  An important aspect of artificial intelligence (AI) is the ability to reason
in a step-by-step "algorithmic" manner that can be inspected and verified for
its correctness. This is especially important in the domain of question
answering (QA). We argue that the challenge of algorithmic reasoning in QA can
be effectively tackled with a "systems" approach to AI which features a hybrid
use of symbolic and sub-symbolic methods including deep neural networks.
Additionally, we argue that while neural network models with end-to-end
training pipelines perform well in narrow applications such as image
classification and language modelling, they cannot, on their own, successfully
perform algorithmic reasoning, especially if the task spans multiple domains.
We discuss a few notable exceptions and point out how they are still limited
when the QA problem is widened to include other intelligence-requiring tasks.
However, deep learning, and machine learning in general, do play important
roles as components in the reasoning process. We propose an approach to
algorithm reasoning for QA, Deep Algorithmic Question Answering (DAQA), based
on three desirable properties: interpretability, generalizability, and
robustness which such an AI system should possess, and conclude that they are
best achieved with a combination of hybrid and compositional AI.
