  The rapid advancement of machine learning techniques has re-energized
research into general artificial intelligence. While the idea of
domain-agnostic meta-learning is appealing, this emerging field must come to
terms with its relationship to human cognition and the statistics and structure
of the tasks humans perform. The position of this article is that only by
aligning our agents' abilities and environments with those of humans do we
stand a chance at developing general artificial intelligence (GAI). A broad
reading of the famous 'No Free Lunch' theorem is that there is no universally
optimal inductive bias or, equivalently, bias-free learning is impossible. This
follows from the fact that there are an infinite number of ways to extrapolate
data, any of which might be the one used by the data generating environment; an
inductive bias prefers some of these extrapolations to others, which lowers
performance in environments using these adversarial extrapolations. We may
posit that the optimal GAI is the one that maximally exploits the statistics of
its environment to create its inductive bias; accepting the fact that this
agent is guaranteed to be extremely sub-optimal for some alternative
environments. This trade-off appears benign when thinking about the environment
as being the physical universe, as performance on any fictive universe is
obviously irrelevant. But, we should expect a sharper inductive bias if we
further constrain our environment. Indeed, we implicitly do so by defining GAI
in terms of accomplishing that humans consider useful. One common version of
this is need the for 'common-sense reasoning', which implicitly appeals to the
statistics of physical universe as perceived by humans.

Artificial Intelligence