Research Platform
AIscienceAI develops research-driven approaches for studying artificial systems through measurable frameworks, structured analysis, and formal evaluation methods.
The platform is designed to make complex AI questions clearer through careful modelling, conceptual precision, and accessible scientific communication.
Core Research Focus
Artificial Age Score
The Artificial Age Score (AAS) is a framework for analysing artificial memory, recall consistency, redundancy, entropy, and structural transformation in AI systems.
AI Evaluation
AIscienceAI studies how artificial systems behave, change, remain consistent, or become unstable across repeated interactions and use cases.
Artificial Memory Modelling
The platform examines how AI systems retain, recall, distort, or reorganise information over time.
Simulation-Based Methodology
AIscienceAI uses simulation, mathematical reasoning, and structured modelling to study artificial behaviour and AI-related outcomes.
Research Methodology & Framework Design
The platform develops conceptual models, scoring structures, evaluation logic, and methodological frameworks for AI-focused research.
Research Cartoons
AIscienceAI also uses research cartoons and visual storytelling to communicate ideas about artificial intelligence, academic life, publication culture, and scientific thinking.
Artificial Age Score (AAS)
Rather than measuring artificial systems through chronological time, AAS focuses on structural change: how memory behaviour, recall accuracy, redundancy, entropy, and response consistency evolve across repeated interactions.
This makes AAS a framework for studying artificial ageing, transformation, and memory degradation in a measurable way.
Research Direction
- AI evaluation
- Artificial memory modelling
- Artificial Age Score applications
- Redundancy-adjusted scoring
- Simulation-based methodology
- Metamorphic inconsistency analysis
- Human–AI and human–robot evaluation
- Research methodology and framework design
- Scientific communication and research cartoons
Research Lead
AIscienceAI is developed by Seyma Yaman Kayadibi, an Australia-based mathematician, AI researcher, and developer of the Artificial Age Score framework.
Collaborations
AIscienceAI is open to selected research collaborations, framework discussions, AI evaluation projects, and scientific communication initiatives.
Contact
For research collaboration, framework discussions, and project inquiries, contact AIscienceAI.