The inventor of the world wide web, Sir Tim Berners-Lee, recently warned that artificial intelligence could develop decision-making capabilities that impact the fairness of economic systems, if allowed to operate at a high level of business. Although data gathered by artificial intelligence is often used to inform decisions made at board-level, such as the predicted growth of demand for a product within a particular industry, AI is a long way from calling the shots. Presently, decision-making AI in offices remains limited to repeatable, mundane tasks like formatting and filing documents. A recent report from PwC predicts that up to 30% of jobs in the UK are at risk of being taken over by AI and robots by early 2030. While this might come as a surprise to some, IT departments will already be aware that they need to prepare their systems for the technology.

Exponential progress in artificial intelligence (AI) and machine learning, fuelled by the combination of cloud, big data and new algorithms, is transforming the retail industry. As AI leverages big data to automate, predict and personalize, retail is testing and implementing these applications to garner robust competitive advantages. The key focus for AI in retail is customer relationships. In times of concerns for the retail sector in the UK where sales posted biggest quarterly fall since 2010, the refashioning of this industry comes as a breath of fresh air with many opportunities to come. Global Artificial Intelligence Opportunities in Retail, 2017, new research from Frost & Sullivan's Connected Industries Growth Partnership Service, offers an overview of AI and its relevance to business in 2017.

Christina is audience development editor. After graduating from the University of Nottingham reading philosophy and theology in 2013, Christina joined a tech start-up specialising in mobile apps. She has a keen interest in the mobile platform and innovative tech. If you haven't heard 100,000 times already, artificial intelligence is going to be a pretty big deal. The tools and software in place will accelerate this growth.

From 24 – 28 April, four ECHORD experiments will present their work at Hannover Messe, the world's biggest trade show. You can see the latest advances in EU-funded robotics technology development in Hall 17, stand C70, and get the chance to develop your own ideas with their on-site competition. CoCoMaps, Collaborative Cognitive Maps, uses an expanded version of the existing Cognitive Map Architecture implemented on Honda's ASIMO robot in an environment with more complex tasks than already attempted. This will allow the robot to interact in more complex ways, in particular, to simultaneously interact with another robot and more than one person at a time. Thus, the project aims for a group of 2 robots and 2 humans enabling social interactions that can coexist with the robots' attention to – and completion of – practical tasks in the workplace.

Finally, it seems fans of the military shooter series Call of Duty are going to get what they've been wanting for almost a decade: the past. On Friday evening, Activision announced Call of Duty: WWII as the next instalment in the multi-million selling video game franchise, taking it back to the original setting. For several years, the games have been moving forward in time, advancing beyond the near-future setting of the Modern Warfare titles into the space battles of Infinite Warfare, introducing drones, robots and, most controversially, rocket packs, along the way. But as the diminishing returns from the game's annual instalments have shown, hardcore fans have become alienated by the endless new technological additions, preferring the'boots on the ground' authenticity of the first Call of Duty titles, with their cinematic renderings of Operation Overlord, the second battle of El Alamein and the advance on Berlin. It was similar feedback, aimed at the rival shooter series Battlefield, that encouraged Electronic Arts to set the latest title during the first world war – to critical and commercial success. There is no information yet on where in the conflict Call of Duty WWII will be set and fans will undoubtedly be watching closely on 26 April when Activision promises to reveal more.

In a little over two years, a fleet of driverless cars will make its way from Oxford to London, completing the entire journey from start to finish without human intervention, including on urban streets and motorways. Organisers of the government-backed project, announced on Monday, still expect to have a human in the driving seat. But as the cars communicate, update on hazards, and automatically react, is the time coming when a human driver is not just redundant but an active danger? A driverless transport trial started this month in Greenwich, south-east London, with members of the public invited to climb aboard Harry – a version of the pod vehicles used at Heathrow's Terminal 5, souped-up with lasers, sensors and £20,000 worth of autonomous technology. Harry was doing a careful 15mph beside the Thames when an oncoming jogger swerved right in front of us, forcing the vehicle to a sudden halt.

Imagine a doctor you could consult at any time, describing your symptoms and then getting a speedy and accurate diagnosis - all in a smartphone app. That is what British firm Babylon is promising, and to build this perfect doctor it is using machine learning. It is a technique which is suddenly transforming many aspects of our lives. Babylon is announcing an investment of £50m to build what it claims will be the world's most advanced artificial intelligence healthcare platform. Its chief executive Ali Parsa says it will put expert health advice in the hands of smartphone users around the world. "Our scientists have little doubt that our AI will soon diagnose and predict personal health better than doctors," he says.

Apart from fears of mass unemployment, accidents with machinery, restrictions on freedom, increased economic inequality and a devalued human experience, the public are broadly optimistic about the arrival of artificial intelligence, according to one of the first surveys of British opinions about the technology. Research by the polling firm Ipsos Mori found nearly a third of people believe the risks of "machine learning" outweigh the benefits, while 36% believe the risks and benefits are balanced. Machine learning is technology that underpins internet searches, recommendations on Amazon and Netflix, and voice recognition on smartphones. The findings provide a snapshot of UK views on what some researchers regard as the early stages of a major revolution that is poised to affect almost every aspect of life. The research suggests that – while people are generally positive about the technology for improving medical treatments, guiding driverless cars and personalising education – substantial concerns remain.

According to Clark's seminal work on common ground and grounding, participants collaborating in a joint activity rely on their shared information, known as common ground, to perform that activity successfully, and continually align and augment this information during their collaboration. Similarly, teams of human and artificial agents require common ground to successfully participate in joint activities. Indeed, without appropriate information being shared, using agent autonomy to reduce the workload on humans may actually increase workload as the humans seek to understand why the agents are behaving as they are. While many researchers have identified the importance of common ground in artificial intelligence, there is no precise definition of common ground on which to build the foundational aspects of multi-agent collaboration. In this paper, building on previously-defined modal logics of belief, we present logic definitions for four different types of common ground. We define modal logics for three existing notions of common ground and introduce a new notion of common ground, called salient common ground. Salient common ground captures the common ground of a group participating in an activity and is based on the common ground that arises from that activity as well as on the common ground they shared prior to the activity. We show that the four definitions share some properties, and our analysis suggests possible refinements of the existing informal and semi-formal definitions.

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.