One of the best tools Microsoft currently has in its AI toolkit is the QnA Maker. It uses NLP to mine one or more source documents and expose the contents as a chatbot. It does a great job of answering, but a newly added feature (Multi-Trun) mimics a crucial ability that a real human adds: the ability to clarify, ask for more information, or do anything more than a one-off question-response type of conversation.

In this article, Matt Wade examines this feature and how to exploit it to make your QnA bots even smarter.

But that all changed with the recent introduction of QnA Maker multi-turn conversations. With multi-turn, the experience with your QnA KB is much more fluid and significantly more natural. Let’s see how.

Python has quickly grown to be the de facto language for AI and a leading language of Data Science. Its support is so widespread, however, that developers have a choice of a wide array of open source libraries. Here’s a great round up of 24 of the best.

In fact, there are so many Python libraries out there that it can become overwhelming to keep abreast of what’s out there. That’s why I decided to take away that pain and compile this list of 24 awesome Python libraries covering the end-to-end data science lifecycle.

David Giard recently posted a how-to article on creating an Azure DataBricks service. Check it out!

Azure Databricks is a web-based platform built on top of Apache Spark and deployed to Microsoft’s Azure cloud platform. Databricks provides a web-based interface that makes it simple for users to create and scale clusters of Spark servers and deploy jobs and Notebooks to those clusters. Spark provides a […]