STARWEST 2022 Tutorial: A Quality Engineering Introduction to AI and Machine Learning

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Tuesday, October 4, 2022 - 8:30am to 4:30pm

A Quality Engineering Introduction to AI and Machine Learning

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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, and operations.  Join Tariq King as he gives you a quality engineering introduction to testing AI and machine learning. You’ll learn AI and ML fundamentals, including how intelligent agents are modeled, trained and developed. Tariq then dives into approaches for validating ML models offline, prior to release, and online, continuously post-deployment. Engage with other participants to develop and execute a test plan for a live ML-based recommendation system, and experience the practical issues around testing AI first-hand. Tariq wraps up the session with a set of expert-recommended, AI engineering practices to help your organization develop trusted machine learning systems.

Tariq_King
test.ai
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.