Why We Don't Have AGI Yet

TL;DR

This paper discusses the challenges and reasons behind the lack of progress towards achieving Artificial General Intelligence, emphasizing theoretical, methodological, and socio-technical barriers.

Contribution

It highlights key cognitive abilities needed for AGI, critiques statistical approaches, and surveys socio-technical factors hindering progress.

Findings

Purely statistical methods are unlikely to produce AGI

Crucial cognitive abilities for AGI include reasoning and autonomous learning

Socio-technical factors have slowed AGI development

Abstract

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.