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So this is my niece. Her name is Yahli. She is nine months old.
這是我的侄女。她叫Yahli。她只有九個月大。
Her mum is a doctor, and her dad is a lawyer.
她媽媽是一名醫(yī)生，爸爸是一名律師。
By the time Yahli goes to college,
等到Y(jié)ahli上大學(xué)的時(shí)候，
the jobs her parents do are going to look dramatically different.
像她父母這樣的工作將面目全非。
In 2013, researchers at Oxford University did a study on the future of work.
2013年，牛津大學(xué)的研究人員做了一項(xiàng)關(guān)于未來就業(yè)的研究。
They concluded that almost one in every two jobs have a high risk of being automated by machines.
他們得出結(jié)論：差不多將近一半的工作都有被機(jī)器自動化取代的危險(xiǎn)。
Machine learning is the technology that's responsible for most of this disruption.
而機(jī)器學(xué)習(xí)應(yīng)對這種顛覆負(fù)主要責(zé)任。
It's the most powerful branch of artificial intelligence.
它是人工智能最強(qiáng)大的分支。
It allows machines to learn from data and mimic some of the things that humans can do.
允許機(jī)器從現(xiàn)有數(shù)據(jù)中學(xué)習(xí)并模仿人類的所作所為。
My company, Kaggle, operates on the cutting edge of machine learning.
我的公司Kaggle專注于尖端的機(jī)器學(xué)習(xí)。
We bring together hundreds of thousands of experts to solve important problems for industry and academia.
我們召集了成千上萬的專家，正為工業(yè)和學(xué)術(shù)界尋找重要問題的答案。
This gives us a unique perspective on what machines can do,
因此，我們可以從獨(dú)特的視角來觀察，
what they can't do and what jobs they might automate or threaten.
機(jī)器可以做什么，不可以做什么，哪些工作可以被自動化或受到威脅。
Machine learning started making its way into industry in the early '90s.
機(jī)器學(xué)習(xí)是在90年代初進(jìn)入人們的視野。
It started with relatively simple tasks.
一開始，它只是執(zhí)行一些相對簡單的任務(wù)。
It started with things like assessing credit risk from loan applications,
像評估貸款申請的信用風(fēng)險(xiǎn)，
sorting the mail by reading handwritten characters from zip codes.
通過識別手寫的郵政編碼來檢索郵件。
Over the past few years, we have made dramatic breakthroughs.
在過去幾年里，我們?nèi)〉昧送黄菩赃M(jìn)展。
Machine learning is now capable of far, far more complex tasks.
現(xiàn)在，機(jī)器學(xué)習(xí)可以完成非常復(fù)雜的任務(wù)。
