BALT 4363

When I think about website design, I typically associate the process with the rather grueling undertakings I was forced to endure during the web design unit of my middle school STEM class. Since my time in middle school preceded the widespread accessibility and integration of AI (i.e., 2016-2018), web design was still done the hard way. More specifically, the web design platform used in my class was WordPress. The ease of contemporary drag-and-drop features was absent. Instead, manual file uploads were required, making the process of web design especially tedious. While WordPress had optional coding at the time, our class was required to write lines of code, which added to the already time-consuming task of creating websites. In short, I have consistently assumed web design to be a field exclusive to computer science experts and unfit for use by the average person. But after using Replit, I’ve seen firsthand how the strenuous tasks of coding and uploading for web design have been trans...

As can be seen in the Iris dataset example below, this week’s reading focused on the practical and real-world applications of both descriptive statistics and probability distributions. It made me think about how these statistical and analytical tools could have been streamlined during the data analysis portion of my economics research project with the help of Python and AI. For some background, my research examines the concept of logistics sprawl in rural areas. Logistics sprawl is a phenomenon characterized by the geographic dispersal of distribution centers, warehouses, and other logistics facilities. In other words, these logistics campuses are relocating to various areas, including, but not limited to, metropolitan, suburban, exurban, and rural regions. This is primarily motivated by the proximity of key transportation thoroughfares such as highways, airports, and railways, as well as the growing need for larger warehousing centers on larger and relatively cheaper tracts of land. A...

There are particular mathematical and scientific principles that underlie the functions of machine learning in AI. I used to believe that only individuals with a quaternary education in mathematics and data science were capable of both understanding and applying these principles in the context of AI. However, after reading about concepts related to linear algebra and data science—including vectors, matrices, and linear transformations—I realized that my previous assumptions were incorrect. Instead of the complex functions I was expecting, simple-to-understand mathematics form the foundations of AI and machine learning. In fact, I recall learning about some of these functions and concepts, such as vectors and matrices, in my junior year of high school. Despite my familiarity with some of these mathematical ideas, I had never encountered the concept of linear transformations before this reading. Linear transformations can involve processes such as normalization, as demonstrated in the re...