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Due to the advent of big data and efficient computational resources, artificial intelligence (AI) and machine learning (ML) have seen massive growth in recent years. Informatics degree programs are scrambling to meet the ever-increasing market demand of such professions. To explore the skillsets required for AI and ML positions, the authors conducted a content analysis of online job advertisements posted on Indeed.com . They present a ranking of the relevant skills for the two positions. Further, they performed a pairwise comparison between AI and ML positions. Overall, it was observed that technical skills like data mining, programming, statistics and big data are more valued for ML positions than for AI positions. On the contrary, AI positions tend to be more generic, with an emphasis on communication skills. These clearly defined skills can be valuable for the hiring process as well as to revamp existing course curricula to cater to the increasing market demand.

Sugar mills in Brazil represent significant capital investments. To maintain appropriate returns on their investment, sugar companies seek to run the mills at capacity over the entire nine months of the sugarcane harvest season. Because the sugar content of cane degrades considerably once it is cut, maintaining inventories of cut cane is undesirable. Instead, mills want to coordinate the arrival of cut cane with production. In this paper, we present a model of the sugarcane harvest logistics problem in Brazil. We introduce a series of valid inequalities for the model, introduce heuristics for finding an initial feasible solution, and for lifting the lower bound. Computational results demonstrate the effectiveness of the inequalities and heuristics. In addition, we explore the value of allowing trucks to serve multiple rather than single locations and demonstrate the value of allowing the harvest speed to vary.

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