Network colaboration – WP9

About WP9

Collaboration is at the heart of the TAILOR network, and the aim of this work package is to promote and support interaction and collaboration between network partners through tools, development of training material, organisation of training events, PhD and staff exchanges, etc. The overall goals are to foster excellence through shared knowledge and vision, to support a sustainable research capacity in Trustworthy AI which has critical mass and can effectively interact with European funding mechanisms, and to establish a healthy pipeline of future talent.

The tasks in this work package are coordinated with VISION CSA in order to maximise visibility and impact, reduce overhead, and align with the other ICT-48 projects.

WP9 tasks

Task 9.1 AI-Powered Collaboration Tools (Task Lead: TUE)

General-purpose tools for collaboration and networking (Slack, Zulip), code development (Github, Bitbucket), AI libraries (scikit-learn etc.), data and experiment repositories (UCI, OpenML) already exist. What a large and complex network such as TAILOR needs are specific tools supporting collaboration in AI research and innovation. Such tools will be driven by AI techniques such as text mining, network analysis, pattern discovery etc. and hence also act as demonstrators of what AI can do to facilitate research.

Task 9.2 Training Platform and Materia (Task Lead: UPV)

The availability of training material is indispensable for knowledge transfer, developing a shared vision and raising the next generation of researchers. Joint development of such material is also an important driver of collaboration.

Task 9.3 PhD Training (Task Lead: UNIBRIS)

An important form of collaboration and knowledge transfer is by encouraging PhD students to spend time at multiple TAILOR partners. To facilitate such exchanges, this task will map the availability of AI-oriented PhD programmes at TAILOR partners, in particular cohort-based models such as exist in the UK (Centres for Doctoral Training, including one on Interactive Artificial Intelligence at Bristol), in Sweden (Wallenberg AI, Autonomous Systems and Software Program), in Ireland (Science Foundation Ireland Centre for Research Training in Artificial Intelligence), among others. Following this mapping, a joint TAILOR PhD curriculum will be developed.

Task 9.4 Summer Schools and other training events (Task Lead: UNIBRIS)

The network needs regular events for knowledge exchange, training future talent, sustaining a shared vision, etc. In particular Summer Schools are an important and established instrument that will be coordinated by WP9.

A PhD curriculum

One of our tasks is to design a PhD curriculum in Artificial Intelligence, understood as a set of topics and learning goals that would give a PhD student a solid grounding in Trustworthy AI through Integration Learning, Optimisation and Reasoning.

A wealth of AI courses are offered by TAILOR partners. We have made an effort to map and document them all, in a living document that can be updated whenever new courses become available. This mapping will inform the next steps in developing the TAILOR PhD curriculum in AI, possibly allowing multiple streams to navigate the topics. The mapping is available as TAILOR deliverable 9.5 (see deliverables).

TAILOR partners and others with ideas about what a TAILOR PhD curriculum should look like are invited to get in touch with Peter Flach (University of Bristol) at Peter.Flach@bristol.ac.uk.