The article explores the prediction of Artificial General Intelligence (AGI) arriving by 2027, analyzing both mathematical and intuitive forecasting methods. It critiques reliance on formal models, arguing that while they offer a structured approach, they often mask the subjective nature of their inputs. The author contrasts two forecasting types:
- Mechanistic Models, suited for well-defined phenomena like climate change, where complex interactions can be mathematically modeled.
- Intuitive Forecasts, which may be more effective in uncertain scenarios like geopolitical developments, where the variables are too numerous and complex for reliable modeling.
Focusing on the AI 2027 forecast, the author highlights its reliance on task completion metrics from AI systems rather than comprehensive models. They suggest that using a "scribble-based" method to visualize potential future scenarios can provide clearer insights. Ultimately, the piece promotes a hybrid approach to forecasting, blending intuition with data, while also introducing a tool for users to create their own forecasts intuitively.
