Towards Fluid Machine Intelligence: Can We Make a Gifted AI?

Abstract

Most applications of machine intelligence have focused on demonstrating crystallized intelligence. Crystallized intelligence relies on accessing problem-specific knowledge, skills and experience stored in long term memory. In this paper, we challenge the AI community to design AIs to completely take tests of fluid intelligence which assess the ability to solve novel problems using problem-independent solving skills. Tests of fluid intelligence such as the NNAT are used extensively by schools to determine entry into gifted education programs. We explain the differences between crystallized and fluid intelligence, the importance and capabilities of machines demonstrating fluid intelligence and pose several challenges to the AI community, including that a machine taking such a test would be considered gifted by school districts in the state of California. Importantly, we show existing work on seemingly related fields such as transfer, zero-shot, life-long and meta learning (in their current form) are not directly capable of demonstrating fluid intelligence but instead are task-transductive mechanisms.