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,...
Philip Daye
Quality Assurance Team Lead
Insider Intelligence
Philip Daye is a seasoned software quality professional with more than 25 years of experience in the field. Currently the QA Team Lead at Insider Intelligence, he has a diverse background as a tester, manager, architect, and leader, and has worked with companies of all sizes to ensure the delivery of high-quality software. Philip is deeply committed to staying current with advances in the field, and actively shares his knowledge and experience with others through speaking engagements at conferences and meetups, as well as by founding internal communities of practice.