NLPIR 2026 Spcial Session 2
Topic: Emerging Frontiers in Artificial Intelligence: Advanced Learning Paradigms, Language Intelligence, and Real-World Applications
Organizer(s): Dr. Sadam Al-Azani, King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia
Artificial Intelligence (AI) is rapidly evolving through advances in machine learning, deep learning, natural language processing (NLP), video analytics, and social network analysis. Recent developments in large language models, self-supervised learning, and embodied AI are opening new opportunities for intelligent systems that can understand complex information, learn from limited supervision, and interact effectively with the real world. Meanwhile, AI is transforming knowledge discovery in scientific domains such as biomedical research and climate analysis, as well as in specialized fields such as legal document processing.
This special session aims to bring together researchers and practitioners to explore emerging methodologies, innovative architectures, and practical applications of AI. It welcomes contributions on advanced learning paradigms, language and multimodal intelligence, intelligent video analysis, social network mining, and AI-driven scientific discovery. Particular emphasis is placed on interdisciplinary research, trustworthy and efficient AI systems, and the integration of intelligent models with real-world environments. By fostering discussions on both theoretical advances and practical implementations, the session seeks to identify new research opportunities and promote the development of robust, adaptive, and human-centered AI technologies.
Topics of Interest:
Natural Language Processing and Computational Linguistics
Large Language Models and Generative AI
LLMs for Biomedical NLP and Scientific Knowledge Discovery
AI for Climate Text Analysis and Environmental Intelligence
Legal Document Processing and Domain-Specific Language Models
Video Analytics, Video Understanding, and Intelligent Surveillance
Social Network Analysis, Graph Learning, and Community Detection
Self-Supervised Learning and Unsupervised Representation Learning
Pre-Training Paradigms Beyond Transformer-Based Architectures
Embodied AI and Language-Model-Driven Robotics
Multimodal Learning and Vision-Language Models
Human-AI Interaction and Conversational AI Systems
Explainable, Trustworthy, and Responsible AI
Efficient, Adaptive, and Scalable AI Methods