This paper proposes a new machine learning model-interactive machine learning. The ability to build this learning model is largely driven by advances in machine learning. However, more and more researchers are aware of the importance of studying the users of these systems. In this paper, the author promotes this method and demonstrates how it can lead to a better user experience and a more effective learning system. After exploring many examples, the authors also reached the following conclusions:
- This machine learning mode is different from the traditional machine learning mode. Because the user participates, the interaction cycle is faster than the traditional machine learning cycle, which increases the possibility of interaction between the user and the machine.
- Researching users is the key to advancing research in this area. Knowing the user can better design the system and better respond to people.
- It is unneccesarry to be restrict learning system and the user, because it will lead the interaction process more transparent and produce better results.
First of all, from the text, we know that models in interactive machine learning are updated faster and more concentrated. This is because the user checks interactively and adjusts subsequent inputs. Due to these fast interaction cycles, even users with little or no machine learning expertise can guide machine learning through low-cost trial and error or specialized experiments on input and output. This also shows that the foundation of interactive machine learning is fast, centralized and incremental interaction cycles. And these cycles will help users participate in the process of machine learning. These cycles also lead to tight coupling between users and the system, making it impossible to study the system in isolation. Therefore, we found that in the new system, the machine and the user interact with each other, and in my opinion, in the future, there will be more and more research on the user, and people will eventually pay more attention to the user, because the user experience can ultimately determine the quality of a product, and for this system, the user can influence the machine learning, and the feedback from the machine to the user can ultimately determine the quality of the learning process.
Secondly, the paper mentions that a common language across diverse fields should be developed, which coincides with last week’s paper “Affordance-based framework for human-computer collaboration”, although the domains mentioned are different, and this paper proposes is later, but I think this reflects a same idea, we should establish a common language, for example, in the process of interactive machine learning, there are many ways to analyze and describe the various interactions between humans and machine learners. Therefore, there is an important opportunity to bring together and adopt a common language in these areas to help accelerate research and development in this area, but also in other areas. In this way, in the process of cross-disciplinary integration, we will also have new discoveries and have new impacts.
Questions:
1.Do you think that frequent interactions must have a positive impact on machine learning?
2.For beginners in machine learning, do you think this interactive machine learning is beneficial?
3.In machine learning, which one have a significant impact on the learning result, human or the model’s efficiency.
It depends whether interactions will bring positive impacts to the results. If the interactions are based on practical situations and are proved beneficial, it is for sure these interactions will improve the results. However, if the interactions are not under comprehensive consideration, the results may suffer from overfitting or some other problems in machine learning.
I would like to make a comment on your first question. As mentioned in the paper, the interaction between humans and interactive machine-learning could influence each other. Frequent interaction could improve the accuracy of learning results, but also enable users to have a reflection on their own, just as the example of the user who playing the instrument. In addition, I am not very sure about the positive impact on machine learning, but I think frequent interactions would make users feel like they have more control and more feedback from systems. Thus they would proactively interact with systems more. This may lead to more accurate learning results.