  The field of machine learning has focused, primarily, on discretized
sub-problems (i.e. vision, speech, natural language) of intelligence. While
neuroscience tends to be observation heavy, providing few guiding theories. It
is unlikely that artificial intelligence will emerge through only one of these
disciplines. Instead, it is likely to be some amalgamation of their algorithmic
and observational findings. As a result, there are a number of problems that
should be addressed in order to select the beneficial aspects of both fields.
In this article, we propose leading questions to guide the future of artificial
intelligence research. There are clear computational principles on which the
brain operates. The problem is finding these computational needles in a
haystack of biological complexity. Biology has clear constraints but by not
using it as a guide we are constraining ourselves.
