04/29/2020 – Ziyao Wang – IdeaHound: Improving large-scale collaborative ideation with crowd-powered real-time semantic modeling

Innovation without guidance is a hard task. To help the users, the authors proposed that a map of the solution space that is being explored can inspire and direct exploration. With research on current automated approaches, they found that all the approaches cannot build adequate maps. In addition to this, all the current deployed crowdsourcing approaches require external workers to do tedious semantic judgment tasks. To resolve this problem, they presented IdeaHound. The system can seamlessly integrate semantic tasks of organizing ideas into users’ idea generation activities. They used several case studies to prove that the system can yield high-quality maps without detracting from idea generation. And the users are found willing to use the system to simultaneously generate and organize ideas, and the system can obtain more accurate models than existing approaches.

Reflections:

The idea of the paper is really interesting. Instead of hiring crowd workers to do the idea clustering, they designed an interface that can let the users write their ideas and make the users able to group the ideas by themselves. Instead of letting a group of people write the ideas and hiring another group of people to cluster the ideas, making the first group of people able to do both idea generation and idea clustering is more efficient and more accurate. According to the idea groups, the system can generate a map with all ideas locates on it. If two groups of ideas have similarities, they will position nearby. With this map, the users can be inspired and provide more ideas. Additionally, if the users write some ideas, the system will automatically recommend some ideas which have similarity with them. This can also inspire users to come up with more novel ideas. I really like the example in the authors’ presentation. When not given an inspiring map or generate ideas automatically, we may get outputs like pizzas with top of broccoli, which is hardly accepted by most of the people. This makes me aware of the importance of the system.

I think this kind of interaction should be learned by all other current applications which deployed crowdsourcing. The users of the systems are willing to do some more tasks than required if they are interesting or they think the tasks are meaningful. Humans are not hired by AIs. Instead, AIs should be the helper of humans when humans are doing some tasks. As is said, we should leverage both human strength and machine strength. In this kind of idea generation task, it is more efficient to let machines support humans to complete the tasks.

Questions:

Will you do the ideas clustering automatically when you are only asked to provide ideas while you are able to do the clustering using the system?

Can this kind of idea, letting users able to do something instead of requiring them to do something, being applied to other applications?

Do you like pizzas with top of broccoli? What is your opinion on these ideas generated by systems? What about using a system to generate ideas and let human workers select the useful ones from all the ideas?

One thought on “04/29/2020 – Ziyao Wang – IdeaHound: Improving large-scale collaborative ideation with crowd-powered real-time semantic modeling

  1. Hi, to response your first question, I am not willing to only provide ideas, because I think the ultimate goal of users to use this system is to be inspired and see some similar or new ideas. So if I am asked to provide ideas without feedback, I don’t think it will help me. And for the third question, I think this question is a little similar to the article I want to discuss about this week. I think if we want to achieve automate all ideation process, it is necessary to specify uses’ input, such as the purpose and method of this idea. In this way, there is no need for crowd source workers to understand idea, instead of it, we can directly calculate the similarity between ideas according to different structures.

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