Tariq King from Test IO will explore how generative AI can enhance quality across the software lifecycle. 
He discusses AI’s role in testing, the importance of checks and balances, and the challenges in validating AI systems.
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Hi there,
If you couldn’t catch the session live, don’t worry! You can watch the recording here:
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Here are some of the Q&As from this session:
How can organizations strike a balance between AI-driven automation and conventional testing methods to ensure comprehensive coverage and reliability?
Tariq King: Organizations can balance AI-driven automation and conventional testing by using AI to handle repetitive, large-scale tasks and automate complex scenarios, while relying on traditional methods for nuanced, exploratory testing. Combining both approaches ensures comprehensive coverage, as AI excels in speed and efficiency, whereas conventional testing provides depth and context.
How do you see, is there any initiative to work on the Ethical part of AI?
Tariq King: Yes, there are ongoing initiatives to address the ethical aspects of AI. These include developing ethical guidelines, ensuring transparency, and promoting fairness in AI algorithms. Organizations and research groups are actively working on frameworks and standards to address biases, privacy concerns, and the responsible use of AI technologies.
What does “automating quality beyond AI” mean, and how does it differ from traditional automation approaches?
How much does a traditional team reduce with an AI integration like yours? And, which is the timeframe where humans will just monitor what AI creates/tests/auto-corrects, etc.?
How do we best navigate beyond AI for testing with the pressure of the industry forcing QA to be replaced or shrunk in teams having fewer QAs per team?
Does this mean I shouldn’t bother learning JAVA and Selenium?
How can you introduce the concept of AI in your organization and convince your manager to progress towards AI testing?
When should manual testing be used in addition to, or in replacement of, automated or AI-powered testing?
Is there something by which we can balance between AI-driven automation and conventional testing?
How can we implement the concept of AI in our organization and improve the quality of the services?
I believe you, but how soon until AI just does everything then?
What are approaches to automation that go beyond current AI capabilities, and how can we integrate them into existing testing frameworks?
What specific strategies or frameworks can be used for automating the testing of AI-ML models to ensure their accuracy, reliability, and performance?
What do you think the landscape of automated testing with AI might look like even one year from now?
Can Gen AI help us to remove mundane and repetitive test cases?
How to speed up testing with AI?
How to use DevIn AI in software testing?
If AIs are becoming developers, shouldn’t there be an increased need for QA activity from sapiens?
