Robotics is a unique educational tool for many reasons including its ability to inspire students and motivate them to be creative. This paper presents our experiences in designing and teaching introductory robotics courses in Qatar and Ghana, two contexts in which robotics is not established and computing technology is in its early stages of impact. We discuss the motivation, challenges, approach, impact, similarities and differences in teaching robotics in these two settings. We highlight lessons learned from these experiences that are generally applicable to robotics education in emerging technology regions.
In recent years, the availability of massive data sets and improved computing power have driven the advent of cutting-edge machine learning algorithms. However, this trend has triggered growing concerns associated with its ethical issues. In response to such a phenomenon, this study proposes a feasible solution that combines ethics and computer science materials in artificial intelligent classrooms. In addition, the paper presents several arguments and evidence in favor of the necessity and effectiveness of this integrated approach.
Deploying machine-, and in particular deep-learning, (ML/DL) solutions in industry-strength, production quality contexts proves to challenging. This requires a structured engineering approach to constructing and evolving systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML/DL well as a framework for integrating ML/DL components in systems consisting of multiple types of components. In addition, we provide an overview of the engineering challenges surrounding AI/ML/DL solutions and, based on that, we provide a research agenda and overview of open items that need to be addressed by the research community at large.
To learn more about cutting-edge data science tools like Apache Kafka, check out the Strata Data Conference in San Jose, March 5-8, 2018--registration is now open. Machine learning has become mainstream, and suddenly businesses everywhere are looking to build systems that use it to optimize aspects of their product, processes or customer experience. The cartoon version of machine learning sounds quite easy: you feed in training data made up of examples of good and bad outcomes, and the computer automatically learns from these and spits out a model that can make similar predictions on new data not seen before. What could be easier, right? Those with real experience building and deploying production systems built around machine learning know that, in fact, these systems are shockingly hard to build. This difficulty is not, for the most part, the algorithmic or mathematical complexities of machine learning algorithms. Creating such algorithms is difficult, to be sure, but the algorithm creation process is mostly done by academic researchers.