{"id":9339,"date":"2018-05-31T08:27:19","date_gmt":"2018-05-31T13:27:19","guid":{"rendered":"https:\/\/vieux.ivado.ca\/?post_type=tribe_events&p=9339"},"modified":"2020-02-21T15:16:35","modified_gmt":"2020-02-21T20:16:35","slug":"software-engineering-for-machine-learning-applications-semla","status":"publish","type":"tribe_events","link":"https:\/\/vieux.ivado.ca\/en\/events\/software-engineering-for-machine-learning-applications-semla\/","title":{"rendered":"Software Engineering for Machine Learning Applications (SEMLA)"},"content":{"rendered":"

Polytechnique Montreal, June 12 \u2013 13, 2018<\/h3>\n

The Software Engineering for Machine Learning Applications (SEMLA) initiative, to be held on June 12 and 13 2018,\u00a0aims 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.<\/p>\n

\"\"<\/p>\n

From the early attempts in the late 80s (such as the\u00a0MAIA<\/a>\u00a0project) 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\u00a0Broken: The human toll of Michigan\u2019s unemployment fraud saga<\/a>), or the recent finding that simple tweaks can fool neural networks in identifying street signs (see\u00a0Robust Physical-World Attacks on Deep Learning Visual Classification).<\/p>\n

The increasing concern of machine learning impacting people\u2019s lives found a strong advocate in Prof. David Parnas, who expressed his concern in\u00a0an ACM communication article<\/a>. These challenges are also reflected in new IEEE standardization initiatives. With data science and deep learning becoming increasingly pervasive in the contemporary world, it is now imperative to engage software engineers and machine learning experts in in-depth conversations about the necessary perspectives, approaches and roadmaps to address these challenges and concerns.<\/p>\n

In particular, we are interested in (but not limited to) discussing the following topics concerning\u00a0software-intensive machine learning applications<\/strong>\u201cin the wild\u201d:<\/p>\n