It was the case of the missing PhD student. As another academic year got under way at Imperial College London, a senior professor was bemused at the absence of one of her students. He had worked in her lab for three years and had one more left to complete his studies. But he had stopped coming in. Eventually, the professor called him.

Artificial intelligence is not a new concept for the people. Machine learning and robots were infused into our lives years ago but the hype of artificial intelligence today, is extra-ordinary. Whether it is an upcoming AI technology or a new perspective of it's on the society. It is still obscure that what will be the potential impacts of these technologies on different platforms in which organizational implementation of AI is most highlighted. People are still confused on the idea especially when it comes to the recruitment of these machineries.

Microsoft first launched HoloLens in 2015 as a gaming-centric consumer product, but so far, very few folks have so much as picked up a Minecraft block with the $3,000 device. HoloLens has been a big success with businesses, allowing designers to visualize digital changes on real-life objects and helping employees do complex tasks or high-tech sales demos. In fact, it's been so popular with companies that Microsoft is now expanding sales to 29 new European markets, taking the total up to 39 nations. Microsoft says that companies like Ford and Thyssenkrupp have been asking for HoloLens availability in Spain, Sweden and Turkey, where it's currently unavailable. The device has been particularly popular for so-called firstline workers that repair elevators or build cars, for instance.

The EnhanceNet-PAT algorithm could help with everything from restoring old photos to improving image recognition for self-driving cars. Anyone who has ever worked with image files knows that, unlike the fictional world of shows like CSI, there's no easy way to take a low-resolution image and magically transform it into a high-resolution picture using some fancy "enhance" tool. Fortunately, some brilliant computer scientists at the Max Planck Institute for Intelligent Systems in Germany are working on the problem -- and they've come up with a pretty nifty algorithm to address it. What they have developed is a tool called EnhanceNet-PAT, which uses artificial intelligence to create high-definition versions of low-res images. While the solution is not a miracle fix, it does produce a noticeably better result than previous attempts, thanks to some smart machine-learning algorithms.

Artificial Intelligence (AI) could soon be deciding how much your weekly grocery shop costs, with dynamic pricing expected to become common place in the coming years. Dynamic pricing sees the costs of products change throughout the day according to factors such as demand and availability. You may have already witnessed similar pricing strategies with the likes of Expedia, who adapt prices based on your internet data and history, and Uber, who use "surge" prices when there is an increased demand, and cinemas are now even experimenting with such schemes. With the rise in popularity of internet shopping and tech giants like Amazon getting involved in the grocery sector, brick and mortar stores are becoming inventive to keep consumers shopping in-store. We are seeing consumers increasingly take a hybrid approach to their shopping, comparing prices online and buying in store. As a result of these changing shopping habits, supermarkets will increasingly turn to real-time data to react to market trends.

When Angelica Lim bakes macaroons, she has her own kitchen helper, Naoki. Her assistant is only good at the repetitive tasks, like sifting flour, but he makes the job more fun. Naoki is very cute, just under two feet tall. He's white, mostly, with blue highlights, and has speakers where his ears should be. The little round circle of a mouth that gives him a surprised expression is actually a camera, and his eyes are infrared receivers and transmitters. "I just love robots," says Lim, a Ph.D. student in the Department of Intelligent Science and Technology at Kyoto University in Japan.

HPE has just announced a bunch of new things to help organisations everywhere tap into AI. The new offerings include an integrated hardware-software solution, a set of guiding tools, a research collaboration platform, and a place to get access to the latest expertise. Deep Learning, as a subset of AI, is key for things like facial or voice recognition, image classification or other challenging tasks. It requires a high performance compute infrastructure to build and train learning models that can handle vasts amount of data, and that is something many organisations lack. This is also the core problem HPE is trying to solve with its new solution.

The emerging field of artificial intelligence (AI) risks provoking a public backlash as it increasingly falls into private hands, threatens people's jobs, and operates without effective oversight or regulatory control, leading experts in the technology warn. At the start of a new Guardian series on AI, experts in the field highlight the huge potential for the technology, which is already speeding up scientific and medical research, making cities run more smoothly, and making businesses more efficient. But for all the promise of an AI revolution, there are mounting social, ethical and political concerns about the technology being developed without sufficient oversight from regulators, legislators and governments. The report found that AI had the potential to add £630bn to the economy by 2035. But to reap the rewards, the technology must benefit society, she said.

In the intervening years British biotechs have been springing up in the fertile space between drug development and big data, including genomics data specialists Congenica and Eagle, while firms like E-Therapeutics use artificial intelligence to help discover new medicines. Not to be outdone, FTSE 100 behemoth AstraZeneca has plans to map 2m genomes within a decade. Dr Jurgi Camblong, founder and chief executive of Sophia, says his firm could ultimately map the genomes of 50m cancer patients alone a year, helping identify the genetic traits that contribute to the disease. He hopes more data sharing will lead to positive behavioural change in healthcare provision. "My father was sick and for my mother it was a nightmare," he says.

Microsoft Research, Cambridge, UK We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian inference problem for an auxiliary observation process. The solution can be approximated efficiently by solving a closed set of coupled ordinary differential equations (for the low-order moments of the process) whose size scales with the number of species. We apply it to an epidemic model and a trimerisation process, and show good agreement with stochastic simulations. Many systems in nature consist of stochastically interacting agents or particles. Such systems are frequently modelled as reaction processes whose dynamics are described by master equations [1].