In the previous post, we gave an overview of how ZUJYA helps in validating predictive models. While ZUJYA is built to validate machine learning based predictive models, it can also be used along with standard automation frameworks that are used for Functional/Non-Functional Testing.
For example, Zuci’s EPIQ (Engineering Productivity Improvements for Quality) integrates with ZUJYA in validating functional tests. EPIQ is built on Java and includes features for code coverage and unit testing using tools such as Cobertura and TestNG.
In the first part of this blog series, we gave a general introduction to Artificial Intelligence, Machine Learning, Deep Learning and Robotics but for the most part we have discussed about Machine Learning. Let’s have a quick overview on Robotics and Deep Learning.
Well, Robotics in Software or Chatbots, as they are called, are designed to automate repetitive tasks. For example, our chatbot titled, “ZUBOT” helps customers in the Fintech, Retail, and Marketing sectors to perform repetitive tasks such as acting as virtual agents, performing data extraction at specified intervals and feeding them into other systems etc.
Deep Learning is again part of the machine learning family of algorithms. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. Recently, we have seen a lot of AI based facial recognition features etc., which are areas where Deep Learning is applied.
We believe that the four-part series on “Testing Artificial Intelligence systems: Myth vs Reality” has given a broad overview on Machine Learning, Predictive Models and the approach to validate them.
(to be continued …)
Testing Artificial Intelligence systems – Myth vs Reality (Part 4)
Testing Artificial Intelligence systems – Myth vs Reality (Part 3)
Testing Artificial Intelligence systems – Myth vs Reality (Part 2)
Machine Learning and Artificial Intelligence: Software Testing to get “smarter”?
Who is Brad Parscale?