Recently the UK Science & Technology Select Committee published a long-awaited report into robotics and AI, and their implications for society. "Artificial intelligence has some way to go before we see systems and robots as portrayed in the creative arts such as Star Wars," said Dr Tania Mathis, the committee chair. Whilst she is, of course, right, that isn't to say that ground isn't being made in AI applications in the military. A good example of this is the recently prototyped product from Roke Manor Research as part of the Defence Science and Technology Laboratory (DSTL). The device, known as STARTLE, utilizes a range of AI techniques to monitor and evaluate threats at sea.

With technology governing almost every aspect of our lives, industry experts are defining these modern times as the "platinum age of innovation"; verging on the threshold of discoveries that could change human society irreversibly, for better or worse. At the forefront of this revolution is the field of artificial intelligence (AI), a technology that is more vibrant than ever due to the acceleration of technological progress in machine learning – the process of giving computers with the ability to learn without being explicitly programmed – as well as the realisation by big tech vendors of its potential. One major tech behemoth fuelling the fire of this fast-moving juggernaut is Intel, a company that has long invested in the science and engineering of making computers more intelligent. The Californian company held an "AI Day" in San Francisco showcasing its new strategy dedicated solely to AI, with the introduction of new AI-specific products, as well as investments for the development of specific AI-related tech. And Alphr was in town to hear all about it.

Mobile means that, for the first time, pretty much everyone on earth will have a camera, taking vastly more images than were ever taken on film ('How many pictures?'). This feels like a profound change on a par with, say, the transistor radio making music ubiquitous. Then, the image sensor in a phone is more than just a camera that takes pictures - it's also part of new ways of thinking about mobile UIs and services ('Imaging, Snapchat and mobile'), and part of a general shift in what a computer can do ('From mobile first to mobile native'). Meanwhile, image sensors are part of a flood of cheap commodity components coming out of the smartphone supply chain, that enable all kinds of other connected devices - everything from the Amazon Echo and Google Home to an August door lock or Snapchat Spectacles (and of course a botnet of hacked IoT devices). When combined with cloud services and, increasingly, machine learning, these are no longer just cameras or microphones but new endpoints or distribution for services - they're unbundled pieces of apps.

In today's economy, we are seeing companies, business models, products, and processes undergoing major transformation. At the time, I felt that I was watching history in the making: The technology known as artificial intelligence (AI) was finally moving from the lab into the world. Second, the abundance of data being generated throughout the world today requires cognitive technology. Intelligence augmentation -- IA as opposed to AI -- will change how humans work together, make decisions, and manage organizations.

Mobile internet applications are evolving rapidly. Cognitive computing technologies will inspire telecom service providers to profoundly change their business model in new creative ways. Deploying intelligent voice control apps on smartphones was just the beginning of this trend. As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. "Machine learning-based predictive analytics are applicable to all aspects of the telecom business," said Joe Hoffman, vice president at ABI Research.

Securing your digital assets is a clear need for any business and individual, whether you are looking to protect your personal photos, your company's intellectual property, your customers' sensitive data, or anything else that can harm your reputation or business continuity. Although billions of dollars are spent on cybersecurity, the number of reported cyberattacks and the magnitude of breaches keep rising. There are many frontiers where harnessing the predictive power of AI might give the upper hand to security vendors -- and to us all, including individuals and businesses. Cisco forecasts that the number of connected devices worldwide will rise from 15 billion today to 50 billion by 2020. A high percentage of these devices do not have basic security measures due to limited hardware and software resources.

Machine learning and Artificial Intelligence are two of the most important developments of the past 10 years within businesses. They have been at the core of the success of several companies, from Facebook's advertising policies through to how American Airlines monitors wear of their plane engines. We wanted to take a look at three companies who are doing some impressive work in the area who are often overlooked for their efforts, either because they are known for other areas or because their competitors sit in the data science limelight. When you think machine learning, you don't naturally go to'short term letting'. Renting out rooms and flats doesn't seem like it would be an especially data-driven enterprise, but this couldn't be further from the truth.

Many important problems can be represented and studied using graphs -- social networks, interacting bacterias, brain network modules, hierarchical image clustering and many more. If we accept graphs as a basic means of structuring and analyzing data about the world, we shouldn't be surprised to see them being widely used in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become a resilient piece of logic, transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed "community"); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g. So how can we extract this kind of information?

There is a pervasive underlying fear from generations raised on dystopian science fiction that artificial intelligence and robotics will be the undoing of humankind. Eventually, the conventional thinking goes -- even the likes of Elon Musk and Stephen Hawking are on board here -- artificial intelligence will become smarter than the organic variety and terrible things will happen as machines take over the planet. In reality, however, it's much more likely AI isn't going to destroy us -- or even take our jobs. In fact, it's very likely going to help us do our jobs better. Think about that for a moment.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.