Architectural Characteristics of Apple Silicon for Machine Learning Applications; The Rise of Generative Artificial Intelligence as a Technological System 135 views

As artificial intelligence systems are increasingly scaled and integrated into society, their sustainability—both in terms of technical infrastructure and societal impact—is being brought into focus. This portfolio reflects a desire to understand and improve the foundations of AI, which is approached from both sociotechnical and system-level perspectives. As an exploration of the subject and a tool to target technical research, the widespread adoption of generative AI technologies is examined as a product of societal trends and policy decisions through which the evolution of AI is being shaped. Along with this general investigation, a more technical examination of performance and feasibility of computing hardware for large language models is analyzed through a comparison of architectures such as Apple Silicon and NVIDIA CUDA. This research aids in predicting the trajectory of GenAI as a technological system and supports development of efficient subsystems to improve the environmental sustainability of AI. Through these projects, a commitment is shown to ensuring that the future of AI is developed in a way that is powerful, efficient, and mindful of human and ecological needs.

BS (Bachelor of Science)

Generative AI; Artificial Intelligence; Apple Silicon; CUDA; Machine Learning; Technological Momentum; Sociopolitics; Social Construction of Technology

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Felix Lin STS Advisor: Bryn Seabrook Technical Team Members: Tao Groves

English

2025-05-08