A plausible definition of "reasoning" could be "algebraically manipulating
previously acquired knowledge in order to answer a new question". This
definition covers first-order logical inference or probabilistic inference. It
also includes much simpler manipulations commonly used to build large learning
systems. For instance, we can build an optical character recognition system by
first training a character segmenter, an isolated character recognizer, and a
language model, using appropriate labeled training sets. Adequately
concatenating these modules and fine tuning the resulting system can be viewed
as an algebraic operation in a space of models. The resulting model answers a
new question, that is, converting the image of a text page into a computer
readable text.
This observation suggests a conceptual continuity between algebraically rich
inference systems, such as logical or probabilistic inference, and simple
manipulations, such as the mere concatenation of trainable learning systems.
Therefore, instead of trying to bridge the gap between machine learning systems
and sophisticated "all-purpose" inference mechanisms, we can instead
algebraically enrich the set of manipulations applicable to training systems,
and build reasoning capabilities from the ground up.

通过代数操作简单的学习系统来建立推理能力，而非试图在机器学习系统和复杂的推理机制之间建立联系。