Machine Learning & Search Modeling; Analyzing the Environmental and Societal Effects of Bitcoin
Drissi, Noor, School of Engineering and Applied Science, University of Virginia
Riggs, Robert, University of Virginia
Laugelli, Benjamin, University of Virginia
JACQUES, RICHARD, EN-Engineering and Society, University of Virginia
My technical work and explorative STS research are not immediately connected at this point within society. However, as these topics become more widespread in society the connection between the two will be robust. My technical work involves the use of machine learning artificial intelligence to model search queries. My STS research details the environmental and social impacts of Bitcoin. Although these two topics are not currently related as society progresses a bridge will be constructed between the two. I chose my STS research topic because I am aware that machine learning will become more widespread within the Bitcoin network in the future.
The main topic of my technical work revolves around machine learning and search modeling. My technical work addresses the current state of machine learning search modeling algorithms and how they can be improved to promote a higher success rate for search queries. Over the summer I worked with the Search Modeling team at McMaster-Carr working on machine learning algorithms to help direct customers' incorrect search arguments to their desired product. Our work involved the use of customer search arguments, terms that customers use to land at a desired product or supply, as well as synthetic search arguments, terms generated by a machine learning model that mimic those of customers. These search arguments were then used to train models until a certain success rate threshold was achieved. The implementation of these algorithms reduces customer purchasing error in the long run and creates a better online shopping experience. My technical work will hopefully aid in improving the other machine learning technical components within McMaster-Carr.
My STS research examines the environmental and social aspects of Bitcoin. I begin by introducing the amount of energy consumed by Bitcoin mining every day and the adverse effects the world will see if solutions are not implemented. I then dive deeper into the carbon footprint produced by Bitcoin and how it scales up against different countries worldwide. After this, I begin to explain the social effects of Bitcoin and how its learning curve will harm those who are not skilled with technology. This research highlights that Bitcoin, although its rise in popularity, must be regulated so it does not continue to harm the environment or societies shaped around it.
Working on both of my projects simultaneously added significant value to each other. By examining both the technical and social aspects of machine learning and Bitcoin, enabled me to formulate potential solutions for both industries that can come together to supplement each other in the future. By working on my technical project, I was made aware that machine learning will become increasingly popular as artificial intelligence becomes more prominent within society. Through my work, I realize the importance of accounting for social impacts while working on any project no matter the technicality.
Through this process, I would like to extend thanks to my technical advisor, Professor Robert Riggs, as well as Professor Richard Jacques. Both professors provided me with incredible support and advice while crafting my thesis portfolio.
BS (Bachelor of Science)
Bitcoin, Bitcoin Energy Usage, Machine Learning, Search Algorithms
School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: Robert Riggs
STS Advisor: Richard Jacques, Benjamin Laugelli