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A Comparison of Human and Computer Information Processing

A Comparison of Human and Computer Information Processing
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Author(s): Brian Whitworth (Massey University, New Zealand)and Hokyoung Ryu (Massey University, New Zealand)
Copyright: 2012
Pages: 12
Source title: Machine Learning: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-60960-818-7.ch101

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Abstract

Over 30 years ago, TV shows from The Jetsons to Star Trek suggested that by the millennium’s end computers would read, talk, recognize, walk, converse, think, and maybe even feel. People do these things easily, so how hard could it be? However, in general we still don’t talk to our computers, cars, or houses, and they still don’t talk to us. The Roomba, a successful household robot, is a functional flat round machine that neither talks to nor recognizes its owner. Its “smart” programming tries mainly to stop it getting “stuck,” which it still frequently does, either by getting jammed somewhere or tangling in things like carpet tassels. The idea that computers are incredibly clever is changing, as when computers enter human specialties like conversation, many people find them more stupid than smart, as any “conversation” with a computer help can illustrate. Computers do easily do calculation tasks that people find hard, but the opposite also applies, for example, people quickly recognize familiar faces but computers still cannot recognize known terrorist faces at airport check-ins. Apparently minor variations, like lighting, facial angle, or expression, accessories like glasses or hat, upset them. Figure 1 shows a Letraset page, which any small child would easily recognize as letter “As” but computers find this extremely difficult. People find such visual tasks easy, so few in artificial intelligence (AI) appreciated the difficulties of computer-vision at first. Initial advances were rapid, but AI has struck a 99% barrier, for example, computer voice recognition is 99% accurate but one error per 100 words is unacceptable. There are no computer controlled “auto-drive” cars because 99% accuracy means an accident every month or so, which is also unacceptable. In contrast, the “mean time between accidents” of competent human drivers is years not months, and good drivers go 10+ years without accidents. Other problems easy for most people but hard for computers are language translation, speech recognition, problem solving, social interaction, and spatial coordination.

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