Unprecedented technological growth is upon us. Augmented reality (AR), a technology that enhances our capabilities, is one of the most promising examples. My research shows that AR is often a cost-effective alternative to more widely used technologies such as cell phones and personal computers. It is also seen to be more accessible. Additionally, AR has the potential to be more environmentally friendly, safer, and more efficient than current technology. In this literature review, I attempt to answer the question, "How could AR replace current technology to increase capability and accessibility?" Results and experiments have shown that AR is becoming more capable and cost-effective. This pattern indicates that AR will be adapted more to increase our capabilities as a species. Greater adaptation of AR has correlated with more equity amongst the population, giving equal opportunities for a wider range of abilities and socio-economic status. This is evident with a substantially greater student-to-teacher ratio in schools as well as a much greater potential to train many new individuals in professional careers than would exist otherwise. All of this is done while drastically reducing or even eliminating safety concerns. My research has shown a positive growth and implementation pattern that indicates a future inundated with this technology will soon be on the horizon, if it is not already. This implies a future where AR will help us develop a more equitable society.

Have you ever watched a true crime show? I’d be surprised if you said no. Stories of violent crime inundate the entertainment offered to individuals whether you’re looking for a TV show or a podcast. People’s worst moments are retold for TV audiences under the guise of investigation. While crime media has always been popular, true crime media, television in particular, is having a moment of unprecedented popularity. Drawing from my own experiences as a white middle-class viewer of true crime I wonder what the dangers of this genre's popularity are. This project investigates this by asking how the consumption of others' trauma through true crime television impacts how individuals who have no contact with the criminal justice system understand violence and crime. It asks whether or not this creates additional distance between individuals who are not system impacted and those who are. And finally, it wonders if true crime's popularity can be used to put its viewers in conversation with abolition. Using Dateline NBC episodes as its archive, this project hopes to navigate the complicated nature of true crime viewership and its harms. The archive of Dateline episodes provides examples of key narratives within true crime as well as facilitates a true crime viewing experience informed by abolitoinist politic. The patterns observed in Dateline are put in conversation with my academic research to both attempt to answer my research questions as well as articulate critiques of true crime television. These intentions are creatively rendered into a zine that synthesizes my research as well as my relationship to the genre and Dateline specifically. I hope that this project inspires individuals to engage critically with their entertainment and understand that entertainment is a means of perpetuating hegemony.

As the field of astrophysics continues to grow, the quantity of data to analyze is constantly expanding. With projects like the James Webb Space Telescope each sending back hundreds of gigabytes of data every day, Artificial Intelligence (AI) technologies is needed to assist manual analytical techniques in processing these volumes of information. One of the most apparent tasks for AI in astrophysics is image categorization – identifying what sort of astronomical object a certain body is. If a machine could categorize these bodie in significantly less time than a person, it would free tens of thousands of human hours every year. I created a Machine Learning program using a Deep Neural Network (DNN) implemented in Keras and TensorFlow capable of classifying astronomical images based on photometric data. Built from scratch, it utilizes existing labeled images to “learn” how astronomical bodies differ in appearance and assign them a category. The value of automated classification of astronomical phenomena cannot be understated. DNN allows the model to find unique identifiers in images humans often cannot spot, leading to often-more reliable predictions, recognizing possible discoveries in far less time, and freeing astronomers to undertake higher-cognition tasks only humans can accomplish. As the model is continuouly improved, it will be able to make increasingly accurate classifications and be of ever-growing value.