Authors: Lydia B. Chilton, Savvas Petridis, and Maneesh Agrawala.
Summary
This paper discusses the concept of visual blends, which are an advanced design technique to draw attention to a message. This paper presents VisiBlends, a workflow for creating visual blends, by decomposing the process into computational techniques and human micro-tasks. Users can collaboratively create visual blends with steps involving brainstorming, synthesis and iteration. An evaluation of the workflow showed that it improved novices’ ability to create visual blends, and it works well for both decentralized and co-located groups.
Reflection
Stemming from a poor design sense personally, I appreciated how this paper read and what it has to contribute a lot. Creativity is a complex topic, and designing computational tools to support it can get really tricky. This paper was easy to read, and very well supported by figures for helping readers understand not only the concept of Visual Blends, but also the findings. VisiBlends opens up new possibilities of how tools can extend support to other design applications such as web design. (As per my knowledge, there are AI engines for generating web design templates, color schemes, etc. but I am not aware of user studies for these AI solutions)
This paper echoes something that we have read in a lot of papers — decomposing big tasks into smaller, meaningful chunks. However, decomposing creative tasks can become tricky. The steps were simple enough for onboarding novice users and the algorithm was intuitive. This human-AI collaboration seemed a bit unique to me particularly because the success of the whole endeavor also depended on how well the user understood how the algorithm works. This is a stark contrast to the black-box vision of algorithms. Will it become difficult as the algorithm gets more complex to support more complex blends?
All of the findings supported the usefulness of VisiBlend approach. However, I wonder if there’s a possibility of the task or the concept of visual blends (ticking off the checklist of requirements) being complex enough to understand in the first attempt. I am sure the training process, which they stress to be important, was thorough and comprehensive. But, at the end of the day, it boils down to learning from experience. I feel one would understand the requirements of visual blends better through the tool and may face difficulty in the control condition.
I really liked how the participants iterated in different ways to improve upon the initial visual blend. This is a great demonstration of human-machine collaboration, where people use machine suggestions to refine the original parameters and improve the whole process. I am also glad they addressed the issue of gender stereotypes for the “Women in CS” design task as that was something on my mind as well.
Questions
- What do you feel about decomposing creative tasks? What are the challenges? Do you think it’s always possible?
- Do you think users should always have a good sense of how the algorithm works? When do you think it’s necessary?
- What changes are necessary for this to scale up to more complex designs? How would a complex algorithm affect the whole process?
