  Recent progress in deep learning is essentially based on a "big data for
small tasks" paradigm, under which massive amounts of data are used to train a
classifier for a single narrow task. In this paper, we call for a shift that
flips this paradigm upside down. Specifically, we propose a "small data for big
tasks" paradigm, wherein a single artificial intelligence (AI) system is
challenged to develop "common sense", enabling it to solve a wide range of
tasks with little training data. We illustrate the potential power of this new
paradigm by reviewing models of common sense that synthesize recent
breakthroughs in both machine and human vision. We identify functionality,
physics, intent, causality, and utility (FPICU) as the five core domains of
cognitive AI with humanlike common sense. When taken as a unified concept,
FPICU is concerned with the questions of "why" and "how", beyond the dominant
"what" and "where" framework for understanding vision. They are invisible in
terms of pixels but nevertheless drive the creation, maintenance, and
development of visual scenes. We therefore coin them the "dark matter" of
vision. Just as our universe cannot be understood by merely studying observable
matter, we argue that vision cannot be understood without studying FPICU. We
demonstrate the power of this perspective to develop cognitive AI systems with
humanlike common sense by showing how to observe and apply FPICU with little
training data to solve a wide range of challenging tasks, including tool use,
planning, utility inference, and social learning. In summary, we argue that the
next generation of AI must embrace "dark" humanlike common sense for solving
novel tasks.
