Summary of the Reading
Interactive machine learning is another form of machine learning that allows for much more precise and continuous changes to the model, rather than large updates that drastically change the model. In interactive machine learning models, domain experts are able to continuously update the model as it produces results, reacting to the predictions it makes in almost real time. Examples of this type of machine learning system include online recommender systems like those on Amazon and Netflix.
In order for this type of system to work, there needs to be an oracle who can correctly label data. Usually this is a person. However, people do not like being an oracle and in some cases, they can be quite bad at it.Humans would also like richer more rewarding interactions with the machine learning algorithms. The paper suggests some way that these interactions could be made richer for the person training the model.
Reflections and Connections
At the end of the paper, the authors say that these new types of interaction with interactive machine learning is a potentially powerful tool that needs to be applied to the right circumstances. I completely agree. I think that this technology, like all technologies, will be useful in some places and not in others. I think that in cases of a simple recommender system, most people are happy to just give a rating every now and then or answer a survey question every now and then. In cases like this, I think that richer interactions would take away from the simplicity and usefulness of the system. But in other cases, it would be nice to be able to kind of work with the machine learning model to generate better answers in the future.
I also think that in some fields, technologies like the ones presented in his paper will be extremely valuable. I think that in life, it is very easy to get stuck in a rut and to not be able to think outside of the ways that we have always done things. But, it is important to do that to push technology forward. We have always thought of machine learning as an algorithm asking an oracle about specific examples. When we create interactive machine learning, we replaced the oracle with a person and applied the same ideas. But, as this paper points out, people are not oracles and they don’t like to be treated like them. So the ideas in this paper could be very impromat to unlock new ways of using machine learning in conjunction with people. And, the more we play to the strengths of people, we will be able to create better machine learning algorithms that take advantage of those strengths.
Questions
- What is one place you think could use interactive machine learning besides recommender systems?
- Which of the presented models for new ways for people to interact with machine learning algorithms do you think has the most promise?
- Can you think of any other new interfaces for interactive machine learning not mentioned in the paper?
Hello!
I resonate with your reflections, particularly, the one stating that technology will be useful in some places and not in others. I liked the examples you had given to explain this. Regarding the second question, I found the concept of enabling users to query the learner extremely interesting. In cases where the learner has classified something incorrectly, this feature gives users the opportunity to understand why this misclassification happened. This facilitates correcting the learner at a fundamental level and ensures that this mistake does not repeat in the future!