The strongest, most central claim of the current paper is that if we don't do something 
analogous to this, we will not succeed in the quest for robust intelligence. If our AI 
systems do not represent and reason over detailed, structured, internal models of the 
external world, drawing on substantial knowledge about the world and its dynamics, 
they will forever resemble GPT-2: they will get some things right, drawing on vast 
correlative databases, but they won't understand what's going on, and we won't be able 
to count on them, particularly when real world circumstances deviate from training 
data, as they so often do.4   

What computational prerequisites would we need in order to have systems that are 
capable of reasoning in a robust fashion about the world? And what it would take to 
bridge the worlds of deep learning (primarily focused on learning) and classical AI 
(which was more concerned with knowledge, reasoning, and internal cognitive 
models)?
The Next Decade in AI-