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In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays.
2012年，Kaggle給當地學校出了個難題，設計一個算法來評判高中作文。
The winning algorithms were able to match the grades given by human teachers.
獲勝的算法給出的分數居然和真正老師給出的分數相符。
Last year, we issued an even more difficult challenge.
去年，我們出了一道更難的題。
Can you take images of the eye and diagnose an eye disease called diabetic retinopathy?
你能從拍攝出的眼睛圖像中診斷出糖尿病性視網膜病變嗎？
Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists.
再一次，獲勝的演算法給出的診斷和眼科醫生的診斷相符。
Now, given the right data, machines are going to outperform humans at tasks like this.
類似于這樣的任務，只要給定正確的數據，機器將完全超越人類。
A teacher might read 10,000 essays over a 40-year career.
一位老師在40年的職業生涯中可能審閱一萬篇作文。
An ophthalmologist might see 50,000 eyes.
一名眼科醫生大概可以檢查5萬只眼睛。
A machine can read millions of essays or see millions of eyes within minutes.
但在短短幾分鐘之內，機器可以審閱百萬篇文章或檢查數百萬只眼睛。
We have no chance of competing against machines on frequent, high-volume tasks.
對于頻繁、大批量的任務，我們無法與機器抗衡。
But there are things we can do that machines can't do.
但有些事情機器卻無能為力。
Where machines have made very little progress is in tackling novel situations.
機器在解決新情況方面進展甚微。
They can't handle things they haven't seen many times before.
它們還不能處理未曾反復接觸的事情。
The fundamental limitations of machine learning is that it needs to learn from large volumes of past data.
機器學習致命的局限性在于它需要從大量已知的數據中總結經驗。
Now, humans don't.
人類則不然。
We have the ability to connect seemingly disparate threads to solve problems we've never seen before.
我們有一種能把看似毫不相關的事物聯系起來的能力，從而解決從未見過的問題
Percy Spencer was a physicist working on radar during World War II,
Percy Spencer是一個物理學家，在二戰期間從事雷達的研究工作，
when he noticed the magnetron was melting his chocolate bar.
他注意到磁控管融化了他的巧克力。
He was able to connect his understanding of electromagnetic radiation
他從對電磁輻射的理解
with his knowledge of cooking in order to invent -- any guesses? -- the microwave oven.
聯想到烹飪，因此發明了——猜猜是什么？微波爐。
