  The original vision of AI was re-articulated in 2002 via the term 'Artificial
General Intelligence' or AGI. This vision is to build 'Thinking Machines' -
computer systems that can learn, reason, and solve problems similar to the way
humans do. This is in stark contrast to the 'Narrow AI' approach practiced by
almost everyone in the field over the many decades. While several large-scale
efforts have nominally been working on AGI (most notably DeepMind), the field
of pure focused AGI development has not been well funded or promoted. This is
surprising given the fantastic value that true AGI can bestow on humanity. In
addition to the dearth of effort in this field, there are also several
theoretical and methodical missteps that are hampering progress. We highlight
why purely statistical approaches are unlikely to lead to AGI, and identify
several crucial cognitive abilities required to achieve human-like adaptability
and autonomous learning. We conclude with a survey of socio-technical factors
that have undoubtedly slowed progress towards AGI.
