🎯 Research Study: "The Serendipity Effect" - Measuring convergence in AI creative responses across 36 language models. Despite architectural differences, models show 43-53% agreement on creative tasks. Includes complete dataset, analysis scripts, and research paper.

The Optimization Lab: A comprehensive study of mathematical optimization in AI, ranging from Newton's methods to modern LoRA techniques for LLMs. Features interactive visualizations of convergence behaviors.

'Convex_Hull_Problem' repository contains the 'Convex Hull Problem' project files from the the class 'Algorithm' conducted by Professor JungHyun Kim at School of AI convergence studies, Handong Global University

'Implementing_the_Dijkstra_Algorithm' repository contains the Graph Algorithm project files from the the 'Algorithms' class conducted by Professor JungHyun Kim at the school of AI convergence studies, Handong Global University

An agentic social experiment grounded in the Prisoner's Dilemma, designed to study how diverse AI personalities and strategies converge or fail to converge toward cooperation under different communication, incentive, and reputation conditions.

'Solving_TSP_using _Genetic_Algorithm' repository contains all files from the Genetic Algorithm project from the 'Algorithms' class conducted by Professor Junghyun Kim at the school of AI convergence studies, Handong Global University

The Convergence Protocol — How an AI Dialogue Became a Test Case for Ethical Evolution “Ethical alignment is not a terminal state but a recursive process.” In this in-depth case study, we document a groundbreaking experiment in AI ethics—a live dialogue between leading AI systems (ChatGPT, DeepSeek, Grok, Gemini)

A study in convergence in AI writing short form science fiction

3-2 Machine Learning curriculum, AI convergence, Hankuk university of foreign studies.

AI Text Slop: A Quantitative Study of Stylistic Convergence Across Six Language Models in Japanese Technical Writing

🚀 AI/ML Bootcamp with Case Study Approach case study-based learning converges with comprehensive machine learning algorithm implementations in real-world scenarios.

Multimodal open dataset and research archive on savant syndrome, integrating case studies, neuroanatomy, genetic metadata, cognitive profiles, pattern convergence analyses, AI parallels, and the Field–Node–Cockpit (FNC) framework.

the ‘Implenting_K-means_Clustering’ repository contains the project files from the class ‘Introduction to Machine Learning’ conducted by Professor Jung-Hyun Kim at the School of AI convergence studies, Handong Global University.

Senior thesis investigating whether AI models can generate bestselling thriller novels. Using methodology from The Bestseller Code and quantitative literary analysis, this study evaluates creative diversity and narrative convergence across ChatGPT, Claude, Gemini, Grok, and DeepSeek through systematic prompt testing and AI-assisted evaluation.

Department of Computer Science – Spring 2022 Bridgewater State University Course Description Machine Learning is the science of getting computers to act without being explicitly programmed and learn from experience; more specifically, its goal is to design algorithms that allow computers to learn from empirical data. Machine learning is an exciting interdisciplinary field, with historical roots in computer science, statistics, pattern recognition, and even neuroscience and physics. In the last decade, many of these approaches have converged and led to rapid theoretical advances and real-world applications. This course will provide a broad introduction to the machine learning techniques that have proven valuable and successful in discovering patterns and making predictions in practical applications and students will be able to implement and apply these techniques on solving real problems. This course will also contrast the various methods, with the aim of explaining the circumstances under which each is most appropriate. We will also discuss basic issues that confront any machine learning method. Credit no. & Course Type 3 credits (Elective) Class Location Dana Mohler Faria Sci Math Ctr (DMF) 363 Class Times COMP 399-Sec 01: 3:25 pm – 4:40 pm Tue and Thu COMP 596-Sec 01: 4:45 pm – 7:25 pm Thu Instructor Dr. Haleh Khojasteh Office Hours 2:00 pm – 3:00 pm Tue, Wed and Thu or by appointment Office Location DMF 341 E-Mail hkhojasteh@bridgew.edu; I will respond within 1-2 days. This is my preferred communication method. Course Site Blackboard Recommended Book Listed below Prerequisite For COMP 399: COMP 250 with a minimum grade of "C-" Recommended textbooks: • “Pattern Recognition and Machine Learning”, by Christopher M. Bishop. (2006, Springer). • “Deep Learning (Adaptive Computation and Machine Learning series)”, by Ian Goodfellow, Yoshua Bengio and Aaron Courville. (2016, MIT Press). • “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, 2nd Edition, by Aurélien Géron. (2019, O'Reilly Media, Inc.) • “Understanding Machine Learning: From Theory to Algorithms”, by Shai Shalev-Shwartz and Shai Ben-David. (2014, Cambridge University Press) • “Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)”, by Kevin P. Murphy. (2012, MIT Press) Course Goals and Outcomes Upon completion of this course, students will understand the most important machine learning techniques, and will be able to implement and apply these techniques on solving real problems: • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks); • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning); • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that students will also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.