ELISE aims to make Europe competitive in AI through a network of excellence. The best European researchers in machine learning and AI have worked together to attract talent, to foster research through collaboration, and to inspire and be inspired by industry and society. While ELISE starts from machine learning as the current most prominent method of AI, the network invites in all ways of reasoning, considering all types of data, applicable for almost all sectors of science and industry. While being aware of data safety and security, and while striving to explainable and trustworthy outcomes we aim to create a force to Europe.ELISE will run a PhD student and a postdoc programme to attract and to educate world-class talents to Europe. It will operate a Fellows programme for groundbreaking research and high-profile workshops to develop AI methods applications further. Industry involvement is guaranteed by the many connections members of ELISE have with industry, on average one for every member and one start-up for every second member of ELISE. ELISE will demonstrate a fraction of their research in use cases to be implemented in AI4EU and the SMEs of Europe. Additional impact will be created to SMEs through open calls. The current practice of ELISE members of spin-off research in SMEs once a break-through is achieved will be stimulated through incubators. The current practice of participating in dissemination and debate that many members of ELISE are used to will be continued to develop a mature acceptance of AI throughout Europe for the benefit of all and in cooperation with all.ELISE is built on 105 organisations in total, in which the 202 core contributors have actively indicated they will help build and profit from the networks of PhD-students and scholars. ELISE includes 60 ERC grants of their active supporters. By their citation and other accepted scores of scientific quality, ELISE is the network that combines in Europe excellence in AI.
THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
STICHTING KATHOLIEKE UNIVERSITEIT
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
UNIVERSITAT DE VALENCIA
FUNDINGBOX ACCELERATOR SP ZOO
KNOWLEDGE 4 ALL FOUNDATION LBG
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
CESKE VYSOKE UCENI TECHNICKE V PRAZE
DANMARKS TEKNISKE UNIVERSITET
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
EBERHARD KARLS UNIVERSITAET TUEBINGEN
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
UNIVERSITY COLLEGE LONDON
UNIVERSITEIT VAN AMSTERDAM
CONSORZIO INTERUNIVERSITARIO NAZIONALE PER L'INFORMATICA
European Lighthouse on Secure and Safe AI, (data sconosciuta-data sconosciuta) - Responsabile Scientifico
PE6_7 - Artificial intelligence, intelligent systems, multi agent systems
In order to reinforce European leadership in safe and secure AI technology, we are proposing a virtual center of excellence on safe and secure AI, that will address major challenges hampering the deployment of AI technology. These grand challenges are fundamental in nature. Addressing them in a sustainable manner requires a lighthouse rooted in scientific excellence and rigorous methods. We will develop a strategic research agenda which is supported by research programmes that focus on “technical robustness and safety”, “privacy preserving techniques and infrastructures” and “human agency and oversight”. Furthermore, we focus our efforts to detect,prevent, and mitigate threats and enable recovery from harm by 3 grand challenges: “Robustness guarantees and certification”,“Private and robust collaborative learning at scale” and “Human-in-the-loop decision making: Integrated governance to ensuremeaningful oversight” that cut across 6 use cases: health, autonomous driving, robotics, cybersecurity, multi-media, and documentintelligence. Throughout our project, we seek to integrate robust technical approaches with legal and ethical principles supported bymeaningful and effective governance architectures to nurture and sustain the development and deployment of AI technology thatserves and promotes foundational European values. Our initiative builds on and expands the internationally recognized, highlysuccessful and fully operational network of excellence ELLIS. We build on its 3 pillars: research programmes, a set of research units,and a PhD/PostDoc programme, thereby connecting a network of over 100 organizations and more than 337 ELLIS Fellows andScholars (113 ERC grants) committed to shared standards of excellence. Not only will we establish a virtual center of excellence, but allour activities will also be inclusive and open to input, interactions, and collaboration of AI researchers and industrial partners in orderto drive the entire field forward.
While todays robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitably face novel situations in unconstrained settings, and thus will always have knowledge gaps. This calls for robots able to learn continuously about objects by themselves. The learning paradigm of state-of-the-art robots is the sensorimotor toil, i.e. the process of acquiring knowledge by generalization over observed stimuli. This is in line with cognitive theories that claim that cognition is embodied and situated, so that all knowledge acquired by a robot is specific to its sensorimotor capabilities and to the situation in which it has been acquired. Still, humans are also capable of learning from externalized sources like books, illustrations, etc containing knowledge that is necessarily unembodied and unsituated. To overcome this gap, RoboExNovo proposes a paradigm shift. I will develop a new generation of robots able to acquire perceptual and semantic knowledge about object from externalized, unembodied resources, to be used in situated settings. As the largest existing body of externalized knowledge, I will consider the Web as the source from which to learn from. To achieve this, I propose to build a translation framework between the representations used by robots in their situated experience and those used on the Web, based on relational structures establishing links between related percepts and between percepts and the semantics they support. My leading expertise in machine learning applied to multi modal data and robot vision puts me in a strong position to realize this project. By enabling robots to use knowledge resources on the Web that were not explicitly designed to be accessed for this purpose, RoboExNovo will pave the way for ground-breaking technological advances in home and service robotics, driver assistant systems, and in general any Web-connected situated device.
Contratto di ricerca tra Politecnico di Torino (DAUIN) e la società Sony Europe B.V., avente ad oggetto l’attività “neural Architectural Search (NAS), with a strong orientation to deep neural network morphology manipulation under low-power embedded constraints, with a special focus on perceptual applications, (2021-2022) - Responsabile Scientifico