abstract = "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
intelligence 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.",