Summary
In this paper, the authors introduce Discovery Space, an extension to Adobe Photoshop that suggests high-level macro actions based on visual features. Complex platforms such as Photoshop are great tools to aid creativity. However, their features are rather complex for beginners making for a steep learning curve. DiscoverySpace utilizes one-click actions suggested in the online community and makes these macro actions available to new users thus softening their introduction to the software.
For the experiment, the authors maintain two independent control groups. One group has access to the DiscoverySpace panel in Photoshop and the other only has the basic tool. Experiments show that DiscoverySpace helps beginners by suggesting initial macro actions. Subjects in the no tool group were frustrated with the tool’s complex features producing worse results than the subjects in DiscoverySpace group. Also, the authors suggest that some steps in the process can be replaced by advances in AI algorithms in the future which will lead to faster processes.
Reflection
The paper is really interesting in its approach to reduce the system’s complexity by integrating macro action suggestions. The framework is very generalizable and can work in a multiple number of complex softwares such as Excel sheets (to help with common macro functions), Powerpoint presentations (to apply popular transitions or slide formats) and AI frameworks (pre-building popular networks). Another important aspect is that such technologies are already being applied in several places. Voice assistants have specific suggestions to introduce users to common tasks such as setting up alarms, checking weather, etc.
However, the study group is relatively very small. I do not understand the reason for this. The tasks could be put into an MTurk type format and given to several users. Given the length of the task (~30 min), the authors could potentially use train and work platforms such as Upwork too. Hence, I believe the conclusions of the paper are very specific to the subjects. Also, the authors suggest the potential of integrating AI systems to their framework. I think it would help if more examples were given for such integrations.
Also, utilizing DiscoverySpace-like mechanisms draws in more users. This provides a monetary-incentive to businesses to invest in more such ideas. One example can be the paper-clip assistant in initial versions of Windows that introduced users to the operating system.
Questions
- I believe machine learning frameworks like tensorflow and pytorch have examples to introduce themselves to beginners. They could benefit from a DiscoverySpace-like action suggestion mechanism. Can you give some examples of softwares in your research area that could benefit from such frameworks?
- I believe the limited number of subjects is a huge drawback to trust in the conclusions of the paper. Can you provide some suggestions on how the experiments could be scaled to utilize more workers at a limited cost?
- The authors provide the example of using advances in image analysis to replace a part of DiscoverySpace. Can you think of some other frameworks that have replaceable parts? Should we develop more architectures based on this idea that they can be replaced by advances in AI?
- Give some examples of systems that already utilize DiscoverySpace-like framework to draw in more users?
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