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Summary of the Reading
The main goal of this paper is to make image segmentation more efficient. Image segmentation as it is now, requires humans to help with the process. there are just some images that machines cannot segment on their own. However, there are many cases where an image segmentation algorithm can do all of the work on its own. This presents a problem: we do not know when we can use an algorithm and when we have to use a human, so we have to have humans review all of the segmentations. This is highly inefficient. This paper tries to solve this problem by introducing an algorithm that can decide when a human is required to segment an image. The process described in the paper involves scoring each segmented image done by machines, then giving humans the task of reviewing the lowest scoring images. Overall, the process was very effective and saved a lot of human effort.
Reflections and Connections
I think that this paper gives a great example of how humans and machines should interact, especially when it comes to humans and AIs interacting. Often times, we set out in research with the goal of creating a completely automated process that throws the human away and tries to create an AI or some other kind of machine that will do all of the work. This is often a very bad solution. AIs as they currently are, are not good enough to do most complex tasks all by themselves. In the cases of tasks like image segmentation, this is an especially big issue. These tasks are very easy for humans to do and very hard for AIs to do. So, it is good to see a researcher who is willing to use human strengths to make up for the weaknesses of machines. I think it is a good thing to have both things working together.
This paper also gives us some very important research, trying to answer the question of when we should machines and when we should use humans. This is a very tough question and it comes up in a lot of different fields. Humans are expensive, but machines are often imperfect. It can be very hard to decide when you should use one or the other. This paper does a great job of answering this question for image segmentation and I would love to see more similar research in other fields explain when it is best to use humans and machines in those fields.
While I like this paper, I do also worry that it is simply moving the problem, rather than actually solving it. Now, instead of needing to improve a segmentation algorithm, we need to improve the scoring algorithm for the segmentations. Have we really improved the solution or have we just moved the area that now needs further improvement?
Questions
- How could this kind of technology be used in other fields? How can we more efficiently use human and machine strengths together?
- In general, when do you think it is appropriate to create a system like this? When should we not fully rely on AI or machines?
- Did this paper just move the problem, or do you think that this method is better than just creating a better image segmentation algorithm?
- Does creating systems like this stifle innovation on the main problem?
- Do you think machines will one day be good enough to segment images with no human input? How far off do you think that is?
Interesting reflections! I particularly liked the one where you talk about whether the paper is moving the focus of the problem rather than solving it. I feel that this paper focusses on reducing human involvement whenever feasible and this does not necessarily take away from the main problem. I actually think that the findings of this paper could be used to solve the main problem: the paper identifies lower quality images and employ humans to review them. These low-quality images can be taken as input to another system that analyses why the main algorithm failed in those cases and can improve the algorithm iteratively.