Summary:
The author explains the research going on in the visual analytics area to collaborate the work between humans and computers. While there have been multiple promising examples between human-computer collaboration but there are no proper solutions to the following questions
- How can we tell if a problem will benefit from collaboration?
- How do we decide which tasks to delegate to which party and when?
- How can one system compare to others trying to solve the same problem?
The author tries to answer to the above questions in the paper. While the author explains the uses of visual analytics that exist in the present world and their usages. The author then explains the different kinds of affordances that exist in the visual analytical human computer collaboration and explains each affordance in detail further.
Reflections:
It was interesting to learn that visual analytics can be seen as a human computer collaboration. For analytics on large data we need larger computation power to visualize the analytical data. It was interesting to learn about the white box human computer collaboration and black box human computer collaboration. The visual analytics help people with no expertise in the are to provide inputs to the problem.
Questions:
- How can we be sure that one visualization is the correct solution for a particular problem?
- The people who are developing the visualization tools are the humans, will the area of expertise of the people developing the tool affect the results?
- Is visual analytics solution is better than normal machine learning solution to solve the problem?
