19 Feb CISC bridging the gap between AI research and industrial applications
CISC is here to bridge the gap between AI research and industrial applications
Europe is a global leader in the research of (ethical) Artificial Intelligence (AI), but it lags behind the USA and China in its industrial applications. The new Horizon 2020 project Collaborative Intelligence for Safety Critical systems (CISC) seeks to bridge the gap between research and industrial application of AI.
Leveraging on the EU’s human-centric AI approach, CISC will provide high-level training to 14 world-class scientists, forging an inter-disciplinary skill set for the development of collaborative intelligence systems and connecting research with industrial applications.
Joining a consortium of Europe’s top academic institutions and industry leaders, IMR, will recruit a PhD researcher specialising in safe Human-Robot collaboration and intuitive task programming. In addition, IMR plays a key role in the definition of Live labs, which are industry relevant use-cases for validating and refining the collaborative intelligent algorithms.
Europe is a global leader in academic research of human-centric artificial intelligence. In 2016, the EU represented 25% of the topmost-cited AI publications, followed by the US and China. However, when it comes to market uptake, especially industrial applications, we are leagues behind. To address this gap, IMR, together with its European partners, is starting the CISC (Collaborative Intelligence for Safety Critical systems) project. Its collaborative intelligence framework will allow 14 world-class AI researchers to gain interdisciplinary skills, get employed with the industrial players (and European SMEs!) and apply their research in practice.
A human-centric AI approach connecting research with applications.
The CISC project is a joint effort of organisations from academia and business. It is coordinated by the H2020 framework’s Marie Skłodowska Curie action. The importance and potential of AI-driven automation in domains like manufacturing, healthcare and transport is tremendous. However, few understand the importance of AI systems’ interaction and collaboration with humans. CISC will train researchers capable of addressing this shortcoming and contribute to the EU human-centric approach to artificial intelligence.
In a nutshell, the project will:
- Train researchers with interdisciplinary skills and intersectoral experience in the field of AI-driven automation for Industry 4.0;
- Promote academic-industry collaborations; and
- Foster European scientific excellence.
Tackling the ethical and legal aspects of AI with a PhD researcher
In the project framework, IMR will recruit a PhD researcher specialising in Human Robot collaboration. The researcher will be enrolled in the PhD programme of Technological University Dublin and will be guided by two outstanding academic advisors Prof John Kelleher and Dr Philip Long. The scientific goals of the researcher will be to focus on how exteroceptive sensor, human-in-the-loop control architecture and learning from demonstration can improve the productivity of a cell reducing installation time and increasing robustness to modelling errors. At the same time, the physical and socio-psychological implications for the humans sharing an industrial workspace with a robot will be analysed.
The researcher will be seconded for 6 months at Polytechnic University of Turin (POLITO) to investigate innovative safety engineering assessment methods and for 12 months in Faculty of Mechanical Engineering from Kragujevac (FINK) to work on data collection and analysis.
Besides hiring and co-mentoring the researcher, IMR leads Work Package 5 – “Design of Experiments for System Safety engineering in LIVELABS”. WP5 focuses on the design, development and implementation of experiment for safe collaborative robotics and human machine interaction. These industrially relevant robotics cells will be located in IMR’s pilot factory in Mullingar Ireland and will be used by researchers across the consortium for testing and validation.
Funded under H2020-EU.1.3.1 Grant agreement ID: 955901