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Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies, believe that data-driven and intelligent solutions are necessary. Unfortunately, current artificial intelligence (AI) and machine learning (ML) technologies are not sufficiently democratized - building complex AI and ML systems requires deep expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low level of abstraction. It also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning. Moreover, there is a lack of theoretical understanding that could be used to abstract away these subtleties. Conventional programming languages and software engineering paradigms have also not been designed to address challenges faced by AI and ML practitioners. In 2016, companies invested $26–39 billion in AI and McKinsey predicts that investments will be growing over the next few years. Any AI/ML-based systems will need to be built, tested, and maintained, yet there is a lack of established engineering practices in industry for such systems because they are fundamentally different from traditional software systems. This Dagstuhl Seminar brought together two rather disjoint communities together, software engineering and programming languages (PL/SE) and artificial intelligence and machine learning (AI-ML) to discuss open problems on how to improve the productivity of data scientists, software engineers, and AI-ML practitioners in industry.