Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...
Tariq King is the Chief Scientist at test.ai, where he leads research and development of their core platform for AI-driven testing. Tariq has over fifteen years' experience in software engineering and testing and has formerly held positions as Head of Quality, Director of Quality Engineering, Manager of Software Engineering and Test Architect. Tariq holds Ph.D. and M.S. degrees in Computer Science from Florida International University, and a B.S. in Computer Science from Florida Tech. His areas of research are software testing, artificial intelligence, autonomic and cloud computing, model-driven engineering, and computer science education. He has published over 40 research articles in peer-reviewed IEEE and ACM journals, conferences, and workshops, and has been an international keynote speaker at leading software conferences in industry and academia.