AI-DAPT brings forward a data-centric mentality in AI, that is effectively fused with a model-centric, science-guided approach, across the complete lifecycle of AI-Ops, by introducing end-to-end automation and AI-based systematic methods to support the design, the execution, the observability and the lifecycle management of robust, intelligent and scalable data-AI pipelines that continuously learn and adapt based on their context. AI-DAPT will design a novel AI-Ops / intelligent pipeline lifecycle framework cross-cutting the different business, legal/ethics, data, AI logic/models, and system requirements while always ensuring a human-in-the-loop (HITL) approach across five axis: “Data Design for AI”, “Data Nurturning for AI”, “Data Generation for AI”, “Model Delivery for AI”, “Data-Model Optimization for AI”. AI-DAPT will contribute to the current research and advance the state-of-the-art techniques and technologies across a number of research paths, including sophisticated Explainable AI (XAI)-driven data operations from purposing, harvesting/mining, exploration, documentation and valuation to interoperability, annotation, cleaning, augmentation and bias detection; collaborative feature engineering minimizing the data where appropriate; adaptive AI for model retraining purposes. Overall, AI-DAPT aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions. In order to demonstrate the actual innovation and added value that can be derived through the AI-DAPT scientific advancements, the AI-DAPT results will be validated in two, interlinked axes: I. Through their actual application to address real-life problems in four (4) representative industries: Health, Robotics, Energy, and Manufacturing; II. Through their integration in different AI solutions, either open source or commercial, that are currently available in the market.

The project aims at filling the gap between scientific research on Artificial Intelligence (AI) and Machine Learning (ML) and its industrial application as enabling technology for the I4.0 paradigm. AI and ML improve the data acquisition and analysis typical of the I4.0, leading to the optimization of the industrial processes through fast and well-performing algorithms. The academic research efforts on AI have followed a trend of development of complex algorithms that require cloud-centric architectures while the industrial architectures for data acquisition are in most cases fragmented and resource-constrained. Recent researches have demonstrated the need of a decentralized use of AI and ML where algorithms for data acquisition and analysis are executed directly on the machine side. It is evident that a new generation of AI and ML experts, able to adapt these technologies to the industrial needs and to foster their role as the key players of the 4th industrial revolution, is needed. PLANET4 enables a knowledge transfer between academia and industry by achieving the following objectives: a) design of a b-learning course for the porting and integration of AI techniques in I4.0 applications;b) evaluation of a novel method for the description of industrial digitalization needs and pains aimed at enabling fast identification of the most appropriate AI methodologies; c) formalization of a framework of soft skills and related training materials for 4.0 Innovation and Change Management training workshops;d) development of a portal for the collection and sharing of best practices in the applications.The project approach is cross-disciplinary and focuses on both hard skills in AI and ML technologies and soft competencies needed to manage the changes introduced in the industrial ecosystem. Academics will have the possibility to gather needs and requirements from the industrial world, allowing the adaptation of ML teaching to better fit the real-world industrial pains.

Industry 4.0 is undoubtedly the trending topic in the world of business and innovation since year 2017. It mainly refers to organizational methodologies for the production of goods or services that integrates production systems with digital technologies. Several studies reveal that the opportunities offered by the Industry 4.0 turn out to be an unexplored frontier by the majority of companies, and most of the European companies are not sufficiently skilled to embrace the new paradigm. One of the major obstacles to the development of Industry 4.0 is the lack of digital culture within European companies and its spread can only rely on new training courses and on the hiring of highly specialized professional figures. Within this scenario, SPRINT4.0 proposed to create a comprehensive approach for training and support, aimed at increasing the successful implementation of Industry 4.0 innovations and initiatives in European companies and ventures. SPRINT4.0 courses allowed the students to solve real problems (provided by the target companies) with state of the art technology (ensured by the application providers) in a streamlined and coherent course set (ensured by the close cooperation of the universities).The project enabled students, academics and industrial players to leverage knowledge, practice and innovation on Industry 4.0. They shared space, time and knowledge, and faced together the same challenges. The main project aims were:For Students: to provide students with a full set of skills to increase their employability in Industry 4.0-oriented companies; to provide students with entrepreneurial and intrapreneurial skills to carry out innovative Industry 4.0 related projects as new ventures or within existing companies For the Universities: new teaching skills for education and training in the field of Industry 4.0; three new courses (minimum 3 ECTS each), to enrich university curricula; specific and innovative coaching activities, to support students developing industry-viable proof-of-concepts of projects and ideas. For Companies:co-creating new solutions to real market/process problems and implementing proof-of-concept or even prototype; understanding the existing gap between the present status and the Industry 4.0 best in class; recruiting the best talents. The project delivered the following outputs:Audit methodologies on Industry 4.0 (IO1)3 training courses focusing on Industry 4.0 related topics attended by students in 3 Countries (IO2, IO3, IO4)Coaching methodologies to develop Industry 4.0 applications (IO5)A Ready-to-use Guide to implement Industry 4.0 training courses (IO6) To achieve these challenging objects, a solid consortium at european level was developed. Universities and Research Institutes provided Methodologies & Tools for Research & Education on Industry 4.0, bringing their expertise and knowledge on specific Industry 4.0 fields and their educational tools. The academic partners were: University of Pisa (IT), BIBA-Bremer Institut für Produktion und Logistik (GER) and URL University (SP). Along with the academic partners, the project includeD TOI - Zerynth (Italy) a company that brought new approaches towards Industry 4.0 applications, innovative methods and tools. Fondazione Giacomo Brodolini (IT) guaranteed the experience in the formalization and evaluation of training courses and impacts. The SPRINT4.0 actions was conducted with a clear focus on Target Companies that brought to the project the point of view of real business, the need and willingness for Industry 4.0 applications: BarnaSteel (SP) - a large company working in the steel production sector; Selettra (IT) - a medium/large company operating in the production of electric wiring for the householding industry; OHS (GER) - a small company developing and manufacturing customer-tailored technical systems in the access control area. SPRINT4.0 increased the overall ability of the innovation ecosystem at regional, national and European level to generate skills and innovative solutions in the field of Industry 4.0. In particular, Universities improved their educational offer on Industry 4.0 to respond to the market needs by creating specialized professional profiles. SPRINT4.0 is the way to root in industry (SMEs included) a paradigm that now exists only in the theory or in the large tech companies.