Summary
In this paper, the author proposes multiple systems that can combine the power of both artificial inttelgence and human computation and overcome each one weakness. The author thinks that automating all tasks can lead to a poor results as human component is needed to review and revise results get the best results. The author the autocomplete and spell checkers examples to show that artificial intelligence can offer suggestion and then human can review or revise these suggestions or dismiss the suggestions. The author propose different systems that uses predictive interaction that help users on their tasks that can be partially automated to help the users to focus more on the things that they care more about. One of these systems called Data Wrangling that can used by data analyst on the data preprocessing to help them with cleaning up the data to save more than %80 of their work. The users will need to setup some data mapping and can accept or reject the suggestions. The author proposed project called Voyager that can help with data visualization for exploratory analysis which can be used to help with suggesting visualization elements. The author suggests using AI to automate repeated task and offer the best suggestions and recommendations and let the human decide whether to accept or reject the recommendations. This kind of interaction can improve both machine learning results and human interaction.
Reflection
I found the material presented in the paper to be very interesting. Many discussions were about whether machine can replace human or not was addressed in this paper. The author mentioned that machine can do well with the help of human and the human in the loop will always be necessary.
I also like the idea of the Data Wrangling system as many data analysts and developer spend considerable time on cleaning up the data and most of the steps are repeated regardless of the type of data, and automating these steps will help a lot of people to do more effective work and to focus more on the problem that they are trying to solve rather than spending time on things that are not directly related to the problem.
I agree with author that human will always be in the loop especially on systems that will be used by humans. Advances in AI need human on annotating or labeling the data to work effectively and also to measure and evaluate the results.
Questions
- The author mentioned that the Data Wrangler system can be used by data analysts to help with data preprocessing, do you think that this system can also be used by data scientist since most machine learning and deep learning projects require data cleanup ?
- Can you give other examples of AI-Infused interactive systems that can help different domains and can be deployed into production environment to be used by large number of users and can scale well with increased load and demands?
It would be interesting to see Data Wrangler being used by data analysts in the real-world to help with the data cleanup. The interactivity might help depending on the kind of task the end-user is aiming to solve.