C) I’m a Data Science major who’s gonna be looking for full-time work in the tech sector starting next semester. Given that the work I’m aiming to gt into will use a lot of machine learning and repeated tests, using AI like LLMs is going to be very useful (and likely essential) for handling the bottleneck of taking up a lot of time to draft boilerplate code and setting up tests for models. Also, a lot of companies are pushing their engineers to use AI for their work, likely to keep up an output rate that the bosses would want you to have. Their philosophy being it’s better to prototype multiple times and amend it rather than writing the code from scratch and then looking up documentation for syntax things. I think if you want to work in the tech sector in a decently sized company you wouldn’t really have a choice to not use AI. At some point, your managers would get on your case for not having an output as fast as your coworkers (the quality of their code would be irrelevant). For myself personally, I’ve found LLMs very useful in my coding (for personal projects and for a research project I’m currently working on) for the boilerplate code, but knowing the logic for what functions and such to use is still very important for human engineers. An example of this already impacting people is for ex. Meta, who is starting to have AI-Assisted coding interviews. companies are definitely pushing people, and other companies, to use AI in their work. The main issue is definitely environmental on a corporate scale. Companies should not be allowed to cheap out on their payment for evnironmental resource use that Data Centers takes advantage of, especially in small towns.

I think AI has upsides, especially in furthering research at a pace never seen before. However, when it comes to classes, I do think the easily accessible LLMs run the risk of students cognitively offloading and passing on the smallest (and sometimes most crucial) design and structure choices for their assignments. For some assignments I could understand why a student would use an LLM, especially if you could give it an idea and ask it to expand on it, but for furthering your development as a student, it doesn’t really help. I thought the Dinsmore and Fryeer delved really deep into why that is: “They do not replace the need for humans to learn and practice the necessary processes to complete a task themselves in order to build on those processes to continue to develop in a field, domain, or topic” (Dinsmore & Fryer 7).
Although I do agree that overuse of LLMs does stunt your growth as a student, I do believe the onus is on schools to figure out how to incorporate AI into their curriculums. AI is rapidly evolving – which, for better or worse, we can’t do anything about.
An AI workshop I went to in Columbus a few days ago actually had a professor from University of Cincinnati talk about how a big focus of theirs is making students more AI-literate and how they’re using agents to help personalize learning for their students.

I think creativity is the uniquely human ability to come up with ideas/solutions formed through their unique lived experiences. Cause of that, I do think you can become “more” creative – through trying new things, and meeting people different from you.
For the experiment, I used Google Gemini. I don’t think its response was particularly creative. I asked it to write a poem in the style of Frank Ocean, a music artist. Here’s what it gave me:

“The streetlights bleed orange through the tinted glass. We are moving too fast to read the signs, just letting the city blur into something soft. You smell like chlorine and wet concrete, a suburban summer caught in your bleached hair.

I think about the way you looked at the deep end, half afraid to jump but wanting the water to swallow you. Now the radio is playing a song we do not know the words to, humming low under the sound of the engine.

White sneakers resting on the dashboard, counting miles like they are loose change in a pocket. You ask me if the world feels different at night. I just keep my hands on the wheel, watching the taillights ahead fade into the dark.

We loved each other in the spaces between the quiet. A fleeting kind of gold, slipping through my fingers before the sun even tried to rise.”

Although I could see the inspiration it used to draft what I asked, it still felt uninspired. Frank talks a lot about his environment and tries to use metaphors for how he feels in his music, but something like the last sentence just isn’t what a song of his would sound like. Also, no contractions (“We are moving” – sounds unnatural) or any colloquial language also shows LLMs’ current lack of being able to copy and artist’s entire style (which is probably for the better of art).

Alan Turing’s quote from the slides: “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” resonates with me in this example. I think no one would be able to be convinced that a human wrote these lyrics.

I think our experiments this week definitely showed me more of the limitations of AI when it comes to the creative sphere. I mostly use AI for other kinds of computing, so it is interesting to think about its role in shaping entertainment and art as we know it (right now).

Nebiyou
After reading and discussing the prompting guidelines in class Tuesday, my second prompt with Gemini was “Make me a diet for a gymgoer”. I thought this would be fun to try and since I go to the gym as well, I was curious about what recommendation I’d get. The response I got at first was that “we need to move past eating clean and focus on performance fuel”, then it gave me a basic meal plan and nutrition strategy. This was an interesting response, considering I didn’t specify what my training goals were (powerlifting, for athleticism, lifting for mass gain, etc.) Towards the end of the response, it did however ask me if I’m looking to bulk up, cut down, or maintain my physique while getting stronger.

Then I used the Flipping Technique and asked, “Ask me 5 questions about my goals, resources, and routine” and it gave me more focused questions, such as my primary objective, dietary dealbreakers, my current training schedule, and height and weight. I found this to be a lot more helpful and a step in the right direction. This prompt could be very useful for people new to weightlifting or people who have been lifting for a while but may not be seeing the results that they are looking for. I found the flipping technique from Dr. Hayward’s slideshow to be the most useful prompting technique. It’s nice to have the LLM ask questions to kinda home in on what I’m really looking for – since sometimes the prompter themselves might not even be sure what they’re looking for.

For my first prompt I tried, I asked it “Tell me about World War I”, where it gave me an okay overview. My follow up prompt was “You are a history teacher, and i’m a student in your college level class. tell me about the turning points in the world war 1 and the after effects of world war 1 in the western hemisphere”. I used advice from Tips to Get Started and Best Practices from UT’s Prompt Literacy guide, like telling Gemini what role it should take on, the audience, and what the purpose is. “To reduce the likelihood of hallucinations (outputs that sound plausible, but are incorrect or unrelated), tell it that it’s an expert in the topic you’re prompting. For
example, “You are an expert in men’s fashion design.” Other expert role examples
include: Copywriter
o Public Speaker
o Marketing Strategist”
(Under Best Practices)

I’m Nebiyou, I’m from Maryland, I’m a Data Science major, I like basketball, soccer, playing videogames, and hanging out with friends.

I think AI has both upsides and downsides – upside being the ease of automation that comes with tools like LLMs, downside being environmental impacts and bias against certain groups of people in the process of training the LLM