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    自動駕駛汽車--比七個月大的嬰兒還聰明?(1)

    來源:可可英語 編輯:Vicki ?  可可英語APP下載 |  可可官方微信:ikekenet
      


    掃描二維碼進行跟讀打分訓練

    Science & technology

    科技板塊

    AI for vehicles

    自動駕駛汽車

    Is it smarter than a seven-month-old

    比七個月大的嬰兒還聰明?

    How to improve the intelligence of self-driving cars

    如何提高自動駕駛汽車的智力

    By the age of seven months, most children have learned that objects still exist even when they are out of sight.

    大多數孩子在7個月大的時候就知道,有些東西雖然看不見,但確是存在的。

    Put a toy under a blanket and a child that old will know it is still there, and that he can reach underneath the blanket to get it back.

    如果把玩具放在毯子下面,他們就會知道玩具還在那里,然后伸手把玩具從毯子下面拿出來。

    This understanding, of "object permanence", is a normal developmental milestone, as well as a basic tenet of reality.

    這種對“物體恒存性”的理解是正常發展的里程碑,也是現實的基本原則。

    It is also something that self-driving cars do not have. And that is a problem.

    這也是自動駕駛汽車所沒有的。這是一個問題。

    Autonomous vehicles are getting better, but they still don't understand the world in the way that a human being does.

    即便自動駕駛汽車越來越好,但它們仍無法像人類那樣了解世界。

    20210904_std001_副本.jpg

    For a self-driving car, a bicycle that is momentarily hidden by a passing van is a bicycle that has ceased to exist.

    對于一輛自動駕駛汽車來說,一輛被路過的貨車暫時擋住的自行車就是一輛不存在的自行車。

    This failing is basic to the now-widespread computing discipline that has arrogated to itself the slightly misleading moniker of artificial intelligence (AI).

    這一缺點是如今被廣泛應用的計算學科的基礎,該學科自詡為人工智能(AI),這個綽號有點讓人誤解。

    Current AI works by building up complex statistical models of the world, but it lacks a deeper understanding of reality.

    目前的人工智能是通過建立復雜的世界統計模型來工作的,但它缺乏對現實的更深層次的理解。

    How to give AI at least some semblance of that understanding—the reasoning ability of a seven-month-old child, perhaps—is now a matter of active research.

    如今,如何讓人工智能具備至少一些類似的理解能力(比如一個7個月大的孩子的推理能力)是一個熱門研究課題。

    Modern AI is based on the idea of machine learning.

    現代人工智能基于機器學習這一理念。

    If an engineer wants a computer to recognise a stop sign, he does not try to write thousands of lines of code that describe every pattern of pixels which could possibly indicate such a sign.

    如果工程師想讓電腦識別一個停車標志,他不會寫成千上萬行描述每一個可能表示這種符號的像素模式的代碼。

    Instead, he writes a program that can learn for itself, and then shows that program thousands of pictures of stop signs.

    相反,他會編寫一個可以自我學習的程序,然后向該程序展示數千張停車標志的圖片。

    Over many repetitions, the program gradually works out what features all of these pictures have in common.

    經過多次重復后,該程序能夠逐漸找出所有這些圖片的共同特征。

    譯文由可可原創,僅供學習交流使用,未經許可請勿轉載。

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