Abstract. In this paper we describe the strategy under adoption in Telecom Italia (TI) Technology Department toward open source software. This stems from trying to create synergy among big Telco Player to increase knowledge and influence over strategic communities to the evaluation of the creation of new communities over internally developed applications. In particular here the approach and the expectations in starting the community on WADE (Workflow and Agent Development Environment) is described. This is a platform used to develop mission critical applications and is the main evolution of JADE a popular Open Source framework for the development of interoperable intelligent multi-agent systems. It adds to JADE the support for the execution of tasks defined according to the workflow metaphor as well as a number of mechanisms that help managing the complexity of the distribution both in terms of administration and fault tolerance. The idea is to use WADE as a mean to gather critical information on the opportunity of approaching OS as a strategic mean toward the development of always more important application in Operating Support System for TI, possibly also involving other great Telco Players For this reason great care is being paid in setting up the Community environment and in deciding which metrics are to be extracted from it, since the result will be the input for a strategic decision in TI.

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.

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