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- All Subjects: artificial intelligence
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Description
The rapid growth of published research has increased the time and energy researchers invest in
literature review to stay updated in their field. While existing research tools assist with organizing papers, providing basic summaries, and improving search, there is a need for an assistant that copilots researchers to drive innovation. In response, we introduce buff, a research assistant framework employing large language models to summarize papers, identify research gaps and trends, and recommend future directions based on semantic analysis of the literature landscape, Wikipedia, and the broader internet. We demo buff through a user-friendly chat interface, powered by a citation network encompassing over 5600 research papers, amounting to over 133 million tokens of textual information. buff utilizes a network structure to fetch and analyze factual scientific information semantically. By streamlining the literature review and scientific knowledge discovery process, buff empowers researchers to concentrate their efforts on pushing the boundaries of their fields, driving innovation, and optimizing the scientific research landscape.
ContributorsBalamurugan, Neha (Author) / Arani, Punit (Co-author) / LiKamWa, Robert (Thesis director) / Bhattacharjee, Amrita (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor)
Created2024-05
Description
Supply chain management is a complex field that deals with a variety of ever-changing factors, and artificial intelligence has the opportunity to create lots of value and drive efficiency if organizations can implement it effectively. This thesis examines the different types of AI based on functionality and capability and provides a brief overview of the history behind artificial intelligence. Different supply chain functions including demand forecasting, inventory management, route optimization, supply transparency, and safety and sustainability were analyzed before and after adding AI systems. After examining AI missteps and successes in recent years, a detailed roadmap was created to help decision-makers deal with the numerous complexities when implementing AI technology within a business to improve the supply chain.
ContributorsHildebrand, Ryan (Author) / Printezis, Antonios (Thesis director) / Pofahl, Geoffrey (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Department of Supply Chain Management (Contributor)
Created2024-05
Description
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.
ContributorsDolasinski, Nicholas (Author) / McElenney, Nicholas (Co-author) / Mehta, Ari (Thesis director) / Asheim, Brody (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Economics (Contributor)
Created2024-05
Description
With the growing presence of Artificial Intelligence(AI), this paper serves to examine its role in the corporate compliance world with an emphasis on hiring practices and policies. Current AI technology allows for companies to handle, sort, filter, and assess thousands of applications and resumes a day, all without the interference of humans. This shift from human to human hiring to AI to human is one that can be implemented, but must be done with caution. Ethical dilemmas such as bias, privacy, and lack of human interactions play major roles in determining if this process is for every business.
ContributorsValencia, Zacarias (Author) / Koretz, Lora (Thesis director) / Pofahl, Geoff (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
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.
ContributorsMcElenney, Nicholas (Author) / Dolasinski, Nicholas (Co-author) / Asheim, Brody (Co-author) / Mehta, Ari (Co-author) / Calvo, Paula (Thesis director) / Don, Rachel (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2024-05
Description
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.
ContributorsUlreich-Power, Cameron (Author) / Kujawa, Brennan (Co-author) / Mostaghimi, Dunya (Co-author) / Livesay, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Department of Economics (Contributor)
Created2024-05
Description
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.
ContributorsLivesay, Thomas (Author) / Kujawa, Brennan (Co-author) / Ulreich-Power, Cameron (Co-author) / Mostaghimi, Dunya (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2024-05
Description
In today’s world, artificial intelligence (AI) is increasingly becoming a part of our daily lives. For this integration to be successful, it’s essential that AI systems can effectively interact with humans. This means making the AI system’s behavior more understandable to users and allowing users to customize the system’s behavior to match their preferences. However, there are significant challenges associated with achieving this goal. One major challenge is that modern AI systems, which have shown great success, often make decisions based on learned representations. These representations, often acquired through deep learning techniques, are typically inscrutable to the users inhibiting explainability and customizability of the system. Additionally, since each user may have unique preferences and expertise, the interaction process must be tailored to each individual. This thesis addresses these challenges that arise in human-AI interaction scenarios, especially in cases where the AI system is tasked with solving sequential decision-making problems. This is achieved by introducing a framework that uses a symbolic interface to facilitate communication between humans and AI agents. This shared vocabulary acts as a bridge, enabling the AI agent to provide explanations in terms that are easy for humans to understand and allowing users to express their preferences using this common language. To address the need for personalization, the framework provides mechanisms that allow users to expand this shared vocabulary, enabling them to express their unique preferences effectively. Moreover, the AI systems are designed to take into account the user’s background knowledge when generating explanations tailored to their specific needs.
ContributorsSoni, Utkarsh (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Bryan, Chris (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2024
Description
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.
ContributorsAsheim, Brody (Author) / Dolasinski, Nicholas (Co-author) / Mehta, Ari (Co-author) / McElenney, Nicholas (Co-author) / Calvo, Paula (Thesis director) / Don, Rachael (Committee member) / Medcalf, Rollin (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / Department of Finance (Contributor)
Created2024-05
Description
In this thesis, I propose a framework for automatically generating custom orthotic insoles for Intelligent Mobility and ANDBOUNDS. Towards the end, the entire framework works together to ensure users receive the highest quality insoles through human quality checks. Three machine learning models were assembled: the Quality Model, the Meta-point Model, and the Multimodal Model. The Quality Model ensures that user uploaded foot scans are high quality. The Meta-point Model ensures that the meta-point coordinates assigned to the foot scans are below the required tolerance to align an insole mesh onto a foot scan. The Multimodal Model uses customer foot pain descriptors and the foot scan to customize an insole to the customers’ ailments. The results demonstrate that this is a viable option for insole creation and has the potential to aid or replace human insole designers.
ContributorsNucuta, Raymond (Author) / Osburn, Steven (Thesis director) / Joseph, Jeshua (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05