Our lives are now heavily mediated by digital technology (music streaming, social media, e-banking etc). We are increasingly and often continuously online, open to engagement in a myriad of services and simultaneously open to cyberattack. We now need to defend against the lone wolf hacker, organised crime and terrorism, and nation states with well-funded advanced capabilities. The 2016 cyber message is clear – we have a big problem, it's going to get worse, and we need help. Artificial Intelligence (AI) is a promising source of such help.

Nvidia had a strong showing overall at CES this year, but its most impressive demo had to be the self-driving vehicles it was showing off in a cordoned course built in a parking lot. The demo wasn't on city streets, as were others like the Delphi ride, but it was impressive in a different way; mostly because no human at all sat in the driver's seat of Nvidia's cars. Two cars were in rotation for Nvidia at its test track. The first is a Lincoln MKZ Nvidia purchased kitted with sensors ready for autonomous driving off the shelf from a third-party supplier that retrofits the vehicles specifically for this purpose, affectionately nicknamed'BB8.' BB8 has been in testing with Nvidia for some time now, and is the company's core vehicle for building out its neural network-based autonomous drive software. The second was an Audi Q7, newly equipped with Nvidia's DRIVE PX 2 in-car computer, which offers tremendous computing power in a very small package, and is suitable for handling the huge task of running a locally contained neural net that learns how to drive simply by observing the action of human drivers; these vehicles were trained in Vegas on only four days of driving, Nvidia's Senior Director of Automotive Danny Shapiro told me.

The combination of machine learning capabilities with streaming analytics provides really rich capabilities for not only generating predictions but even more importantly to act on the predictions. Machine learning is about letting the software figure things out on its own. For example, the Denstream Clustering algorithm lets you feed in a stream of data and find out *if* there are any related clusters – without having to know ahead of time. More importantly it identifies the outliers for you, or to put it another way – the clustering algorithm figures out groups of "normal" behaviors and flags the "weird" one's for you to react to. Even more importantly it adapts over time by aging out older values and giving more weight to recent events – the algorithm recognizes the "new normal" long before us humans ever could.

Another way of looking at this hype wave is to track the share price of NVIDIA, the leading graphics processing unit (GPUs) designer. In 2012, University of Toronto researchers developed a then state of the art convolutional neural network (CNN) that achieved a record breaking performance on a large scale image classification task. This feat was made possible, in no small part, because the authors optimised their network (henceforth known as'AlexNet') for parallel training and inference on two NVIDIA GPUs. Since then, NVIDIA GPUs along with their parallel computing platform and programming model (CUDA) have veritably become the shovels for the AI gold rush. The dramatic increase in parallelizable computing power has enabled developers to train deep, data-hungry architectures faster than ever before, whether they are neural network or reinforcement learning models. We've achieved incredible breakthroughs in environment perception, autonomy, robotics, machine translation, speech recognition and dialogue, search, image and video super-resolution, and many more to come.

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Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects - swarm intelligence - resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/ management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems.

Since the earliest days of the Internet, most government agencies have eagerly explored how to use technology to better deliver services to citizens, businesses and other public-sector organizations. Early on, observers recognized that these efforts often varied widely in their implementation, and so researchers developed various frameworks to describe the different stages of growth and development of e-government. While each model is different, they all identify the same general progression from the informational, for example websites that make government facts available online, to the interactive, such as two-way communication between government officials and users, to the transactional, like applications that allow users to access government services completely online. However, we will soon see a new stage of e-government: the perceptive. The defining feature of the perceptive stage will be that the work involved in interacting with government will be significantly reduced and automated for all parties involved.

Large platform companies like Amazon, Apple, Google, Samsung, and Microsoft want to provide the operating system for our lives, and they will fight hard in 2017 to establish their foothold in the emerging technologies we will likely come to rely on in the future. Those with the most complete product offerings have an advantage. Since people like to buy products that play well with the other products they already own, a platform company risks losing customers by not having a product in a hot category. These large companies already have an advantage over smaller companies due to their massive R&D budgets and their ability to hire the best people to build the stuff we want now and to anticipate the technology we'll want in the future. And if a hot product is developed by some ambitious startup, these giants can easily swoop in and acquire both the product and the people who created it.