[TED] 聽Kaggle創始人講講這些年會被機器"搶"走的工作

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Lessons Learned

人工智慧的發展會威脅到人類嗎？

在工作領域而言，答案看起來是的。2013年牛津大學的研究者們得出結論：每兩份工作中幾乎就有一份有很高的風險會被機器自動化取代。人工智慧最有影響力的分支--機器學習領域的前沿工作者，Kaggle公司創始人，Anthony Goldbloom 分享了他的觀點:

• 機器的優勢領域：頻繁性的、大規模的任務量 ->此優勢需要通過學習大量已有的、正確的數據作為保障
• 機器的不足：創造性的、新穎的方面

因此對於未來的工作而言，人類是否可能被威脅/取代取決於一個簡單的問題：這份工作繁雜反覆的比重佔多少，需要發揮創造力的部分又佔多少？

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?

-- Anthony Goldbloom

Anthony認為，機器學習發展之勢迅猛。從最初只用來簡單的評估貸款申請風險、分類信件郵編，到如今Kaggle上已經成功地發展出了高中作文評分、糖尿病患者視網膜病變診斷的演算法，而未來機器可能用於會計審計、閱讀法律合同模板方面[作者有話要說：前兩天跟PwC Assurance Innovation團隊聊了一下，他們現在已經在很大程度地自動化審計流程，這個未來已經在發生了...]，但會計師和律師仍需要負責複雜的稅務和沒有先例的訴訟等。

總之，

Whatever you decide to do, let every day bring you a new challenge.

Vocabulary Builder

• disruption: n. disturbance or problems that interrupt an event, activity or process -> 造成了困擾

e.g. They concluded that almost one in every two jobs have a high risk of being automated by machines. Machine Learning is the technology thats responsible for most of this disruption.

• give sb a (unique) perspective on sth ->告訴/啟示了某人某事

e.g. This gives us a unique perspective on what machines can do, what they cant do and what jobs they might automate or threaten.

• make dramatic breakthroughs 取得重大突破

e.g. Machine learning started making its way into industry in the early 90s... Over the past few years, we have made dramatic breakthroughs.

• have no chance of competing against sb on sth -> 在某方面跟某人比毫無勝算，在某方面被某人碾壓

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

• disparate: adj. = different

e.g. We have the ability to connect seemingly disparate threads to solve problems weve never seen before.

• boilerplate: n. rolled steel for making boilers -> 樣板

e.g. Over the coming years, theyre going to conduct our audits, and theyre going to read boilerplate from legal contracts.

• pathbreaking: adj. = innovative, pioneering ->破天荒的

e.g. Theyre going to be needed for complex tax structuring, for pathbreaking litigation.

Reference

• TED: The jobs well lose to machines - and the ones we wont by Anthony Goldbloom
• 演講細節尋幽：
• 論文 - The Future of Employment: How Susceptible are Jobs to Computerisation? by Carl Benedikt Frey, Michael A. Osborne
• New York Times - The Algorithm Didn』t Like My Essay by Randall Stross
• The Economist - Now there』s an app for that
• 拓展閱讀文章：The Economist - The return of the machinery question
• 演講人推薦書目[Book recommended by Anthony Goldbloom]:
• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
• The Second Machine Age by Erik Brynjolfsson and Andrew McAfee

標題背景Photo by Markus Spiske on Unsplash

aLittleBit on 2018/02/25

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