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- All Subjects: artificial intelligence
Description
Artificial Intelligence (AI) is a rapidly advancing field with the potential to impact every aspect of society, including the inventive practices of science and technology. The creation of new ideas, devices, or methods, commonly known as inventions, is typically viewed as a process of combining existing knowledge. To understand how AI can transform scientific and technological inventions, it is essential to comprehend how such combinatorial inventions have emerged in the development of AI.This dissertation aims to investigate three aspects of combinatorial inventions in AI using data-driven and network analysis methods. Firstly, how knowledge is combined to generate new scientific publications in AI; secondly, how technical com- ponents are combined to create new AI patents; and thirdly, how organizations cre- ate new AI inventions by integrating knowledge within organizational and industrial boundaries. Using an AI publication dataset of nearly 300,000 AI publications and an AI patent dataset of almost 260,000 AI patents granted by the United States Patent and Trademark Office (USPTO), this study found that scientific research related to AI is predominantly driven by combining existing knowledge in highly conventional ways, which also results in the most impactful publications. Similarly, incremental improvements and refinements that rely on existing knowledge rather than radically new ideas are the primary driver of AI patenting. Nonetheless, AI patents combin- ing new components tend to disrupt citation networks and hence future inventive practices more than those that involve only existing components.
To examine AI organizations’ inventive activities, an analytical framework called the Combinatorial Exploitation and Exploration (CEE) framework was developed to measure how much an organization accesses and discovers knowledge while working within organizational and industrial boundaries. With a dataset of nearly 500 AI organizations that have continuously contributed to AI technologies, the research shows that AI organizations favor exploitative over exploratory inventions. However, local exploitation tends to peak within the first five years and remain stable, while exploratory inventions grow gradually over time.
Overall, this dissertation offers empirical evidence regarding how inventions in AI have emerged and provides insights into how combinatorial characteristics relate to AI inventions’ quality. Additionally, the study offers tools to assess inventive outcomes and competence.
ContributorsWang, Jieshu (Author) / Maynard, Andrew (Thesis advisor) / Lobo, Jose (Committee member) / Michael, Katina (Committee member) / Motsch, Sebastien (Committee member) / Arizona State University (Publisher)
Created2023
Description
A skin lesion is a part of the skin which has an uncommon growth or appearance in comparison with the skin around it. While most are harmless, some can be warnings of skin cancer. Melanoma is the deadliest form of skin cancer and its early detection in dermoscopic images is crucial and results in increase in the survival rate. The clinical ABCD (asymmetry, border irregularity, color variation and diameter greater than 6mm) rule is one of the most widely used method for early melanoma recognition. However, accurate classification of melanoma is still extremely difficult due to following reasons(not limited to): great visual resemblance between melanoma and non-melanoma skin lesions, less contrast difference between skin and the lesions etc. There is an ever-growing need of correct and reliable detection of skin cancers. Advances in the field of deep learning deems it perfect for the task of automatic detection and is very useful to pathologists as they aid them in terms of efficiency and accuracy. In this thesis various state of the art deep learning frameworks are used. An analysis of their parameters is done, innovative techniques are implemented to address the challenges faced in the tasks, segmentation, and classification in skin lesions.• Segmentation is task of dividing out regions of interest. This is used to only keep the ROI and separate it from its background.
• Classification is the task of assigning the image a class, i.e., Melanoma(Cancer) and Nevus(Not Cancer). A pre-trained model is used and fine-tuned as per the needs of the given problem statement/dataset.
Experimental results show promise as the implemented techniques reduce the false negatives rate, i.e., neural network is less likely to misclassify a melanoma.
ContributorsVerma, Vivek (Author) / Motsch, Sebastien (Thesis advisor) / Berman, Spring (Thesis advisor) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021