Add Theano section in introduction

doc/introduction.rst

@@ -36,11 +36,22 @@ limited to:
 
 For more information see :ref:`optimizations`.
 
-The library that Aesara is based on, Theano, was written at the LISA_ lab to
-support rapid development of efficient machine learning algorithms. Theano was
-named after the `Greek mathematician`_, who may have been Pythagoras' wife.
-Aesara is an alleged daughter of Pythagoras and Theano.
+Theano
+-----------------
 
+The library that Aesara is based on, Theano, was written at the LISA lab to support rapid development of efficient machine learning algorithms but while Theano was commonly referred to as a "deep learning" (DL) library, Aesara is not a DL library.
+
+Designations like "deep learning library" reflect the priorities/goals of a library; specifically, that the library serves the purposes of DL and its computational needs. Aesara is not explicitly intended to serve the purpose of constructing and evaluating DL models, but that doesn't mean it can't serve that purpose well.
+
+The designation "tensor library" is more apt, but, unlike most other tensor libraries (e.g. TensorFlow, PyTorch, etc.), Aesara is more focused on what one might call the symbolic functionality.
+
+Most tensor libraries perform similar operations to some extent, but many do not expose the underlying operations for use at any level other than internal library development. Furthermore, when they do, many libraries cross a large language barrier that unnecessarily hampers rapid development (e.g. moving from Python to C++ and back).
+
+If you follow the history of this project, you can see that it grew out of work on PyMC, and PyMC is a library for domain-specific (i.e. probabilistic modeling) computations. Likewise, the other ``aesara-devs`` projects demonstrate the use of Aesara graphs as an intermediate representation (IR) for a domain-specific language/interface (e.g. `aeppl <https://github.com/aesara-devs/aeppl>`_ provides a graph representation for a PPL) and advanced automations based on IR (e.g. `aemcmc <https://github.com/aesara-devs/aemcmc>`_ as a means of constructing custom samplers from IR, ``aeppl`` as a means of automatically deriving log-probabilities for basic tensor operations represented in IR).
+
+This topic is a little more advanced and doesn't really have parallels in other tensor libraries, but it's one of the things that Aesara uniquely facilitates.
+
+The PyMC/probabilistic programming connection is similar to the DL connection Theano had, but—unlike Theano—we don't want Aesara to be conflated with one of its domains of application—like probabilistic modeling. Those primary domains of application will always have some influence on the development of Aesara, but that's also why we need to avoid labels/designations like "deep learning library" and focus on the functionality, so that we don't unnecessarily compromise Aesara's general applicability, relative simplicity, and/or prevent useful input/collaboration from other domains.
 
 Sneak peek
 ==========