The Humane AI initiative will develop the scientific foundations and technological breakthroughs needed to shape the ongoing artificial intelligence (AI) revolution. The goal is to design and deploy AI systems that enhance human capabilities and empower both individuals and society as a whole to develop AI that extends rather than replaces human intelligence. This vision fits very well into the ambitions articulated by the EC in its Communication on AI but cannot be achieved by legislation or political directives alone. Instead it needs fundamentally new solutions to core research problems in AI and human-computer interaction (HCI), especially to help people understand actions recommended or performed by AI systems. Challenges include: learning complex world models; building effective and fully explainable machine learning systems; adapting AI systems to dynamic, open-ended real-world environments (in particular robots and autonomous systems in general); achieving in-depth understanding of humans and complex social contexts; and enabling self-reflection within AI systems. The focus is on human-centered AI, with a strong emphasis on ethics, values by design, and appropriate consideration of related legal and social issues. The HumanE AI project will mobilize a research landscape far beyond the direct project funding and create a unique innovation ecosystem that offers substantial return on investment. It will result in significant disruption across its socio-economic impact areas, including Industry 4.0, health & well-being, mobility, education, policy and finance. It will spearhead the efforts required to help Europe achieve a step-change in AI uptake across the economy. The consortium, with 35 partners from 17 countries, including four large industrial members, will define the details of all aspects necessary to implement future projects in the topic, and mobilize major scientific, industrial, political and public support for the vision.

BigDataFinance, a Marie Skłodowska-Curie Innovative Training Network “Training for Big Data in Financial Research and Risk Management”, provides doctoral training in sophisticated data-driven risk management and research at the crossroads of Finance and Big Data for 13 researchers. The main objectives are i) to meet an increasing commercial demand for well-trained researchers experienced in both Big Data techniques and Finance and ii) to develop and implement new quantitative and econometric methods for empirical finance and risk management with large and complex datasets. To achieve the objectives, the emphasis is put on exploiting big data techniques to manage and use datasets that are too large and complex to process with conventional methods. Banks and other financial institutions must be able to manage, process, and use massive heterogeneous data sets in a fast and robust manner for successful risk management; nonetheless, financial research and training has been slow to address the data revolution. Compared to the USA, Europe is still at an early stage of adopting Big Data technologies and services. Immediate action is required to seize opportunities to exploit the huge potential of Big Data within the European financial world. This world-class network consists of eight academic participants and six companies, representing banks, asset management companies, and data and solution providers. The proposed research is relevant both academically and practically, because the program is built around real challenges faced both by the academic and private sector partners. To bridge research and practice, all researchers contribute to the private sector via secondments. BigDataFinance provides the European financial community with specialists with state-of-the-art skills in finance and data-analysis to facilitate the adoption of reliable and realistic methods in the industry. This increases the financial strength of banks and other financial institutions in Europe.

The HumanE AI Net brings together top European research centers, universities and key industrial champions into a network of centers of excellence that goes beyond a narrow definition of AI and combines world leading AI competence with key players in related areas such as HCI, cognitive science, social sciences and complexity science. This is crucial to develop a truly Human Centric brand of European AI. We will leverage the synergies between the involved centers of excellence to develop the scientific foundations and technological breakthroughs needed to shape the AI revolution in a direction that is beneficial to humans both individually and societally, and adheres to European ethical values and social, cultural, legal, and political norms. The core challenge is the development of robust, trustworthy AI capable of what “understanding” humans, adapting to complex real-world environments, and appropriately interacting in complex social settings. The aim is to facilitate AI systems that enhance human capabilities and empower individuals and society as a whole while respecting human autonomy and self-determination. The HumanE AI Net project will engender the mobilization of a research landscape far beyond direct project funding, involve and engage European industry, reach out to relevant social stakeholders, and create a unique innovation ecosystem that provides a many fold return on investment for the European economy and society. We will make the results of the research available to the European AI community through the AI4EU platform and a Virtual Laboratory, develop a series of summer schools, tutorials and MOOCs to spread the knowledge, develop a dedicated innovation ecosystem for transforming research and innovation into an economic impact and value for society, establish an industrial Ph.D. program and involve key industrial players from sectors crucial to European economy in research agenda definition and results evaluation in relevant use cases.

Data analytics is one of the main cornerstones in many enterprise architectures and the data lake paradigm is more and more adopted to assist organizations in taking reliable, accurate, and fast decisions. Although the initial approaches to address these issues saw the data lakes as the evolution of data warehouses to be implemented on-premises, cloud providers are nowadays including in their offerings platforms able to setup and run them. Nevertheless, the increasing amount of data generated at the edge and the need to enable the data sharing among organizations are posing new challenges in terms of performances, energy efficiency, and privacy/confidentiality which can be properly addressed with data lakes which are deployed along the whole computing continuum as well as building a federation of such data lakes. The ambition of TEADAL is to provide key cornerstone technologies to create stretched data lakes spanning the cloud-edge continuum and multi-cloud, providing privacy, confidentiality, and energy-efficient data management. The TEADAL data lake technologies will enable trusted, verifiable and energy efficient data flows, both in a stretched data lake and across a trustworthy mediatorless federation of them, based on a shared approach for defining, enforcing, and tracking privacy/confidentiality requirements balanced with the need for energy reduction.