AEGIS, brings together the data, the network & the technologies to create a curated, semantically enhanced, interlinked & multilingual repository for public & personal safety-related big data. It delivers a data-driven innovation that expands over multiple business sectors & takes into consideration structured, unstructured & multilingual datasets, rejuvenates existing models and facilitates organisations in the Public Safety & Personal Security linked sectors to provide better & personalised services to their users. AEGIS will introduce new business models through the breed of an open ecosystem of innovation & data sharing principles. From the technology perspective, AEGIS targets to revolutionise semantic technologies in big data, big data analytics & visualisations as well as security & privacy frameworks by addressing current challenges & requirements of cross-domain & multilingual applications. The main benefits derived from AEGIS to data identification, collection, harmonisation, storage & utilisation towards value generation for these sectors will be: Unified representation of knowledge; Accelerated, more effective & value-packed cycles of intelligence extraction & of services & applications development; Introduction of novel business models for the data sharing economy & establishment of AEGIS as a prominent big data hub, utilising cryptocurrency algorithms to validate transactions & handle effectively IPRs, data quality & data privacy issues though a business brokerage framework. Based on an early market analysis, the Total Addressable Market of AEGIS is up to $31bn (€27.1bn); AEGIS is able not only to capture a portion of the market size, but also to expand the pie through creating additional uncaptured value based on small data integration in typical big data repositories & algorithms. Based on the same analysis, the project will break even & will be viable from its launch (2020) & will have a ROI investment of EU-commission in the first years.

In parallel to the current developments in the so-called narrow artificial intelligence (AI) realm, there is an urgent demand for more universal, general AI approaches that can operate across a wider spectrum of application domains with varying data characteristics. It is expected that the emerging sustainable AI methods can be efficiently deployed in the edge-cloud continuum on different hardware platforms and computing infrastructure depending on the real-world task scenarios and constraints including the limited energy budget. In response to this growing demand and emerging trends we propose to adopt a brain-like approach to AI system design due to its promising potential for functional flexibility, hardware friendliness as well as energy efficiency among others. To this end, EXTRA-BRAIN is aimed at developing a new generation of AI solutions based on brain-like neural networks that enable us to overcome key limitations of the current state-of-the-art methods, exemplified by deep learning, such as limited cross-task generalisation and extrapolation to novel domains (bounded reliability), excessive dependence on costly annotated data as well as extensive training and validation processes with heavy demand for compute resources at high energy cost, to name a few. The core brain-like neural network design in our approach derives from the accumulated computational neuroscience insights into the brain's working principles of information processing, key learning schemes and neuroanatomical structures that underlie the brain's perceptual/cognitive phenomena and its functional flexibility. Furthermore, these novel models are supported by data optimisation pipelines, which improve data quality, security and reduce the costs of assembling suitable training data, and an explainability framework to empower the human user. The proposed EXTRA-BRAIN framework will be examined in a diverse set of use cases with different hardware demands in the edge-cloud continuum.

Triple-A has a very practical result-oriented approach, seeking to answer three questions: - How to assess the financing instruments and risks an early stage? - How to agree on the Triple-A investments, based on selected key performance indicators? - How to assign the identified investment ideas with possible financing schemes? The Triple-A scheme is introduced, compromising three critical steps (answering each question), with the following main outputs: - Step 1 - Assess: Member States (MS) risk profiles and mitigation polices, including a Web based database, enabling national and sectoral comparability, market maturity identification, good practices experiences exchange, reducing thus uncertainty for investors. - Step 2 - Agree: Standardised Triple-A tools, efficient benchmarks, and guidelines, translated in consortium partners’ languages, accelerating and scaling up investments. - Step 3 - Assign: In-country demonstrations, replicability and overall exploitation, including recommendations on realistic and feasible investments in the national and sectoral context, as well as on short and medium term financing. The Triple-A case study countries were selected to promote diversity across a number of factors, including: a leading European economy (Germany), an innovation front-runner in energy (The Netherlands), a weak economy, went through one of the longest and most severe recessions (Greece), an economy with slow economic recovery (Italy), a diversified economy with a strategic geographical location having some of the largest European firms (Spain), a country that has experienced one of the fastest economic recoveries in Europe (Lithuania), a progressing country with a once sceptical stance towards low-carbon development (Czech Republic), and a country, recovering from a slow transition to a market economy, with growing regional strategic role and significant ambition towards EU processes (Republic of Bulgaria).

OBJECTIVES: Build on multi-discipline research (e.g., human-centred methodology integrates cognitive models, ergonomics, understanding of worker’s well being) to accelerate how we identify, acquire and exploit skills valued by industry. Get high take-up by early adopters (e.g., in manufacturing). Augment training in situ with live expert guidance, a tacit learning experience and a re-enactment of the expert, in knowledge-intensive environments where effective decision making, often in new situations, has high impact on effectiveness in production. Bring learning content and technical documentation to life via task-sensitive Augmented Reality (AR). Make final products flexible for workplace integration via industry-standard repositories and toolkits. HOW: Wearable TEL platform enhances human abilities to acquire procedural knowledge by providing a smart system that directs attention to where it is most needed. An extensive audit of industry procedures, policies and participatory design methods will define the main facets of the platform. User test cycles will refine prototypes and deliverables. Existing wearable smart devices and sensors will be tailored to provide an innovative solution for content delivery and measurement of user performance. Comparative tests, stakeholders’ review and leading the IEEE AR group will secure high-standard academic and industrial outputs. RELEVANCE to work programme: WEKIT is strongly aligned with EU job/training policies (e.g., Grand Coalition for Digital Jobs). It enhances the industrial value chain, reduces fragmentation/cost and improves efficiencies with impact regarding speed and scale in production. Looking ahead: roadmap shows safe skill pathways for use of TEL in changing industrial landscapes (e.g. smart machine-to-machine (M2M) knowledge-sharing). Smarter products and services will improve workflows, enhancing (re)training of workers whose skill sets need upgrading after ‘Industry 4.0’.