AI is a trending word both in the academic world and in our daily life, but it still remains to be a huge blackbox that people with no science background can barely understand. Among all the AI technologies, machine learning is a method of data analysis that recognizes patterns and automates analytical model building. Machine learning is a great tool that helps researchers to deal with an incredibly large amount of data. However, Lipton and Steinhardt pointed out some trending problems present in machine learning scholarship. The four problems, including the use of mathiness and the misuse of language, that they focus on in their paper can be pertinent explanations for the misunderstanding in this field. In addition, it is noticeable that some of the papers involve a huge amount of computing resources. These researches are difficult to reproduce and verify, which has the potential to bring about the Matthew effect and the monopoly of the academic research. Questions might be asked: Is it necessary to use so many computing resources for machine learning? And how can we get meaningful results from data? Clarification is needed here in terms of the process of machine learning.
At the beginning of this semester, we read articles that give more detailed information about AI/ML from the technical perspective, which helps us go deeper into the applications that we use every day on our phone or websites. But I have to admit that it is hard for me to understand the whole procedures inside the technologies that we talked about in class. For example, I have not figured out convolutional neural networks in facial recognition technology yet. However, I realized that learning the fundamental design principles of technology equips me with the ability to identify the problems in it. For instance, bias facial recognition probably come from biased data. This brings my attention to the stage of data preparation and allows me to think more about what I can do.
All through this semester, we have discussed major ethical and political issues related to artificial intelligence including biased data, privacy, attention manipulation and the loss of human agency. In order to figure out what is going wrong with one specific technology, the first step is to understand the architecture and algorithm of this blackbox. The more clarification there is, the more we can do.
- Zachary C. Lipton and Jacob Steinhardt, “Troubling Trends in Machine Learning Scholarship,” ArXiv:1807.03341 [Cs, Stat], July 9, 2018.
- Gary Marcus, “Deep Learning: A Critical Appraisal,” ArXiv.Org, January 2, 2018.