Mirindi Derrick

Followers

Following

Co-authors

Public Views

Derrick Mirindi, a civil engineer, both structural and hydroinformatician specialist, and member of the American Institute of Architecture Students (AIAS), American Society of Civil Engineers (ASCE), Construction Management Association of America (CMAA), and Deep Foundations Institute (DFI), is a doctoral candidate in Architecture, Urbanism, and Built Environments with a strong foundation in civil engineering and hydroinformatics. His research interests lie in the intersections of infrastructure, artificial intelligence (AI), machine learning (ML), and remote sensing, with a focus on an-alyzing urban nexus analysis through remote sensing and nexus assessment and modeling, as well as combining structural mate-rials for construction in Building Information Technology (BIM). Derrick is committed to advancing knowledge in sustainable infrastructure solutions using waste materials and is seeking opportunities to collaborate, teach, and further his research. He has a diverse educational background, including a Master of Science in Water Science and Engineering with a specialization in hydroinformatics from the Netherlands, a Master of Science in Civil Engineering with a focus on structures from Kenya, and a Bachelor of Science in Civil Engineering from Burundi. Der-rick's research experience includes roles as a research assistant at Morgan State University, where he conducts literature reviews, designs research studies, and collaborates with other researchers. He has published various articles focusing on waste materials for construction, artificial intelligence, and building information technology.
Phone: 16673817575
Address: 4 Thurmont

less

Interests

Uploads

Papers by Mirindi Derrick

Advance toward Artificial Superintelligence with OpenAI's O1 reinforcement learning and ethics

IEEE, 2025

We acknowledge why the larger AI community believes that superintelligence is not far away if an ... more We acknowledge why the larger AI community believes that superintelligence is not far away if an artificial intelligence (AI) can search for answers, learn from its findings, and apply that improved knowledge to conduct even better searches in the future. This study aims to assess OpenAI O1's reinforcement learning framework and its potential implications for superintelligence by examining its four fundamental pillars: policy initialization, reward design, search mechanisms, and learning processes. We examine how O1's architecture implements elaborate methods such as Process Reward Modeling (PRM) and advanced search strategies using recent developments in AI technology, with a particular focus on the transition from artificial narrow intelligence (ANI) to artificial superintelligence (ASI) and the emergence of hybrid artificial intelligence (HAI). Our method involves examining the types of AI, using recent research on OpenAI O1 reinforcement learning and elaborate ethical considerations. Key findings demonstrate the superiority of PRM over traditional Outcome Reward Modeling (ORM) in providing granular feedback for learning optimization. In addition, the advance toward ASI emphasizes the critical importance of ethical considerations. This includes safety protocols, transparency requirements, and human-aligned development practices in advancing toward superintelligence. This study contributes to the understanding of how advanced AI systems can be developed responsibly while ensuring progress toward superintelligence remains beneficial to humanity. Future research directions should focus on enhancing the integration of ethical frameworks within the reinforcement learning system.

Mirindi Derrick

Uploads

Papers by Mirindi Derrick

Log In