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Technical Perspective: Computing with the Crowd


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To view the accompanying paper, visit doi.acm.org/10.1145/2927928

Computer science is primarily focused on computation using microprocessors or CPUs. However, the recent rise in the popularity of crowdsourcing platforms, like Amazon's Mechanical Turk, provides another computational device—the crowd. Crowdsourcing is the act of outsourcing a job to an undefined group of people, known as the crowd, through an open call.3 Crowdsourcing platforms are online labor markets where employers can post jobs and workers can do jobs for pay, but they can also be viewed as distributed computational systems where the workers are the CPUs and will perform computations for pay. In other words, crowdsourcing platforms provide a way to execute computation with humans. In a traditional computational system when a programmer wants to compute something, they interact with a CPU through an API defined by an operating system. But in a crowdsourcing environment, when a programmer wants to compute something, they interact with a human through an API defined by a crowdsourcing platform.

Why might one want to do computation with humans? There are a variety of problems that are easy for humans but difficult for machines. Humans have pattern-matching skills and linguistic-recognition skills that machines have been unable to match as of yet. For example, FoldIt1 is a system where people search for the natural configuration of proteins and their results often outperform solutions computed using only machines. Conversely, there are problems that are easy for machines to solve but difficult for humans. Machines excel at computation on massive datasets since they can do the same operations repeatedly without getting tired or hungry. This brings up the natural question: What kinds of problems can be solved with both human and machine computation that neither could do alone?


 

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