Sign up for our newsletter to not miss out on tomorrow's game-changers for your industry. Sport clubs like Paris Saint-Germain and Chelsea are going digital as they envisage a new level of sport performance analytics. And we can support this next generation sport analytics with platforms collecting any type of sensory data and with analytics suites enabling real-time correlation and presentation of data in human readable format to experts of sport domain. This will make it possible to eliminate today's limitations of vertical sensor analytics and offline correlations. There are a range of possibilities for providing these kinds of solutions to large sport arenas, like soccer, with capabilities of low latency, low energy consumption and large coverage range.

It is a truth universally acknowledged that there is huge potential for public services to be transformed by artificial intelligence (AI). We are living longer and healthier lives and the population growth is slowing down. The working age population is shrinking and with that comes a reduced capacity to deliver public services. At the same time the demands and needs for public services are increasing and the pressure on efficiency of public services has never been larger. We need to do more with less.

What better stock to review first for robots and AI month than one of the most familiar names in the space: iRobot (IRBT)? For one, as mentioned, the company is familiar. I would reckon that pretty much everyone reading this either has a Roomba, knows someone who has one, or at the very least knows what it is! Secondly, the company has an extremely easy-to-understand business model. And third, iRobot is very much a "pure play" on the robotics trend, in a space where a lot of the advancements are taking place within the "skunk works" divisions of larger, more diversified companies that make their money in other ways.

Artificial intelligence had something of a coming out party in 2017. While some intense sensationalization of AI persisted throughout the year, progress was made to help people understand the realities of AI-driven technologies and applications that guide several aspects of their personal digital lives. The inner workings of AI technologies and how they make decisions, however, remained a black box for many. Here's how I see AI -- and its journey into the mainstream -- evolving in 2018. Sorry, Sophia the Robot, but the AI industry will begin moving away from developing technologies that reside in humanlike physical structures.

Global technology company Huawei has launched a study on the similarities between the human brain and Artificial Intelligence, which reveals that the average European is unaware of 99.74% of the actual decisions they make every day, showing how hard our brain works without us having to consciously engage it. It is commonly accepted that the human brain makes approximately 35,000 decisions a day; however, the new research, polling 10,000 Europeans, reveals that we are aware of just 0.26% of these decisions with respondents on average believing they make only 92 decisions per day. Walter Ji, President, Huawei Western Europe Consumer Business Group comments, "The research shows how human intelligence works just like Artificial Intelligence, operating in the background to empower us in everything we do. While revealing a significant gap between the number of decisions we believe we make every day and the actual number we make, the results also shed light on other discrepancies between how we think we spend our time, and how we actually spend it." The research also revealed how people would like their smartphones to help with decisions and make their lives easier, with 47% saying they would like to be presented with creative ways to use up the food that's in their fridge, and 43% saying they would like automatic notifications about travel.

The global retail analytics market is expected to grow by $5.1 billion by 2022 to an estimated $8.64 billion. This extraordinary growth will largely be driven by the use of data science in disrupting the retail sector -- changing the way retailers do business online and offline, from the way stores organise their layout to hyper-targeted and optimised pricing and offers. In the UK alone, the retail sector is worth more than £368bn, employs 2.3 million people and accounts for 1/3 of all consumer spending. Retail generates huge amounts of highly valuable data that businesses are only recently starting to utilise. However, recent reports suggest that the sector is struggling, due to changing spending habits and shifting shopping trends.

It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us. One algorithm meant to shine a light in the darkness is AdVerif.ai,

In a recent essay, Martin Rees, an astrophysicist and retired University of Cambridge professor, says he is certain that, for good or ill, we are coming upon the limits of human knowledge -- a point at which computers could one day overtake us. The idea certainly taps into deep-seated fears about artificial intelligence, but I'd argue that we shouldn't worry about computers outsmarting us, or even the real (or imagined) limits of human knowledge. What we need to worry about is wasting the knowledge we already have. It is not a question of whether there is a limit to scientific understanding but whether we are limiting ourselves in our scientific understanding. Science, be it in the form of cancer biology or climatology or statistics, can help provide perspective and potential solutions for many of our most pressing problems.

You'd need your glasses on to play this version of Pac-Man. Tiny metal robots can plot their own route around a maze modelled on the iconic video game. Similar devices could one day be used to travel around the body, delivering drugs or performing surgery. Sarthak Misra from the University of Twente, the Netherlands, and colleagues created four different types of micro-gripper robots, with the smallest being just 100 micrometres long. The biggest was still less than one millimetre.

Reinforcement learning (RL) is learning by interacting with an environment. An RL agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration), which is essentially trial and error learning. The reinforcement signal that the RL-agent receives is a numerical reward, which encodes the success of an action's outcome, and the agent seeks to learn to select actions that maximize the accumulated reward over time. In general we are following Marr's approach (Marr et al 1982, later re-introduced by Gurney et al 2004) by introducing different levels: the algorithmic, the mechanistic and the implementation level. The best studied case is when RL can be formulated as class of Markov Decision Problems (MDP). The agent can visit a finite number of states and in visiting a state, a numerical reward will be collected, where negative numbers may represent punishments.