Common Sense and AI

Common sense is shared knowledge about people and the physical world, enabled by the biological brain. It comprises intuitive psychology, intuitive physics, and intuitive sociality. Unlike deep neural networks, common sense requires only limited experience. Human intelligence has evolved to deal with uncertainty, independent of whether big or small data are available. Complex AI algorithms, in contrast, work best in stable, well-defined situations such as chess and Go, where large amounts of data are available. This stable-world principle helps to understand what statistical algorithms are capable of and distinguish it from commercial hype or techno-religious faith. I introduce the program of psychological AI, which uses psychological heuristics to make algorithms smart. For instance, when predicting the spread of the flu, a situation of uncertainty, the recency heuristic which relies on only one data point can lead to better predictions than Google Flu Trends’ big-data algorithms. What we need is a fusion of the adaptive heuristics that embody common sense with the power of machine learning. Reference: Gigerenzer, G. (2022). How to stay smart in a smart world. MIT Press.