Common Sense, the Turing Test, and the Quest for Real AI (Hardcover)

Hector J. Levesque

  • 出版商: MIT
  • 出版日期: 2017-02-24
  • 售價: $1,060
  • 貴賓價: 9.5$1,007
  • 語言: 英文
  • 頁數: 192
  • 裝訂: Hardcover
  • ISBN: 0262036045
  • ISBN-13: 9780262036047
  • 相關分類: 人工智慧
  • 海外代購書籍(需單獨結帳)

買這商品的人也買了...

商品描述

What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It's a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to "good old fashioned artificial intelligence," which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns -- as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk.

Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence -- the Winograd Schema Test, developed by Levesque and his colleagues. "If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it," he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.

商品描述(中文翻譯)

人工智慧能教我們關於心智的什麼?如果人工智慧的基本概念是思考是一個計算過程,那麼計算如何能夠闡明思考呢?這是一個及時的問題。人工智慧正風靡一時,而最熱門的人工智慧話題圍繞著「適應性機器學習」:從大量數據中學習智能行為的電腦系統。例如,這就是無人駕駛汽車的動力所在。在這本書中,赫克托·勒韋斯克將對話轉向「老派人工智慧」,它不是基於大量數據,而是基於對常識智能的理解。這種人工智慧能夠應對偏離以往模式的情況,就像我們在現實生活中遇到一座被淹沒的橋樑或者咖啡師告訴我們沒有豆漿時一樣。

勒韋斯克考慮了語言在學習中的作用。他認為通過著名的圖靈測試的電腦程序可能是一個無意識的殭屍,並提出了另一種測試智能的方法——勒韋斯克和他的同事開發的Winograd Schema測試。他觀察到:“如果我們的目標是理解智能行為,我們最好了解製造和偽裝之間的區別。”他確定了常識和調用背景知識的可能機制:能夠符號化地表示思考對象的能力。隨著人工智慧越來越多地融入日常生活,如果沒有常識的系統在需要常識的地方做出決策,我們應該感到擔心。