taken as bedrock in generative linguistics) and records of individuals and their 
properties. 

What it lacks is a satisfactory framework for learning. Hybrids could be a way of 
combining the best of both worlds: the capacity to learn from large scale data sets, as 
exemplified by deep learning, with the capacity to represent abstract representations 
that are syntactic and semantic currency of all the world's computer programming 
languages. I conjecture that they are a prerequisite for safely arriving at robust 
intelligence. 

Far fewer resources have gone into studying the universe of hybrid models than have 
gone into "pure" deep learning systems that eschew symbol-manipulating, but the 
growing strand of work reviewed in section 2.1.2, from a wide range of research labs, to 
say nothing of the success of Google Search, all point to the value of greater study of 
hybrid architectures.
The Next Decade in AI-