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Week 8 - BALT 4363 - LinkedIn Refresh and Chatbase Exploration

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This week, I decided to update my LinkedIn profile to reflect the AI skills I’ve cultivated in this course. These skills include Python, Replit, Lovable, Chatbase, and Generative AI. While updating my profile and reflecting on the knowledge gained from this course, I found myself once again considering the implications of AI in accounting, my chosen career field. Unfortunately, many discussions purport that AI will lead to the demise of humans’ role in accounting. At best, this claim is only half true. While it is inevitable that AI will replace accountants previously tasked with completing journal entries and other repetitive work, I predict the implementation of AI will not eradicate the human element of accounting. Instead, there will be a shift in accounting roles. Repetitive tasks will be replaced by those increasingly driven by creative thinking and analytics. Furthermore, AI is prone to mistakes and hallucinations. Based on these circumstances, human-based accounting roles will ...
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Week 7 - BALT 4363 - Lovable Experimentation

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After using Lovable, I was surprised to find how closely it resembled Replit in terms of functionality and ease of use. All I needed to do was type in my prompt with all the necessary details, and Lovable was able to create a fully functioning app based on these requirements. Despite the initial similarities between these two AI platforms, I feel that Lovable is better suited for use by the average person. When I typed information into the Replit prompt box, I had to keep repeating myself or rephrasing my initial request in slightly different ways to get it to understand and subsequently implement it into my webpage design. For example, I created an Outfit Roulette simulator and asked Replit to integrate both female and male models, depending on whether the user selected the “masculine” or “feminine” clothing option. However, Replit struggled to interpret this request, as all the picture outputs included only female models. After multiple rephrasing attempts, Replit was still not able ...
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Week 6 - BALT 4363 - Replit Experimentation

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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...
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Week 5 - BALT 4363 - Descriptive Statistics and Probability Distributions

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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...
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Week 4 - BALT 4363 - Introduction to Linear Algebra for Data Science

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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...
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Week 3 - BALT 4363 - Handling and Cleaning Data with Python Libraries

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As I continue to learn about the workings of Python, I am again struck by the functional similarities between this computer programming language and Microsoft Excel. These tools can consequently be leveraged, given a particular situation, to maximize both efficiency and meaningful outcomes. When it comes to automation, I personally don’t believe that Python produces quicker results than Excel when data cleaning or basic, functional data tasks are required. There is most likely an equal amount of time spent writing functional code for Python and creating a function or table in Excel in this instance. In the example shown in the picture above, the code necessary to produce the mean global sales for each genre of video game took about as much time, if not more, to write than the time it took me to produce the same results through the PivotTable function in Excel. Despite the advantage Excel offers when simple functions and smaller amounts of data are involved, Python maintains the upper h...
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Week 2 - BALT 4363 - Python Data Manipulation

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After delving more into the inner workings of Python, I realized my initial hesitancy regarding this particular programming language was unfounded. I believed that Python would be difficult to work with because of its inherent nature as a programming language. I’ve experimented with different programming languages in the past, all of which were difficult to work with and comprehend. It follows that I would expect Python to be cumbersome as well. Instead, I was quickly surprised to find that it was simple to use and understand. I also found it interesting that many of the different coding functions that Python uses are similar to those of Microsoft Excel. This may be why I found this programming language more intuitive than the ones I’ve experimented with in the past. For example, the “=IF” function in Excel can return a similar output to that of Python’s “if” statements. Furthermore, the “=IF” function in Excel can be combined with the “=OR” function. This combination of Excel func...
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