learning.

    我的公司「Kaggle」,運營著尖端機器學習科技

  • We bring together hundreds of thousands of experts

    我們集合數十萬名這類的專家,

  • to solve important problems for industry and academia.

    要來解決工業和學術界的重要問題

  • This gives us a unique perspective on what machines can do,

    這給了我們一個獨特的觀點,瞭解什麼是機器可以做,

  • what they can't do,

    什麼是它們不能做的,

  • and what jobs they might automate or threaten.

    以及哪些工作可能會被自動化或威脅

  • Machine learning started making its way into industry in the early '90s.

    機器學習在90年代初開始進入工業界

  • It started with relatively simple tasks.

    它始於相對簡單的工作

  • It started with things like assessing credit risk from loan applications,

    它始於像評估貸款申請者的信用風險

  • sorting the mail by reading handwritten characters from zip codes.

    以及透過辨識手寫郵遞區號,將郵件進行分類

  • Over the past few years, we have made dramatic breakthroughs.

    過去幾年中,我們取得重大突破

  • Machine learning is now capable of far, far more complex tasks.

    機器學習現已能勝任較以往更為複雜的任務

  • In 2012, Kaggle challenged its community

    在2012年,Kaggle 挑戰其社群

  • to build an algorithm that could grade high-school essays.

    建立一個可以為高中程度的論文評分的演算法

  • The winning algorithms were able to match the grades

    當時獲勝的演算法所產生的評分已經

  • given by human teachers.

    和人類教師的評分不相上下

  • Last year, we issued an even more difficult challenge.

    去年,我們發出一個更困難的挑戰

  • Can you take images of the eye and diagnose an eye disease called diabetic retinopathy?

    你能透過拍攝眼睛成像診斷出「糖尿病性視網膜病變」這種眼疾嗎?

  • Again, the winning algorithms were able to match the diagnoses

    再一次,獲勝的演算法能夠完全匹敵

  • given by human ophthalmologists.

    由人類眼科醫生所做出的診斷

  • Now, given the right data,

    目前,只要賦予正確的數據,

  • machines are gonna outperform humans at tasks like this.

    在這種任務上,機器的表現將勝過人類

  • A teacher might read 10,000 essays over a 40-year career.

    在40年的職業生涯中,一個老師可能讀過一萬篇文章

  • An ophthalmologist might see 50,000 eyes.

    一名眼科醫生可能看過50,000隻眼睛

  • A machine can read millions of essays or see millions of eyes

    但一台機器可以讀過數百萬篇文章,或者看過數百萬隻眼睛

  • within minutes.

    在數分鐘內

  • We have no chance of competing against machines

    我們完全沒有機會與機器競爭

  • on frequent, high-volume tasks.

    針對這種頻繁、大批量的工作

  • But there are things we can do that machines can't do.

    但還是有些東西是我們可以做,機器不能做的

  • Where machines have made very little progress is in tackling novel situations.

    機器在處理未曾遭遇的新情況這方面的進步很慢

  • They can't handle things they haven't seen many times before.

    他們無法處理以前沒有見過幾次的東西

  • The fundamental limitations of machine learning

    這正是機器學習的基本限制,

  • is that it needs to learn from large volumes of past data.

    它需要從過去的大量數據中加以學習

  • Now, humans don't.

    人類則不必

  • We have the ability to connect seemingly disparate threads

    我們有能力連結看似不同的個別線頭,

  • to solve problems we've never seen before.

    解決我們從未見過的問題

  • Percy Spencer was a physicist working on radar during World War II,

    波西‧史賓塞是二次世界大戰期間,從事雷達研究的物理學家,

  • when he noticed the magnetron was melting his chocolate bar.

    他注意到磁控管會融化他的巧克力棒

  • He was able to connect his understanding of electromagnetic radiation

    他能連結接自己對電磁輻射的理解

  • with his knowledge of cooking

    和烹飪的知識

  • in order to invent -- any guesses? -- the microwave oven.

    而發明了... 猜到了嗎? 微波爐

  • Now, this is a particularly remarkable example of creativity.

    現在,這項發明成為特別引人注目的創造性案例

  • But this sort of cross-pollination happens for each of us in small ways thousands of times per day.

    但是,這類「異花授粉」的情況,在我們週圍每天都以小的方式出現數千次

  • Machines cannot compete with us

    機器無法與人競爭,

  • when it comes to tackling novel situations,

    特別當面臨處理新狀況時,

  • and this puts a fundamental limit on the human tasks that machines will automate.

    這也為機器自動化可替代的人類工作,套上基本限制

  • So what does this mean for the future of work?

    這對未來的工作又意味著什麼?

  • The future state of any single job lies in the answer to a single question:

    任何工作的未來形勢,都端看一個問題的答案

  • To what extent is that job reducible to frequent, high-volume tasks,

    任務可以減化到頻繁或大批量的程度

  • and to what extent does it involve tackling novel situations?

    又有多大程度涉及處理新的狀況?

  • On frequent, high-volume tasks, machines are getting smarter and smarter.

    在頻繁、大批量的工作上,機器會變得越來越聰明。

  • Today they grade essays. They diagnose certain diseases.

    像今天,它們批改文章、 它們診斷某些疾病

  • Over coming years, they're going to conduct our audits,

    未來幾年,它們將處理我們的帳目審計

  • and they're going to read boilerplate from legal contracts.

    它們會從法律合約中解讀法律語言

  • Accountants and lawyers are still needed.

    但社會對會計師和律師仍有需求

  • They're going to be needed for complex tax structuring,

    複雜的稅務結構仍需要他們,

  • for path-breaking litigation.

    開創性的訴訟也需要他們

  • But machines will shrink their ranks

    但是機器會降低這兩類人士的社會排名,

  • and make these jobs harder to come by.

    且使得這些工作更難獲得

  • Now, as mentioned,

    現在,如同我前面所說的

  • machines are not making progress on novel situations.

    機器在處理新情況不易進步

  • The copy behind a marketing campaign needs to grab consumers' attention.

    行銷活動背後的故事版本,需要吸引消費者的注意。

  • It has to stand out from the crowd.

    如此它才能從群眾之中脫穎而出

  • Business strategy means finding gaps in the market,

    商業策略則意味著必須找到市場缺口

  • things that nobody else is doing.

    那些無人在做的事情

  • It will be humans that are creating the copy behind our marketing campaigns,

    創造行銷活動背後故事的是我們人類,

  • and it will be humans that are developing our business strategy.

    而開展商業策略的也將是我們人類

  • So Yahli, whatever you decide to do,

    所以,雅里,無論你未來決定做什麼,

  • let every day bring you a new challenge.

    讓每一天,都為你帶來新的挑戰。

  • If it does, then you will stay ahead of the machines.

    如果是這樣,那麼你將永遠超越機器。

  • Thank you.

    謝謝你們

So this is my niece.

這是我的小侄女

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B1 中級 中文 美國腔 TED 機器 人類 工作 學習 處理

【TED】安東尼.葛博倫: 即將要被機器取代以及無法取代的工作 (The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom)

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