The aim of the 30-months PICASSO project is (1) to reinforce EU-US collaboration in ICT research and innovation focusing on the pre-competitive research in key enabling technologies related to societal challenges - 5G Networks, Big Data, Internet of Things and Cyber Physical Systems, and (2) to support the EU-US ICT policy dialogue by contributions related to e.g. privacy, security, internet governance, interoperability, ethics. PICASSO is oriented to industrial needs, provides a forum for ICT communities and involves 24 EU and US prominent specialists in the three technology-oriented ICT Expert Groups and an ICT Policy Expert Group, working closely together to identify policy gaps in the technology domains and to take measures to stimulate the policy dialogue in these areas. A synergy between experts in ICT policies and in ICT technologies is a unique feature of PICASSO. An analysis of the industrial drivers, societal needs, and priorities for EU-US ICT collaboration will be done, and policy gaps will be highlighted. An Opportunity Report will point out new avenues for EU-US research, innovation and policy collaboration. An “ICT Industry Toolkit” app will support companies and academia in exploiting collaboration opportunities. Policy briefs focusing on specific aspects of identified policy gaps will provide visibility for EU policies and propose ways forward. Strategic initiatives will be investigated and discussed, and a White Paper will be prepared. The outreach campaign will include 30+ events, success stories factsheets, info sessions and webinars. PICASSO will directly contribute to the strengthening of the European industrial leadership in ICT. PICASSO’s approach will be integrative, inclusive, industry-driven, societally responsible and beneficial for both EU and US. It is supported by NIST, National Institute of Standard and Technology, US, and the European Cluster Alliance.

In the next decade our economy and society will be revolutionised by ubiquitous Autonomous, Intelligent Machines and Systems, which can learn, adapt, take decisions and act independently of human control. They will work for us and beside us, assist us, and interact and communicate with us. The UK has the opportunity to become a world-leader in developing these technologies for sectors as diverse as energy, transport, environment, manufacturing and aerospace. This CDT directly addresses the present need to train future leaders capable of accelerating innovation in autonomy, and promoting it to some of the UK's largest sectors. This requirement can be met by cohorts of highly-trained individuals versed in the underpinning sciences of robotics, embedded systems, machine learning, wireless networks, control, computer vision, statistics & data analysis, design and verification. These disciplines are intimately related via the application and development of mathematical models and techniques implemented on computers to make predictions, take optimal decisions, perform inference and actions that are robust in the face of uncertainties at all levels. The synthesis of a range of disciplines is absolutely essential to train individuals in all aspects of autonomy, who will then be able to credibly communicate with large technical teams, and pioneer disruptive technologies into industrial labs. This CDT is focused on student training in algorithms, devices, and data feeds inherent to autonomous, intelligent machines & systems. To create and understand these complex systems, students need to be trained to program, embed and design software, to implement established and novel algorithms efficiently and correctly and to develop and apply models and decompositions which lie at the core of approaches to control, communicate, learn from, interpret and distil the large volumes of data endemic to autonomous systems. We believe that for a training centre to achieve its full potential in the AIMS area, it must recognise and respond to the synthesis of a number of component technologies. Students belonging to this CDT will be trained in both the fundamentals of autonomous systems engineering and the latest approaches and perspectives. The UK is faced with an increasing technology skills shortage, with a recent (2012) large-scale survey reporting that half of all key UK industries surveyed suffer from a worsening skills shortage (net.org.uk/news, June 2012). This is even more acute in high-tech industry and requires core investment in teaching highly-qualified cohorts. More specifically, the commercial potential of Autonomous Systems for the UK is tremendous, as demonstrated by the recent AAD KTN (Aerospace, Aviation & Defence Knowledge Transfer Network) study. Their research indicates "an untapped short term market value of circa £7bn per annum just for relatively low level autonomy products and services". Developing skills in designing and deploying autonomous systems will offer significant opportunities for growth to high priority sectors, as diverse as manufacturing, energy, smart buildings, intelligent transport systems, and defence. These sectors are in need of rapid change to reach targets of national importance, while still being able to compete in the global market. One of the main targets is the reduction of greenhouse gas emission (by 80% by 2050), which calls for energy-aware autonomous systems to become a cross-cutting technology in our society. Another driver of change is the growing and ageing population, which advocates the need for autonomous telecare, transport, efficient usage of public/private infrastructure, safety and security. Changing demographics, combined with strict emissions targets and budget cuts, raise unique challenges and opportunities for revolutionising key UK sectors.

The ambitious targets in the United Kingdom for increasing the share of renewable energy sources integrated to the network, and the need for providing affordable, resilient and clean energy, call for a paradigm shift in energy systems operations. Electric vehicles offer the means to address these challenges and achieve uninterrupted operation by deferring their demand in time and acting as dynamic storage devices. As a result, their number is expected to increase rapidly over the next years, leading to a "green car revolution". This constitutes an opportunity for modernizing energy systems operation, but will unavoidably give rise to coordination and scheduling issues at a population level so that cost savings are achieved and reliability is ensured. The latter is of significant importance to prevent from undesirable disruptions of service. This project will address this problem using tools at the intersection of control theory, optimization and machine learning, allowing for a decentralized computation of the electric vehicle charging strategies, while preventing vehicles from sharing information about their local utility functions and consumption patterns that is considered to be private. We will develop algorithms capable of dealing both with cooperative and non-cooperative vehicle behaviours in large fleets of vehicles, and immunize the resulting strategies against uncertainty due to unpredictability in the vehicles' driving behaviour and due to the presence of renewable energy sources. The presence of an algorithmic tool with these features will allow for scalable charging solutions amenable to problems of practical relevance, will provide insight on the mechanism driving the response of large populations of electric vehicles, and embed robustness in the resulting charging schedules. As such, the proposed project will offer the means for reliable system operation and facilitate the integration of higher shares of renewable energy sources.