It has been sixty-seven years since Newell and Simon wrote the first AI program. AI has moved from a proof of concept or a pilot stage to mainstream in the last four years. A good number of products that we used daily have AI being applied in one form or the other. In the session, I will explain how one can use synthetic data to improve the accuracy of AI applications and the associated benefits.
Takeaways:
George is a performance-driven, innovative, and seasoned technocrat with over two decades of Test Engineering, Product Management, and User Experience experience. Known as a go-to person around anything testing amongst my peers, he has conceptualized and rolled out first-of-kind intelligent quality engineering platform NoSkript to reduce the cost of quality to 15% and accelerate test automation implementation 1.5X. His core areas of expertise are optimizing cost, improving market speed and quality levels by deploying the right set of tools, practices, and platforms. He has worked with several Fortune 500 customers in implementing Agile Testing Practices like Behavior Driven Testing for Functional and Non-Functional Testing, Synthetic Data Generation, Intelligent Reporting, and Bots. During the last decade, He had carried out more than three dozen maturity assessments using digitized frameworks, value stream maps, etc., and have defined roadmaps/strategies for Agile, DevOps, and Cloud Transformation. Automated user story review, self-healing automation engines, bot-enabled data provisioning, intelligent test optimization, production feedback analyzers are some of the use cases that he has implemented for customers using Artificial Intelligence and natural language processing. Several of my customers cut down their test cycle time by implementing BDD-based test automation frameworks, Scriptless automation platforms, intelligent object recognition tools, robotic arms, micro-controllers, and robotic process automation tools.
