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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, the jobs her parents do are gonna look dramatically different.

    等到她上大學時,

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

    她爸媽所做的工作,看起來會跟現在非常不同

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

    在2013年,牛津大學的研究人員對未來的工作進行調查

  • Now, machine learning is the technology that's responsible for most of this disruption.

    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

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

    機器學習這項科技是導致大部分工作斷裂的主因

  • It allows machines to learn from data, and mimic some of the things that humans can do.

    它是人工智慧最強大的一個分支

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

    它使機器可以從數據中學習,

  • We bring together hundreds of thousands of experts to solve important problems for industry and academia.

    並模仿人類可以做的一些事情

  • So this gives us a unique perspective on what machines can do, what they can't do, and what jobs they might automate or threaten.

    我的公司「Kaggle」,運營著尖端機器學習科技

  • So machine learning started making its way into industry in the early 90s.

    我們集合數十萬名這類的專家,

  • And it started with relatively simple tasks.

    要來解決工業和學術界的重要問題

  • It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes.

    這給了我們一個獨特的觀點,瞭解什麼是機器可以做,

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

    什麼是它們不能做的,

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

    以及哪些工作可能會被自動化或威脅

  • In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays.

    機器學習在90年代初開始進入工業界

  • The winning algorithms were able to match the grades given by human teachers.

    它始於相對簡單的工作

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

    它始於像評估貸款申請者的信用風險

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

    以及透過辨識手寫郵遞區號,將郵件進行分類

  • Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists.

    過去幾年中,我們取得重大突破

  • Now, given the right data, machines are gonna outperform humans at tasks like this:

    機器學習現已能勝任較以往更為複雜的任務

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

    在2012年,Kaggle 挑戰其社群

  • An ophthalmologist might see 50,000 eyes.

    建立一個可以為高中程度的論文評分的演算法

  • A machine can read millions of essays or see millions of eyes within minutes.

    當時獲勝的演算法所產生的評分已經

  • We have no chance of competing against machines on frequent, high-volume tasks.

    和人類教師的評分不相上下

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

    去年,我們發出一個更困難的挑戰

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

    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?

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

    再一次,獲勝的演算法能夠完全匹敵

  • The fundamental limitation of machine learning is that it needs to learn from large volumes of past data.

    由人類眼科醫生所做出的診斷

  • Now, humans don't.

    目前,只要賦予正確的數據,

  • We have the ability to connect seemingly disparate threads to solve problems we've never seen before.

    在這種任務上,機器的表現將勝過人類

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

    在40年的職業生涯中,一個老師可能讀過一萬篇文章

  • 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...

    一名眼科醫生可能看過50,000隻眼睛

  • Any guesses? The microwave oven.

    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

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

    在數分鐘內

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

    我們完全沒有機會與機器競爭

  • Machine 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.

    針對這種頻繁、大批量的工作

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

    但還是有些東西是我們可以做,機器不能做的

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

    機器在處理未曾遭遇的新情況這方面的進步很慢

  • To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations?

    他們無法處理以前沒有見過幾次的東西

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

    這正是機器學習的基本限制,

  • Today they grade essays. They diagnose certain diseases.

    它需要從過去的大量數據中加以學習

  • Over coming years, they're gonna conduct our audits, and they're gonna read boilerplate from legal contracts.

    人類則不必

  • Accountants and lawyers are still needed.

    我們有能力連結看似不同的個別線頭,

  • They're gonna be needed for complex tax structuring, for path-breaking litigation.

    解決我們從未見過的問題

  • But machines will shrink their ranks and make these jobs harder to come by.

    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,

  • Now, as mentioned, machines are not making progress on novel situations.

    他注意到磁控管會融化他的巧克力棒

  • The copy behind a marketing campaign needs to grab consumers' attention. It has to stand out from the crowd.

    他能連結接自己對電磁輻射的理解

  • Business strategy means finding gaps in the market, things that nobody else is doing.

    和烹飪的知識

  • It will be humans that are creating the copy behind our marketing campaigns, and it will be humans that are developing our business strategy.

    而發明了... 猜到了嗎? 微波爐

  • So Yahli, whatever you decide to do, let every day bring you a new challenge.

    現在,這項發明成為特別引人注目的創造性案例

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

    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

  • Thank you.

    機器無法與人競爭,

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 診斷

【TED】機器人無法取代的工作,你須擁有什麼關鍵能力?(The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom)

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    clara.english.0001 發佈於 2022 年 08 月 03 日
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