This is What AI Looks Like: BiDAF Model for Question Answering

We show ground-breaking components of machine learning so artificial intelligence is less abstract.

Zetane provides a new environment to work with complex neural networks and datasets, transforming abstract AI projects into tangible concepts. Here is a screenshot of working with the neural network of the BiDAF model that is processing the Stanford Question & Answering Dataset (SQuAD).

We at Zetane are all about democratizing AI, but getting to the laudable goal of empowering more people within AI innovation requires many steps along the way. One beginner’s step on our journey involves peeling away the abstract nature of AI like the skins of an onion. We peel these layers of abstraction away by presenting fundamental components of machine learning in ways technical and non-technical professionals can appreciate. This will be a repeated theme of ours on the Zetane blog, which we call:

This is what AI looks like.

To start us off we present here the Bidirectional Attention Flow model, or BiDAF.

At first glance, this model looks to be a practical attempt to understand elements of unstructured text. Its development in 2016 marked a major step forward in Natural Language Processing (NLP) for Question and Answer tasks. That may at first sound straightforward. This basic task, however, has huge implications in business as a tool to automate important tasks like customer support inquiries or common requests for information. It thus comes as no surprise that this model…