Crystal Fung Instagram – 速讀還可以跟Nvidia學習💪🏻 要更生動解釋「喬治速讀法」， 我在《晶片戰爭》(Chip War)這本書裡找到一個好例子： 英偉達(Nvidia)的圖形處理器（GPUs） “In the early 2010s, Nvidia-the designer of graphic chips-began hearing rumors of PhD students at Stanford using Nvidia’s graphics processing units(GPUs) for something other than graphics. GPUs were designed to work differently from standard Intel or AMD CPUs, which are infinitely flexible but run all their calculations one after the other. GPUs, by contrast, are designed to run multiple iterations of the same calculation at once. This type of “parallel processing,” it soon became clear, had uses beyond controlling pixels of images in computer games. It could also train AI systems efficiently. Where a CPU would feed an algorithm many pieces of data, one after the other, a GPU could process multiple pieces of data simultaneously. To learn to recognize images of cats, a CPU would process pixel after pixel, while a GPU could “look” at many pixels at once. So the time needed to train a computer to recognize cats decreased dramatically.” 「在2010年代初期，Nvidia，一家圖像芯片的設計者，開始聽到關於斯坦福大學的博士生們使用Nvidia的圖形處理單元（GPUs）進行一些非圖形處理的傳言。與標準的Intel或AMD的中央處理單元（CPUs）設計不同，它們無比靈活但是需要依次執行所有計算，GPUs則設計用於同時運行多個相同的計算。很快就顯而易見，這種「並行處理」不僅可以用於控制電腦遊戲中的圖像像素，還能高效地訓練人工智能系統。當CPU需要一個接一個地向演算法提供許多數據時，GPU卻可以同時處理多份數據。以識別貓的圖像為例，CPU需要逐個像素處理，而GPU可以「同時」觀察許多像素。因此，訓練電腦識別貓的圖像所需的時間大幅度減少。」 我好像無意之中發現了「喬治速讀法」與Nvidia的GPU之間的異曲同工之妙？😯 有沒有這方面的專家可以進一步解釋一下？ 歡迎大家發表想法🙆🏼‍♀️