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04/29/2020 – Dylan Finch – IdeaHound: Improving Large-scale Collaborative Ideation with Crowd-powered Real-time Semantic Modeling

Dylan Finch 2 Comments

Word count: 550

Summary of the Reading

This paper aims to create a system that will improve large-scale collaboration by making collaboration easier. These researchers aim to accomplish this by giving idea creators access to a new and better form of semantic modeling that would allow them to more easily see all of the ideas that have been created so far on a project. They want this new system to be nearly-real time and self-sustainable (they want to limit external labor). 

The system has 3 main components: (1) it allows users to request to see the ideas of others from a global idea map, with some of the returned ideas will be very different from each other; (2) the user can request to see ideas similar to their own idea; and (3) the user can request to see a map of ideas, that shows how they relate to each other and how similar they are. 

An evaluation was conducted and found that on average most people thought that the system was helpful. 

Reflections and Connections

I find this idea to be very interesting because it seems like if it was done correctly, it would have many applications. I took an HCI class in undergrad which covered design (and ideation) and I always found ideation to be a hard process. The system proposed here could help with many parts of the ideation process. One of the hardest parts of ideation is keeping track of all the ideas. Sometimes, it can be overwhelming to try and organize all of the ideas that a group has come up with. When you get to a certain number of ideas, you just can’t cope with the sheer volume of information. It becomes extremely hard to organize the ideas in a coherent way and ideas you see one second can become lost in another. A system like this would make dealing with all these ideas much easier. Plus, with the weight of managing ideas lifted, the contributors would be free to come up with even more ideas.

I think that when it comes to ideaton, there really is no magic bullet solution. The fact is, ideation simply involves too much information. The human brain simply cannot deal with all of the information that comes with doing ideation. So, there is no perfect solution that will make ideation easy. This system does not eliminate all of the struggles of ideation. It is still hard to keep all the ideas you need in your head and it is still hard to deal with all the relevant information. But, this system does vastly improve on older, traditional methods, like sticky notes. This system makes it much easier to find ideas you are looking for and to manage all the ideas you’ve had. That is about as good as it gets when it comes to ideation.

Questions

  1. Could this system do anything else to help make ideation easier?
  2. What part of this system would you use most when you’re doing ideation?
  3. What makes it so hard to automate all or part of the ideation process? Can we ever achieve full automation of the whole process or even parts of it beyond what this system shows?

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04/29/2020 – Dylan Finch – Accelerating Innovation Through Analogy Mining

Dylan Finch 1 Comment

Word count: 579

Summary of the Reading

This paper works to make it easier to find analogies in large, unstructured datasets that cover a variety of domains. The work has many real world applications, targeting real world examples of large, unstructured datasets, like data from the US patent office. The system works by collecting data about each entry in the dataset. This data includes the purpose and the mechanism that achieves the purpose. By collecting both of these features, it makes it easier to find analogies. 

This system was evaluated to see how well it helped with ideation by analogy. The evaluation used crowd workers to see how effective the new method is. Many workers were asked to come up with new product ideas. To help, these workers were shown example products. Some were shown products found to be similar using the system described in this paper. Others were shown products found to be similar by other means and others were shown random products. The evaluation showed that this system helped the workers to come up with better ideas than the other 2 methods.

Reflections and Connections

I think this problem is really one that spans generations. The paper brings up the troubles of trying to use a real world dataset like the one from the US patent office. In a case like this, I think that the data presents many different challenges, depending on when you look. For data from the past, there are probably inconsistencies in the formats of the data, the data may be in multiple different sources, and some of it may have been lost or changed over time. For data from the present, there is just so much of it. With so many more people and intellectual property more valuable than ever, the US patent office probably has more data about inventions than they can deal with. These represent two very different challenges that a dataset like the one from the US patent office face and they are a great reason why research like this is so sorely needed.

The idea of this paper also reminds me of an idea from last week’s papers: SOLVENT. Both papers try to make it easier for researchers to find analogies in data sources. In fact the existence of both of these papers I think helps to illustrate the need for technology like this. In fact, neither of these papers cite each other even though they are working on very similar research. Perhaps if there had been a widely available version of SOLVENT, they would have been able to find each other’s papers and build off of each other. 

I think that as these datasets get larger and larger, the need for easier ways to access things from them will become more and more important. The number of patents and papers is growing quicker than ever before and that means that it is easier than ever for valuable knowledge to be lost. We need to start implementing more ideas like this so that we don’t lose important knowledge. I hope that the existence of both of these papers helps to show others the real need for technology like this.

Questions

  1. Do you think this system is better or worse than SOLVENT?
  2. What is another real world, unstructured data source that a system like this might work well on?
  3. What are some applications for this system outside of ideation?

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04/29/2020 – Nan LI – VisiBlends: A Flexible Workflow for Visual Blends

NAN LI 2 Comments

Summary

The author in this paper introduced an advanced graphic design technique which combines two objects or concept in a novel and meaningful way in conveying a message symbolically. To achieve this, the author presents a tool, VisiBlends, a flexible hybrid system that facilitates the generating of visual blends based on an iterative design process. The author first introduces and defines the problem of vidual blends and then decomposes the process of creating visual blends into sub-task. The baseline of this iterative design process is that let users brainstorm first regarding the concept and then find certain relevant types of images. Then the user annotates images for the convenience of the system automatically detects which images to blend. Finally, users evaluate each blend and decide whether or not iterate the process. To find out whether the system could support decentralized collaboration and co-located teams generate the visual blend, and whether this system would help novices create blends efficiently, the author conducted three user studies. The study results indicate that both decentralized groups and co-located groups can generate visual blends to express their messages efficiently. Further, the system VisiBlends indeed helps novices generate visual blends.

Reflection

I really like this paper, and I even want to try the system. Create something novel out of thin air is always hard. Therefore, people are continually looking for tools that can stimulate creativity and brainstorming, hoping that these tools can inspire us. The paper we discussed last week, which presents a tool to help find analogy from papers also trying to do the same thing. This really shows the essence of creative inspiration.

It really enjoins to see the study process, especially study 2, group collaboration on blends for messages. They discovered many constraints, but they also solved these constraints cleverly, such as focusing on the images of the other concept to increase the chance of finding a blend when the image of another concept is limited. I particularly like the example of women + CS. Workers were trying to avoid gender stereotypes, even though it’s tough to think about the creative way. Thus, the author concludes that it’s hard to meet all the constraints, and we have to decide where to compromise.

The human visual system inspires me of a way to create a database of visual patterns. Since human tends to recognize an object based on its 3D shape, silhouette, depth, color, and details, we could let a group of people identify a blurred shape, which contains certain features but does not clear enough to recognize the actual object. Then we can base on participants’ visual perspectives to perceive the user perspective of the metaphor of this shape.

For the third study, there is an interesting phenomenon pointed out by the author. Participants who saw VisiBlends first then removed VisiBlends have much worse performance than a participant who did not see VisiBlends at all. This reminds me of the participant in one of the previous papers said they are afraid to be spoiled by the automatic system leads to no active thinking. So I think this might be one of the cases.

Questions

  1. Do you think this system would help you generate a visual blend? Will you use the system to help with your design?
  2. It is mentioned in the paper that sometimes we need to compromise to achieve our goals, what do you think about this perspective? Can you think about the examples of this situation?
  3. What is the essential tool for you when you are trying to do the brainstorm?
  4. For the iterative design process described in the paper, which part is the most significant for you? Which part you think could be replaced by an automatic machine.

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04/29/2020 – Myles Frantz – Visiblends: A flexible workflow for visual blends

Miles Frantz 1 Comment

Summary

Advanced graphics designs such as visual blending are a very difficult art to master. Let alone for a singular person to understand and able to create them. Utilizing different designs through various companies to ensure the widest market share and the greatest public opinion requires time, effort, and typically a team of experts to ensure quality. Aiming to ensure a lower barrier of entry for younger or smaller companies, this team created VisiBlends, a crowd sourced framework aiming to ease the burden on teams. This framework breaks down the task of visual blends into 6 different stages to ensure there is validation throughout the whole framework. Throughout the various studies the team had created to measure the results, there was a majority success stapling this usage as a potential tool to generate visual blend assistance through the whole process. 

Reflection

I think this problem domain is similar to the research problem domain in various aspects. Researchers look at the bleeding edge of technology and aim to solve new or old problems using new techniques. These techniques are usually accumulated through various studies, various other methods, or even a new method that uses existing technology to extend previous work. It is interesting to see how the process for creating a new research idea can also be applied throughout the 6 different steps within this teams framework, however with the lab mates, advisor, and committee filling in for the users being crowd sourced within the original framework. 

I do appreciate the machine learning algorithm learning to match the two different pictures (or ideas). This provides several starting places for uses while continuously learning the better matching locations. I believe this could be improved by allowing a human in the loop factor or an override however. Within this kind of work the art is continuously being improved upon in a real time scale. To keep up design artists may have a better idea of new ideas to be used and could be used as an early wave, better feeding the algorithm with new results. 

Questions

  • Visual blend problems are potentially a factor when marketing for a new item or product. Blending two (and potentially very different) objects helps to grab the attention of passersby who only give it a split second while either scrolling past the story or going past the ad. Have you had any experience in creating a visual blend for one of your products and did you use this technique to reach a specific demographic? 
  • One of the perceived problems throughout Visual Blends is the specific pairing of elements that can be related to each other. Notably within this study this was overcome by obtaining the consensus (throughout the Mechanical Turk Workers) the initial idea that came to their head with the idea. Do you think this could be alleviated by using another Machine Learning algorithm pairing with the stream of information from a social media website (accounting for the social consensus of a group)?  
  • This technique could potentially remove part of the need for specific marketing teams who specialize in visual blending. Do you think this program could become widespread or integrated with another bigger system already in use by another company? 

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

Miles Frantz 1 Comment

Summary

Creating solutions for everyday problems requires out of the box solutions. For common problems that people have already worked through this is a problem since many common solutions or work arounds have already been created. Crowd sourcing these solutions are bound to run into redundancy issues if there is no arbiter to ensure there is forward momentum. To alleviate the need for such an arbiter while ensuring there are fresh ideas generated throughout the system, the team created a public sharing platform for sharing ideas. Utilizing the crowd platform, notepads (symbolizing new ideas or small excerpts) can be retrieved from others on the same platform through a similarity ranking. 

Reflection

 I can greatly appreciate the effort the team has gone to solve this problem. From working within a team of technologically advanced peers, I have been able to collaborate and learn greatly from each of the members. Unfortunately for many teams (teams cross-located across the globe or teams working remotely similar to the Corona Virus working from home order) the kind of general idea generation is greatly limited to the technology being used. Whiteboard technology like Draw.io and Microsoft Whiteboard is great but takes extra time to be adapted and written out. A design like this hosted by a company’s platform could provide greater collaboration across teams for potential side projects or to create a better future direction for the company. 

I do appreciate how simplistic and relatively familiar the design that the team made of the platform. Utilizing a similar design of sticky notes from Windows (at least version 7) is a great way to lower the “ramp up” or time of education the user has to take to learn the system. Despite this the team reported the workers needed to use a lot of mental effort to accomplish their tasks that were given to them. It is interesting to see not only with a common design there is always the “writers block” or the mental stopping point that is common throughout many people.

Questions

  • Collaboration is a great way for ideas to be shared and improved. This is only increased when the participants have different experiences and backgrounds, helping to provide potentially new perspective onto the idea. What was your best idea collaboration you have had so far (can be from a professional environment or research environment)? 
  • Unfortunately as described earlier there are a multitude of problems and issues that may arise when teams of people work or interact remotely. These problems are only further exemplified through the recent stay at home orders across the globe due to the Corona Virus typically only permitting essential workers. What types of issues (technological or not) have you experienced working with someone across technology? 
  • Given how wide spread and with a potential spread of the Corona Virus this is predicted to last until at least August of 2020. With this it is likely people will still be hesitant to go directly back to work and even more so openly work together. Would you use this technology to help brainstorm with others in your field (or even in general)? 

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