This paper seeks to investigate a method to achieve AI + IA. That is, enhancing human performance using automated methods but not completely replacing it. The author takes into notice that effective automation should first off bring significant value, second be unobtrusive, third do not require precise user input, and finally, adapt. The author takes these points to account and introduces three interactive systems that he built. All these systems utilize machine computing to handle the initial or small repetitive tasks and rely on human computing to make corrections and improve quality. They are all collaborative systems where AI and humans work together to boost each other’s performance. The AI part of the system tries to predict user intentions while the human part of the system drives the work.
This paper reminded me of Smart-Built Environments (SBE), a term I learned in a Virtual Environments class. SBE is an environment where computing is seamlessly integrated into the environment and interaction with it is very natural. It is capable of “smartly” providing appropriate services to humans in a non-intrusive way. For example, a system where the light automatically lights up upon a person entering a room is a smart feature. I felt that this paper was trying to build something similar in a desktop environment. One core difference with SBEs is that SBE also tries to tackle immersion and presence (which are terms frequently used for evaluating virtual environments). I wonder if the author knows about SBEs or got his project ideas from SBEs.
While reading the paper, I wasn’t sure if the author handled the “unobtrusive” part effectively. In one of the introduced systems, Wrangler was an assist tool for preprocessing data. It tries to predict user intention upon observing certain user behavior and recommends available data transformations on a side panel. I believe this was a similar approach to mimic the Google query auto-completion feature. However, I don’t think it’ll work as well as Google’s auto-completion. Google’s auto-complete suggestions appear right below where the user is typing whereas Wrangler suggests it in the side corner. This requires the user to avert his or her eye from where the point of the previous interaction was, and this is obtrusive.
These are the questions that I had while reading the paper:
1. Do you know any other systems that try to seamlessly integrate AI and human tasks? Is that system effective? How so?
2. The author of this paper mostly uses AI to predict user intentions and process repetitive tasks. What other capabilities of AI would be available for naturally integrating with human tasks? What other tasks are hard to do by humans that machines accel at that could be integrated?
3. Do you agree that “the best kind of systems is one where the user does not even know he or she is using it?” Would there ever be a case where it is crucial that the user feels the presence of the system as a separate entity? This thought came to me because systems could (and ultimately does) fail at some point. If none of the users understand how the system works, wouldn’t that be a problem?
Good comment! I feel like you have a different understanding of this paper with me. I would like to make a comment regard your third question. I think it is a little bit exaggerated to say that the best kind of system is one where the user does not even know he or she is using it, but I think it also makes sense. Because I think the property of IA is to provide users with strong support through subtle design. I agree with your point that for some features it is necessary for users to know the presence of the function, but this doesn’t represent this user knowledge would interfere with their original work. And this is what they talked about “The user does not even know he or she is using it”, instead of they totally don’t know the exists of the functions. My understanding might be wrong, but this is my opinion about what the goal of IA should be like.