Editing Research:Question-18-AI-Capability-Prediction (section)

=== Current Prediction Approaches ===

Contemporary AI capability prediction employs multiple methodologies:

'''Scaling Law Analysis:''' Mathematical relationships between model parameters, training data, computational resources, and performance metrics. The most prominent example is the [[GPT Series]] scaling studies showing predictable relationships between model size and various capability measures.

'''Research Trajectory Modeling:''' Analysis of publication patterns, funding flows, and researcher migration to predict future research focus areas and breakthrough potential. This approach examines both quantitative metrics (paper counts, citation patterns) and qualitative assessments of research directions.

'''Benchmark Progression Analysis:''' Systematic tracking of performance improvements on standardized benchmarks to identify consistent advancement rates and predict future milestone achievements. Examples include [[ImageNet]] progression in computer vision and various [[Natural Language Processing]] benchmarks.

'''Expert Survey Methodologies:''' Structured elicitation of predictions from AI researchers and industry experts, including confidence intervals and reasoning documentation. Notable examples include the [[AI Impacts]] survey series and various conference prediction markets.