MULTIDRONE aims to develop an innovative, intelligent, multi-drone platform for media production to cover outdoor events, which are typically held over wide areas (at stadium/city level). The 4-10 drone team, to be managed by the production director and crew, will have: a) increased decisional autonomy, by minimizing production crew load and interventions and b) improved robustness, security and safety mechanisms (e.g., embedded flight regulation compliance, enhanced crowd avoidance, autonomous emergency landing, communications security), enabling it to carry out its mission even against adverse conditions or crew inaction and to handle emergencies. Such robustness is particularly important, as the drone team has to operate close to crowds and may face an unexpected course of events and/or environmental hazards. Therefore, it must be contextually aware and adaptive with improved perception of crowds, individual people and other hazards. As this multi-actor system will be heterogeneous, consisting of multiple drones and the production crew, serious human-in-the-loop issues will be addressed to avoid operator overload, with the goal of maximizing shooting creativity and productivity, whilst minimizing production costs. Overall, MULTIDRONE will boost research on multiple-actor systems by proposing novel multiple-actor functionalities and performance metrics. Furthermore, the overall multidrone system will be built to serve identified end user needs. Specifically, innovative, safe and fast multidrone audiovisual shooting will provide a novel multidrone cinematographic shooting genre and new media production techniques that will have a large impact on the financially important EU broadcasting/media industry. It will boost production creativity by allowing the creation of rich/novel media output formats, improving event coverage, adapting to event dynamics and offering rapid reaction speed to unexpected events.

The traditional media landscape is in the middle of a monumental shift: boundaries between professional media houses, prosumers and small creators are blurring, the speed of communication and publishing is increasing, audiences are seeking more user-driven and accessible multimedia experiences. MediaVerse (MV) is an answer to this shift: a decentralized network for intelligent, automated, and accessible digital asset management systems, where traditional stakeholders and other media owners can share, enrich, verify, and monetize multimedia content. With this, MV aspires to disrupt current practices of working in isolated silos, while enabling secure and traceable media exchange. An instance of MV can be deployed as a node in the decentralized MV network, and provides its users with a blockchain-enabled rights management solution, AI-powered identification services and smart contracts that automate cross-network rights negotiation and content monetization. Following the European way, MV aspires to help users create and publish content, while keeping control over the content shared. For this, several AI-driven features are implemented towards supporting automated editorial content moderation and identification, authoring immersive experiences, making content accessible, and providing actionable insights through social analytics and collaboration tools. Integrated content adaptation services ensure the distribution of the created media over several networks, platforms and end-devices. The project will be validated through large scale pilots in the field of citizen journalism and co-creation of immersive and inclusive media, foreseeing also a STARTS-inspired use case that involves artistic experiments and practices, pushing forward creativity for a critical approach to user-centric media.

SELMA builds a continuous deep learning multilingual media platform using extreme analytics. Large amounts of multilingual text and speech data are available in the internet, but the potential to fully take advantage of this data has remained largely untapped. Recent advances in deep learning and transfer learning have opened the door to new possibilities – in particular integrating knowledge from these large unannotated datasets into plugable models for tackling machine learning tasks. The aim of the Stream Learning for Multilingual Knowledge Transfer (SELMA) is to address three tasks: ingest large amounts of data and continuously train machine learning models for several natural language tasks; monitor these data streams using such models to improve multilingual Media Monitoring (use case 1); and improve the task of multilingual News Content Production (use case 2), thereby closing the loop between content monitoring and production. SELMA has eight goals: 1. Enable processing of massive video and text data streams in a distributed and scalable fashion 2. Develop new methods for training unsupervised deep learning language models in 30 languages 3. Enable knowledge transfer across tasks and languages, supporting low-resourced languages 4. Develop novel data analytics methods and visualizations to facilitate the media monitoring decision-making process 5. Develop an open-source platform to optimize multilingual content production in 30 languages 6. Fine-tune deep learning models from user feedback, reducing recurring errors 7. Ensure a sustainable exploitation of the SELMA platform 8. Encourage active user involvement in the platform. Achieving these aims requires advancing the state of the art in multiple technologies (transfer learning, language modelling, speech recognition, machine translation, summarization, speech synthesis, named entity linking, learning from user feedback), while building upon previous project results and existing services.

Motivated by the challenges, risks and opportunities that the wide use of AI brings to media, society and politics, AI4Media aspires to become a centre of excellence and a wide network of researchers across Europe and beyond, with a focus on delivering the next generation of core AI advances to serve the key sector of Media, to make sure that the European values of ethical and trustworthy AI are embedded in future AI deployments, and to reimagine AI as a crucial beneficial enabling technology in the service of Society and Media. The AI4Media consortium, comprising 30 leading partners in the areas of AI and media (9 universities, 9 research centres, 12 industrial partners) and 35 associate members, will establish the networking infrastructure to bring together the currently fragmented European AI landscape in the field of media, and foster deeper and long-running interactions between academia and industry, including Digital Innovation Hubs. It will also shape a research agenda for media AI research, and implement research and innovation both with respect to cutting-edge technologies at the core of AI research, and within specific fields of media-related AI. AI4Media will provide a targeted funding framework through open calls, to speed up the uptake of innovations developed within the network. A PhD programme will further enhance links to the industry and the fostering and exchange of talent, while providing motivation to prevent brain drain, and a set of use cases will be developed by the network to demonstrate the impact of the achieved advances in the media sector. The Excellence Centre that is established during the AI4Media project, and the ecosystem that will grow around it, will provide a long-term basis for the support of AI excellence in Europe, long after the project end, with the aim of ensuring that Ethical AI guided by European values assumes a global leading role in the field of Media.