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The Software Engineering for Machine Learning Applications (SEMLA) initiative, to be held on June 12 and 13 2018, aims at bringing together leading software engineers, machine learning experts, and practitioners to reflect on and discuss the challenges and implications of building complex data-intensive software systems. From the early attempts in the late 80s (such as the MAIA project) to the most recent breakthroughs in applications of deep learning, the human kind dreams of building machines capable of learning new tasks, adapting to the environment, and evolving. Yet this exploration poses important computational, practical and ethical challenges. Failure to properly address these challenges in such software-intensive systems can lead to catastrophic consequences. Consider, for example, the recent human toll incidence caused by the $47-million Michigan Integrated Data Automated System (MiDAS) (see Broken: The human toll of Michigan’s unemployment fraud saga), or the recent finding that simple tweaks can fool neural networks in identifying street signs (see Robust Physical-World Attacks on Deep Learning Visual Classification).

SEMLA brings together Software Engineers, Machine Learning Application practitioners, to discuss challenges of building complex AI-based software systems.