Summary
The paper discusses the fact that computer-aided products should be considered to be an enhancement of human work rather than it being a replacement. The paper emphasizes that technology, on its own, is not always full proof and that humans, at times, tend to rely completely on technology. In fact, AI in itself can yield faulty results due to biases in the training data, lack of enough data, among other factors. The authors point out how the coupling of human and machine efforts can be done successfully through some examples of autocompleting of google search and grammar/spelling correction. The paper aimed to use AI techniques but in a manner that makes sure that humans remain the primary controller. The authors considered 3 case studies, namely data wrangling, data visualization for exploratory analysis, and natural language translation, to demonstrate how shared representations perform. In each case, the models were designed to be human-centric and to have automated reasoning enabled.
Reflection
I agree with the authors’ statement about data wrangling that most of the time is spent in cleaning and preparing the data than actually interpreting or applying the task one specializes in. I was amused by the idea that users’ work of transforming the data is cut short and aided by the system that suggests users the proper action to take. I believe this would indeed help the users of the system if they get the desired options directly recommended to them. If not, it will help improve the machine further. I particularly found it interesting to see that users preferred to maintain control. This makes sense because, as humans, we have an intense desire to control.
The paper never explains clearly who the participants of the system are. This would be essential to know who the users were exactly and how specialized they are in the field they are working on. It would also give an in-depth idea about the experience they had interacting with the system, and thus I feel the evaluation would be complete.
The paper’s overall concept is sound. It is indeed necessary to have a seamless interaction between man and the machine. They have mentioned three case studies. However, all of them are data-oriented. It would be interesting to see how the work can be extended to other forms – videos, images. Facebook picture tagging, for example, does this task to some extent. It suggests users with the “probable” name(s) of the person in the picture. This work can also be used to help detect fake vs. real images or if the video has been tampered.
Questions
- How are you incorporating the notion of intelligent augmentation in your class project?
- Case studies are varied but mainly data-oriented. How would this work differ if it was to imply images?
- The paper mentions “participants” and how they provided feedback etc. However, I am curious to know how they were selected? Particularly, the criteria that were used to select users to test the system.
In answer to your second question, I think that this research would be very similar and produce similar results if it was applied to images. I think that a system like this applied to images would look very similar to the systems they describe based on data visualization, because both deal with images to a certain extent. If it were applied to image editing, it could work like editing suggestions for text editors, where the user would start editing the photo and then the system would suggest further edits or small corrections to the edits that the human made. A system like this could also be applied to image categorization. If there was a set number of categories, a human and the suggestion AI could work together, with the AI suggesting tags for each image and the human deciding whether or not to accept them.
I agree with your point that the authors should have been more transparent about who the participants were and how they were selected. Maybe the notion of ‘control’ would differ if the participants are more varied.