Let’s Evolve Intelligence, Not Solutions

Modern methodologies across the disparate fields of artificial intelligence, including neural networks, evolutionary computation and machine learning, suffer from some limiting assumptions and perspectives that perhaps fundamentally prevent us from pursuing the creation of strong, or at least strongish, AI. This position paper offers several contrarian posits, namely that it is impossible to engineer intelligence, that there is no Occam’s Razor for intelligence, that intelligence must be grounded and transferable, and that intelligence must be intrinsically self-reinforcing. Based on these, a new re-framing is discussed of the worlds, drivers, models and processes needed to support the creation of strongish AI. Key elements include the need for an intelligence function, the value of increasing the complexity of the world and drivers over time, and the importance of composable intelligences and processes. Some notations for this new framing are provided, musings on revisiting reproducibility in the context of intelligence are discussed and some preliminary thoughts for how to pursue these ideas using genetic programming for example are offered. Let’s move together towards a common methodology for creating quantifiable, grounded intelligence capabilities that are shareable across different efforts and AI techniques, and work collectively to create robust artificial general intelligences.