Test models against actual future outcomes, not hypotheticals. Measure forecasting accuracy across domains: financial markets, geopolitical events, technological breakthroughs.
Dissect why models succeed or fail at specific predictions. Map which architectures excel in different prediction domains. Research optimal human-AI collaboration patterns.
Translate complex AI capabilities into actionable forecasting tools. Create frameworks for organizations to implement AI prediction systems. Publish accessible insights.
Daily predictions with real-world validation. Public leaderboards showing model performance over time. Open datasets advancing the field of AI forecasting.
Current AI benchmarks reward memorization over reasoning. We explore why no objective measures of true predictive intelligence exist and how models are being optimized for benchmarks rather than real-world performance.
While AI excels at pattern recognition, there's a fundamental gap between predicting outcomes and making decisions. We dissect the difference and why it matters for practical AI applications.
Traditional benchmarks test knowledge recall. We're creating a new paradigm: testing AI against actual future outcomes across financial markets, geopolitical events, and technological breakthroughs.
Not all models are created equal for prediction tasks. Our empirical analysis reveals which architectures excel at different types of forecasting, from market movements to social phenomena.