AI-based tools have transformed from a vague, futuristic vision into actual products that are used to make real-life decisions. Still, for most people, the inner workings of deep-learning systems remain a mystery. If you don’t know what exactly is going on while the input data is fed through layer after layer of a neural network, how are you supposed to test the validity of the output? It’s not magic; it’s just testing.
Eran Kinsbruner, mobile evangelist at Perfecto, discusses his new book and how to be successful in continuous and web testing. He talks about the importance of moving from responsive to progressive web development and taking it to the next level. Eran also shares his insights on AI and machine learning and the element of trust involved with each.
Jeremias Rößler, founder of ReTest, discusses his company’s open source re-check tool, how customer input was vital to the tool’s development, and shares insight on growing a start-up. Jeremias also provides resources for learning about AI that can guide you on how to apply AI into your testing strategy.
In this interview, Gil Sever, the cofounder and CEO of Applitools, explains the importance of automation in modern testing, why you need to be customer-obsessed, and how your UI can determine the success of your applications.
In this interview, Daria Mehra, the director of quality engineering at Quid, explains how people can use machine learning to better contextualize data, details the complexity of test automation and how to be sure you have enough test coverage, and defines the term “artisanal testing.”
Testing artificial intelligence- and machine learning-based systems presents two key challenges. First, the same input can trigger different responses as the system learns and adapts to new conditions. Second, it tends to be difficult to determine exactly what the correct response of the system should be. Such system characteristics make test scenarios difficult to set up and reproduce and can cause us to lose confidence in test results. Yury Makedonov will explain how to test AI/ML-based systems by combining black box and white box testing techniques. His "gray box" testing approach leverages information obtained from directly accessing the AI’s internal system state. Yury will demonstrate the approach in the context of testing a simplified ML system, then discuss test data challenges for AI using pattern recognition as an example and share how data-handling techniques can be applied to testing AI.
AI is here. Will it take over your job? Is it possible to make it beneficial, not detrimental to your career? Kevin Pyles and his team jumped right into the AI universe. Untrained and inexperienced, they realized immediately that they knew nothing.
The machine learning age is well underway. Today’s software can see novel patterns that humans are unable to see and improve task performance based on experience. Learning algorithms are widely used for varied purposes, including loan approval, intrusion detection, fraud prevention, risk...