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Description
Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
ContributorsVerma, Ria (Author) / Goegan, Brian (Thesis director) / Moore, James (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This project did a deep dive on AI, business applications for AI and then my team and I built an AI model to better understand shipping patterns and inefficiencies of different porting regions.
ContributorsFreudenberger, Evan Martin (Author) / Wiedmer, Robert (Thesis director) / Duarte, Brett (Committee member) / Thunderbird School of Global Management (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this thesis is to imagine and predict the ways in which humans will utilize technology to feed the world population in the 21st century, in spite of significant challenges we have not faced before. This project will first thoroughly identify and explain the most pressing challenges the future will bring in climate change and population growth; both projected to worsen as time goes on. To guide the prediction of how technology will impact the 21st century, a theoretical framework will be established, based upon the green revolution of the 20th century. The theoretical framework will summarize this important historical event, and analyze current thought concerning the socio-economic impacts of the agricultural technologies introduced during this time. Special attention will be paid to the unequal disbursement of benefits of this green revolution, and particularly how it affected small rural farmers. Analysis of the technologies introduced during the green revolution will be used to predict how 21st century technologies will further shape the agricultural sector. Then, the world’s current food crisis will be compared to the crisis that preceded the green revolution. A “second green revolution” is predicted, and the agricultural/economic impact of these advances is theorized based upon analysis of farming advances in the 20th century.
ContributorsWilson, Joshua J (Author) / Strumsky, Deborah (Thesis director) / Benjamin, Victor (Committee member) / Department of Supply Chain Management (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05