Last week MPs warned that the UK is facing a digital skills crisis. Its report explained that 90% of jobs require digital skills to some degree, but flagged that the UK needs another 745,000 workers with digital skills by 2017 in order to remain competitive against other countries. At the moment the skills gap is reportedly costing the UK around 63bn a year. In tandem, we are seeing more companies invest in automation and artificial intelligence (AI), which reduces the need for humans in the decision making process. For example, Twitter recently announced the acquisition of machine learning startup, Magic Pony Technology.

The little striker wearing a crescent moon and star jersey lines up his penalty and kicks right, netting his goal as the keeper dives the wrong way and hits the ground yelping in pain. Both players are teammates practising to represent Pakistan in a major world football tournament. Unlike their low-ranked flesh-and-blood counterparts, however, these are advanced robots whose programmers are set to compete against students from the world's top universities as they look to showcase what their country can do in the world of Artificial Intelligence. Students at Pakistan's National University of Science and Technology (NUST) will this year for the first time send a team to the annual RoboCup, an event featuring 32 universities that will be held in Leipzig, Germany from June 27 to July 4. The six machines are NAO humanoid robots purchased from France's Aldebaran Robotics at a cost of roughly 17,000. It is in fact the third year that NUST, Pakistan's premier engineering institute, has qualified for the prestigious cup.

The word Artificial Intelligence is increasing. The latest example is Picturesqe, a tool for photographers that uses AI-powered automation to help pick out the best snaps and filter out the dross. Founders of Picturesqe, a machine-learning powered piece of software, are confident that it can select the good photos from your large stack. But unlike similar mobile Apps, Picturesqe is targeted specifically at professional photographers and semi-pros. Features of the application include smart grouping, which automatically groups similar photos based on visual content, intelligent zoom so that you can quickly compare the same spot on multiple shots, and aesthetic ranking.

The little striker wearing a crescent moon and star jersey lines up his penalty and kicks right, netting his goal as the keeper dives the wrong way and hits the ground yelping in pain. Both players are teammates practising to represent Pakistan in a major world football tournament. Unlike their low-ranked flesh-and-blood counterparts, however, these are advanced robots whose programmers are set to compete against students from the world's top universities as they look to showcase what their country can do in the world of Artificial Intelligence. Students at Pakistan's National University of Science and Technology (NUST) will this year for the first time send a team to the annual RoboCup, an event featuring 32 universities that will be held in Leipzig, Germany from June 27 to July 4. The six machines are NAO humanoid robots purchased from France's Aldebaran Robotics at a cost of roughly 17,000. It is in fact the third year that NUST, Pakistan's premier engineering institute, has qualified for the prestigious cup.

Robots are predicted to steal the jobs of millions of workers across the world over the next few years, as technology replaces human shop assistants, teachers, accountants and potentially even taxi drivers. Artificial intelligence and automation are also expected to revolutionise the investment industry, especially in the area of robo-advice. This is where algorithms are used to suggest funds to investors, replacing some human financial advisers in the process. Less spoken about is the impact robotics and artificial intelligence could have on the role of the portfolio manager. But commentators believe many fund managers' jobs could come under threat as developments are made in technology.

A half-dozen young, mostly male engineers sit in a bright, open office digging through a mountain of code. Multi-monitor work stations sit on desks strewn with laptops, tablets, and headphones. This is not a tech startup, however. Today these factory workers are culling immediate production inefficiencies and developing systems that they hope will, by next year, allow the factory's machines to diagnose their own problems, order replacement parts, and anticipate necessary maintenance hours or even weeks in advance. Through a bank of large windows opening from the computer room onto the shop floor, row after row of machines hum a percussive melody as they turn out small parts--fuel injection nozzles, electronic safety control systems, mechanical brake systems--destined for car companies including BMW, Volkswagen, and Tesla.

The world of autonomous vehicles just got a more luxurious, after Rolls-Royce unveiled a driverless super car of the future. It has dubbed its futuristic car, which looks like it could have come straight out of Batman's cave, the Rolls-Royce 103EX. The vehicle will be six metres long, with a canopy roof, covered wheels and a curved body. The six metre-long vehicle, dubbed the Rolls-Royce 103EX, has a canopy roof, covered wheels and a curved body. The front seat is replaced with a sofa-style body.

After abandoning its own GPU for supercomputers, machine learning, and video games in 2009, Intel has returned to the market with a new 72-core Xeon Phi, to compete with NVIDIA's growing portfolio of GPUs. The Xeon Phi'Knights Landing' chip, announced at the International Supercomputing Conference in Frankfurt, Germany last week, is Intel's most powerful and expensive chip to date and is aimed at machine learning and supercomputers, two areas where Nvidia's GPUs have flourished. Inside the chip there is 72-cores running at 1.5GHz, alongside 16GB of integrated stacked memory. The chip supports up to 384GB of DDR4 memory, making it immensely scalable for machine learning programs. At the conference, Intel mentioned some of the issues with GPUs for complex machine learning programs.

Mixture model-based clustering has become an increasingly popular data analysis technique since its introduction fifty years ago, and is now commonly utilized within the family setting. Families of mixture models arise when the component parameters, usually the component covariance matrices, are decomposed and a number of constraints are imposed. Within the family setting, we need to choose the member of the family, i.e., the appropriate covariance structure, in addition to the number of mixture components. To date, the Bayesian information criterion (BIC) has proved most effective for model selection, and the expectation-maximization (EM) algorithm is usually used for parameter estimation. To date, this EM-BIC rubric has monopolized the literature on families of mixture models. We deviate from this rubric, using variational Bayes approximations for parameter estimation and the deviance information criterion for model selection. The variational Bayes approach alleviates some of the computational complexities associated with the EM algorithm by constructing a tight lower bound on the complex marginal likelihood and maximizing this lower bound by minimizing the associated Kullback-Leibler divergence. We use this approach on the most famous family of Gaussian mixture models within the literature and real and simulated data are used to compare our approach to the EM-BIC rubric.

For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. So the UK has just given itself a national headache. Whether you think the Brexit was the right decision or a dangerous and unmitigated screw-up (as I do), the consequences of the referendum will be non-trivial and take years to complete. But the mechanics of the UK exiting the European Union aside, the question of how people now feel about the Brexit is interesting. Are they awash in jubilation or has buyer's remorse set in?