Examples of using Human computation in English and their translations into Thai
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Human computation Section 5.2.
Table 5.1: Examples of human computation projects in social research.
Human computation projects take a big problem;
Galaxy Zoo shows the evolution of many human computation projects.
Human computation enables you to have a thousand research assistants.
By this definition FoldIt-which I described in the section on open calls-could be considered a human computation project.
Human computation projects combine the work of many non-experts to solve easy-task-big-scale problems.
It is these easy-for-people yet hard-for-computers micro-tasks that we can turn over to human computation projects.
Second generation human computation systems also use machine learning in order to amplify the human effort.
Coding political manifestos, something typically done by experts, can be performed by a human computation project resulting in greater reproducibility and flexibility.
Computer-assisted human computation systems also use machine learning in order to amplify the human effort.
Kenneth Benoit and colleagues(2016) decided to take the manifesto coding task that had previously been performed by experts and turn it into a human computation project.
In addition to creating human computation and open calls, researchers can also create a distributed data collection project.
Kenneth Benoit and colleagues(2015) decided to take the manifesto coding task that had previously been performed by experts and turn it into a human computation project.
Finally, the examples in this section show that human computation can have a democratizing impact on science.
Human computation projects combine the work of many non-experts to solve easy-task-big-scale problems that are not easily solved by computers.
For an excellent book length treatment of human computation, in the most general sense of the term, see Law and Ahn 2011.
Human computation projects combine the efforts of many people working on simple micro-tasks in order to solve problems that are impossibly big for one person.
You might have a research problem suitable for human computation if you have ever thought: I could solve this problem if I had a thousand research assistants.
Human computation projects combine the work of many non-experts to solve easy-task-big-scale problems that are not easily solved by computers.
The term“split-apply-combine” was used by Wickham(2011) to describe a strategy for statistical computing, but it perfectly captures the process of many human computation projects. The split-apply-combine strategy is similar to the MapReduce framework developed at Google Dean and Ghemawat 2004; Dean and Ghemawat 2008.
In human computation and distributed data collection projects, moreover, the best form of quality control comes through redundancy, not through a high bar for participation.
According to the definition proposed in Ahn(2005) Foldit-which I described in the section on open calls-could be considered a human computation project. However, I choose to categorize Foldit as an open call because it requires specialized skills(although not necessarily formal training) and it takes the best solution contributed, rather than using a split-apply-combine strategy.
In order to assess the quality of the crowd coding, Benoit and colleagues also had about 10 experts-professors and graduate students in political science-rate the same manifestos using a similar procedure. Although the ratings from members of the crowd were more variable than the ratings from the experts, the consensus crowd rating had remarkable agreement with the consensus expert rating(figure 5.6). This comparison shows that, as with Galaxy Zoo, human computation projects can produce high-quality results.
In addition to creating human computation and open call projects, researchers can also create distributed data collection projects.
In order to assess the quality of the crowd coding, Benoit and colleagues also had about 10 experts-professors and graduate students in Political Science-rate the same manifestos using a similar procedure. Although the ratings from members of the crowd were more variable than the ratings from the experts, the consensus crowd rating had remarkable agreement with the consensus expert rating(Figure 5.6). This comparison shows that, as with Galaxy Zoo, human computation projects can produce high quality results.