Nearly one in four of the UK adult population – 12.6m adults – lacks key basic digital skills, according to GO ON UK. This gap has many causes and implications, but high on the list is jobs, because those who lack digital skills are more likely to be unemployed. This is more significant to young people, given that the youth unemployment rate is 14.4% and people aged 16 to 24 are three times more likely to be out of work than the rest of the working population. It is estimated that over 4.4 million IT jobs will be created around Big Data, but only a third of these new jobs will be filled. At Acorn Aspirations we strongly believe that if young people are inspired to create technology from an early age, they will be better equipped to face the constantly changing digital world we live in and contribute as creators of technology and change-makers to solve the most pressing local and global problems.

It's your nighttime routine: You drop your phone onto the nightstand charging pad, and it asks about your day. You tell it, talking to the virtual personal assistant just like you'd talk to a friend. Your phone's artificial intelligence knows you almost as well as you know yourself (maybe even better). So when it suggests ways to get through tomorrow's calendar, you trust its advice. AI is practically everywhere, and getting smarter all the time.

The event featured a panel of three expert speakers who discussed the power of Machine Learning and Artificial Intelligence in addressing real-world business challenges – followed by a live Q&A with attendees. By Design' – a partnership between TORI Global and Accion Labs that has transformed and refreshed TORI's Digital offerings. The event's insight, complemented by additional Q&A material and the latest edition of TORI's Perspectives Magazine, was hosted in a powerful and easy-to-use branded player. This was then sent through to a global audience after the event – enabling TORI to stretch the experience, and continuing the conversation well beyond the live webinar itself. Click here to learn more about ditto's solutions for webinars and other events – both digital and physical.

Nearly a month ago, Manan Vyas told me that he thinks a revolution is in the offing – skills inequality, ownership inequality, and ultimately, income inequality, is increasing. To make matters worse, technology (especially those in third platform fields such as artificial intelligence and big data analytics) is developing at a remarkable pace. This has empowered a few developers and shareholders with a specific and dare I say, a'privileged', background. Though the world's gini coefficient has not steeply increased over the last couple of years, the signs are all there. Foxconn recently announced that it was planning to fire 60,000 workers as their role could now be done by robots.

Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customised, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimised outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

In April, a group of Finnish farmers outfitted a spindly black drone with a remote-controlled chainsaw and filmed it decapitating snowmen. They called it "Killer Drone." More formally, it was a DJI S1000. This spring, marine biologists flew a drone over the Sea of Cortez to capture samples of the fluid sprayed from the blowholes of blue whales. It was a DJI Inspire 1.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

In past posts, I've described how Recurrent Neural Networks (RNNs) can be used to learn patterns in sequences of inputs, and how the idea of unrolling can be used to train them. It turns out that there are some significant limitations to the types of patterns that a typical RNN can learn, due to the way their weight matrices are used. As a result, there has been a lot of interest in a variant of RNNs called Long Short-Term Memory networks (LSTMs). As I'll describe below, LSTMs have more control than typical RNNs over what they remember, which allows them to learn much more complex patterns. Lets start with what I mean by a "typical" RNN.

In a recent project, we found which of 700000 property parcels in Adelaide, Australia, contain swimming pools. We used a combination of crowdsourcing and supervised machine learning in order to harness the inherent ability of humans to identify objects in imagery and the speed of machines, which can perform this task much faster than humans, once trained sufficiently. Our initial approach consisted of training a random forest classifier with a set of crowdsourced labels, then using the machine classifications to present to the crowd only the parcels that were likely to contain swimming pools. Since only a small percentage of the parcels actually contain pools, the efficiency gain of this approach is huge compared to a pure crowdsourcing campaign. At first glance, identifying a pool in a high-resolution satellite image might appear to be a simple task for a human and a machine alike.

The notion of applying deep learning techniques to medical imaging data sets is a fascinating and fast-moving area. In fact, in a recent issue of IEEE's Transactions on Medical Imaging journal, there's a fantastic guest editorial on deep learning in medical imaging, that provides an overview of current approaches, where the field is headed, and what sort of opportunities exist. As such, we pulled out some of our favorite nuggets from this article and summarize/extend upon them in Q&A form, so they're more easily digestible. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. One way is via transfer learning, which has been used to overcome the lack of large labeled data sets in medical imaging.