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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)
人工智慧能教我們什麼關於心智的知識?如果人工智慧的基本概念是思考是一種計算過程,那麼計算如何能夠照亮思考?這是一個及時的問題。人工智慧正如火如荼,而最受關注的人工智慧話題圍繞著自適應機器學習:從大量數據中學習智能行為的計算機系統。這就是無人駕駛汽車的運作原理。例如,在這本書中,Hector Levesque 將討論重心轉向「傳統的人工智慧」,這種人工智慧並不是基於大量數據,而是基於對常識智能的理解。這種人工智慧能夠處理與以往模式不同的情況——就像我們在現實生活中一樣,當我們遇到一座被沖毀的橋或當咖啡師告訴我們沒有豆漿時。
Levesque 考慮了語言在學習中的角色。他主張,一個通過著名的圖靈測試的計算機程序可能是一個無意識的僵屍,他提出了另一種測試智能的方法——Winograd Schema Test,這是由 Levesque 和他的同事們開發的。「如果我們的目標是理解智能行為,我們最好理解製造與偽造之間的區別,」他觀察到。他確定了常識背後的一個可能機制,以及調用背景知識的能力:以符號方式表達思考對象的能力。隨著人工智慧越來越多地融入日常生活,我們應該擔心那些缺乏常識的系統在需要常識的情況下做出決策。