This world promises just one thing: continuous change. As humanity has moved through time much has changed in the worlds of science, mathematics, and physics. These shifts in humanity's comprehension often arrive unexpectedly, driven by education, innovation, and experimentation. Such transformative waves resemble a series of technology shocks that are known to cause significant disruptions within an industry and the economy broadly as firms permanently change the ways they produce and distribute goods and services in response to new technologies or information. The recent flurry of innovation and interest in Artificial Intelligence leads us to believe that many industries may be experiencing such a wave of change today. The healthcare industry currently employs the most workers of any other sector in the United States (outside of the government) and is made up of an unprecedented 77% of female workers making the outcomes of changes in its labor market demands particularly important. In this paper we discuss the current state of Artificial Intelligence adoption within the clinical side of healthcare, what sub sectors and occupations are most exposed, and to what extent the FDA approved AI-enabled clinical healthcare products replace or complement those tasks of existing occupations. We also interviewed a few healthcare professionals with different levels of seniority and exposure to AI-enabled products to develop a holistic understanding of current AI adoption, employee preparation, and potential labor market implications over the short and long term. We find that AI implementation within clinical healthcare settings is young in its life cycle yet fast growing. Current use cases are mostly in the earlier stages of the patient’s care journey assisting workers in various capacities in the processes of patient testing, diagnosis, care planning, and post-treatment monitoring. The tasks associated with patient interaction and care administration do not appear to be threatened by AI automation at this point in time. Additionally, approved Artificial Intelligence products for clinical use are disproportionately concentrated in the subsectors of radiology, neurology, and cardiology. Finally, our interviews revealed a concerning lack of consideration and preparation, among healthcare workers, for the potential automation of their fundamental tasks. Going forward, we believe it wise for healthcare workers to monitor the evolution of clinical AI use cases as well as the FDA approval of AI-enabled products and prepare for potential automation by continuing to learn new skills, take on additional responsibilities, and generally inject themselves into as many stages of the patient’s healthcare journey as possible to differentiate among other workers and avoid the coming wave of mass clinical automation.

Artificial Intelligence has had a massive burst of growth in the past two years, and will likely impact every job on the market. In my thesis I investigate how AI will affect the creative arts, specifically visual arts, creative writing and film, and why the industry might turn to using AI over human counterparts.

Historical trends of artificial intelligence have, as shown by recent quantitative and qualitative studies, shown that the reported threats (as understood by the general public) are vastly different from the tech industry’s most pressing and vital concerns. The modern AI that most people interact with on a daily basis are mostly helpful commercialized products or generative AI, leading to a cultural mindset where AI is an assistant capable of autonomous tasks. Popular fictional depictions of artificial intelligence clearly demonstrate that those perceptions of threats fall closely in line with the sorts of actions portrayed by AI characters, suggesting that pop media has a significant influence over its audience’s understanding of AI technology and its potential ramifications. To mitigate harm that AI tools can inflict upon the general public, there is an immediate need for technology-specific legislation, incentives and deterrents, and oversight so that artificial intelligence can be regulated and controlled.

The rapid expansion of artificial intelligence has propelled significant growth in the GPU market. In the evolving data center landscape, Company X faces challenges due to its lag in entering the GPU market, which jeopardizes its competitive advantage against industry players like Nvidia and AMD. To address these issues, our thesis aims to analyze market dynamics between CPUs and GPUs-whether they present distinct markets or compete against each other. We seek to guide Company X in maximizing profitability and sustaining its pivotal role in the semiconductor industry amidst the AI revolution. Specifically, we discuss optimizing their GPU offering, Falcon Shores, towards specific markets and doubling down on the production of CPUs.

The rapid expansion of artificial intelligence has propelled significant growth in the GPU market. In the evolving data center landscape, Company X faces challenges due to its lag in entering the GPU market, which jeopardizes its competitive advantage against industry players like Nvidia and AMD. To address these issues, our thesis aims to analyze market dynamics between CPUs and GPUs-whether they present distinct markets or compete against each other. We seek to guide Company X in maximizing profitability and sustaining its pivotal role in the semiconductor industry amidst the AI revolution. Specifically, we discuss optimizing their GPU offering, Falcon Shores, towards specific markets and doubling down on the production of CPUs.

The rapid expansion of artificial intelligence has propelled significant growth in the GPU market. In the evolving data center landscape, Company X faces challenges due to its lag in entering the GPU market, which jeopardizes its competitive advantage against industry players like Nvidia and AMD. To address these issues, our thesis aims to analyze market dynamics between CPUs and GPUs-whether they present distinct markets or compete against each other. We seek to guide Company X in maximizing profitability and sustaining its pivotal role in the semiconductor industry amidst the AI revolution. Specifically, we discuss optimizing their GPU offering, Falcon Shores, towards specific markets and doubling down on the production of CPUs.

This research targets multimedia content generated by Artificial Intelligence (AI) on social media and measures user responses to the content. AI-generated content on social media has the potential to spread misinformation, so it is important to investigate the type of responses such content evokes. This research asks how easily users can recognize the provenance of AI-generated content, what emotional reactions they have to the content, and how factors such as disclaimers, topic, and platform effect recognition and reaction. The study was done by analyzing comments on popular posts on TikTok and X containing multiple types of AI-generated media spanning a wide range of topics. Findings underscore a dominant majority of negative responses (70.8%, 177 comments) and comments with themes of Aversion (45.2%, 113 comments). Contextual analysis pointed out a stronger negativity towards disinformative posts (89.2%, 33 comments) and more positivity towards humorous posts (39.3%, 11 comments). Differences between platforms showed that X users properly recognized the provenance of AI content 7.3% more than TikTok users, further influenced by the presence of disclaimers. User disclaimers were more effective than platform disclaimers, showing the pivotal role users play in combating misinformation on social media. This research displays the scarcity of platform-initiated disclaimers, showing a need for more proactive measures to identify AI content. 9.6% of responses (24 comments) included legislative sentiments, which paired with such a large majority of negative responses highlights public support for regulatory interventions as societal apprehension towards AI lingers. As AI continues to develop, more research is needed to determine the ability of humans to discern the provenance of AI-generated multimedia content, and new ways to combat misinformation on social media may be needed to address this new technology.

Artificial intelligence (AI) and machine learning (ML) algorithms are revolutionizing the field of healthcare by offering new opportunities for improved diagnosis and treatment planning. These technologies have the potential to transform the way medical professionals approach patient care by analyzing vast amounts of data, identifying patterns, and making predictions. This overview highlights the current state of research and development in the field of AI and ML for diagnosis and treatment planning, as well as explore the ethical benefits and challenges associated with their implementation.