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Before the COVID-19 pandemic, video was already one of the main media used on the internet. During the pandemic, video conferencing services became even more important, coming to be one of the main instruments to enable most social and professional human activities. Given the social distancing policies, people are spending more time using these online services for working, learning, and also for leisure activities. Videoconferencing software became the standard communication for home-office and remote learning. Nevertheless, there are still a lot of issues to be addressed on these platforms, and many different aspects to be reexamined or investigated, such as ethical and user-experience issues, just to name a few. We argue that many of the current state-of-the-art techniques of Artificial Intelligence (AI) may help on enhancing video collabo- ration services, particularly the methods based on Deep Learning such as face and sentiment analyses, and video classification. In this paper, we present a future vision about how AI techniques may contribute to this upcoming videoconferencing-age.

Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual information, exhibit some limitations, such as being error-prone to manually created keywords or due to imprecise speech recognition. This paper presents a method for generating educational video recommendation using deep facefeatures of lecturers without identifying them. More precisely, we use an unsupervised face clustering mechanism to create relations among the videos based on the lecturer's presence. Then, for a selected educational video taken as a reference, we recommend the ones where the presence of the same lecturers is detected. Moreover, we rank these recommended videos based on the amount of time the referenced lecturers were present. For this task, we achieved a mAP value of 99.165%.

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