When everything in the world is data, navigating the data successfully requires technology that starts with and is driven by search. As search has become more and more sophisticated its indexing has become more complicated, its categorization of information ever more deductive. Its understanding of the importance of what it has indexed bounded, as well as defined, by knowledge. In many ways, search has become ubiquitous and invisible. When Google Now lets me know that my "…drive home is 39 minutes" from where I am at a meeting it has taken into account where my car is (by looking at the GPS signal of my phone), the traffic conditions on the way (by using Waze to cross-check information in traffic conditions and possible delays), my own travel patterns (by checking the history of my travelling to and from specific meetings with clients and what I do afterwards).

As video content matures and proliferates and VR content creates new interactive environments and as the way we access software and apps changes and evolves, PVAs or personal virtual assistants will radically influence the interface with the internet and these new layers of content and virtual experience. Viv is just four years old, but by the time IoT and VR matures, she will be ready to make the smart home, smart city and billions of connected devices truly come to life. While we assume it will be one of the tech giants: Google, Facebook, Amazon, Apple or Microsoft; disruption doesn't usually come from an established player whose interests and investments are scattered. Just as VR represents a new platform of information, content, advertisement, marketing and social media, and digital experiences, VPAs like Viv could represent another springboard to the future.

As an amateur futurist, I'm always looking for how AI and exponential technologies are disrupting human society towards more automated smart cities. As video content matures and proliferates and VR content creates new interactive environments and as the way we access software and apps changes and evolves, PVAs or personal virtual assistants will radically influence the interface with the internet and these new layers of content and virtual experience. The future of PVAs is more like an intelligent OS that acts as a buffer between us and the information noise of the internet. Take your most sophisticated contextual chat bot and marry it with millions of data points per person and algorithms learning from us and how we interact online and in our lives, where we can customize our own experience, and that is a bit like how PVAs may turn out. They will be AI, but truly act as personal virtual assistants.

The pathway in the brain responsible for vision operates in several layers, each of which is thought to extract progressively more information from an image, such as movement, shape, color, and so on. Each layer consists of huge numbers of neurons connected into a vast network. Deep convolution neural networks have a similar structure. They too are made up of layers, and each of these is a network of circuits designed to mimic the behavior of neurons, hence the term neural network. Through much trial and error, computer scientists have found that these layers perform best when each extracts progressively more information about an image. And when they look at the behavior of layers individually, they find remarkable similarities to the function of specific layers in the brain.

Computers are becoming rather versatile copycats, thanks to deep-learning algorithms. Just last year, researchers "trained" machines to transfer the brushstrokes of iconic artists onto any still image. Now, Manuel Ruder and a team of computer scientists from the University of Freiburg in Germany have taken the technology a step further: They're altering videos. The team's style transfer algorithm makes clips from Ice Age or the television show Miss Marple appear as living paintings crafted by the likes of Van Gogh, Picasso or any other artist. And the results speak for themselves.

A very popular style of research in NLP and ML is the math abstraction. You cast your learning problem as some sort of objective function that you want to optimize. Or, if you're feeling Bayesian, you write down a joint likelihood that you'll either sample from or, yes, turn into an objective function that you want to optimize. The optimizer is then typically considered a black box, aside from its hyperparameters which you often must tune. This is a very attractive style of research and one that I've personally gotten a lot of leverage out of.

When it comes to Big Data, machines can only do so much. At least that's what startup Spare5 Inc. is betting with the launch of what it calls an "Intelligent Crowdsourcing Platform" that leverages a community of specialists to process Big Data tasks that require a human touch. Crowdsourcing services today run the gamut from the mundane (Amazon.com's Spare5 sits somewhere in the middle, but its special sauce is a machine learning algorithm that rates the skills and preferences of crowd members. The platform is useful for "pretty much anything that involves getting structured data out of unstructured data," said Matt Bencke, founder and CEO.

UNU allows groups to chat online in a new way by forming a Swarm Intelligence that can answer questions, make predictions, and each decisions. Artificial intelligence (unanimous) UNU has not only conquered the Oscars and Super Bowl, but also the famous Kentucky derby. Its Swarm Intelligence software has reportedly made betting gamblers richer, with correct predictions owing to its successful forecasting methodologies. According to News Discovery, UNU is a software program that harnesses the collective power of horse racing professionals to correctly predict the first four horses that cross the finish line and in which order. At last weekend's Kentucky Derby, one experimental player witnessed the power of this software at first hand.

Are deep neural networks creative? It seems like a reasonable question. Google's "Inceptionism" technique transforms images, iteratively modifying them to enhance the activation of specific neurons in a deep net. The images appear trippy, transforming rocks into buildings or leaves into insects. Another neural generative model, introduced by Leon Gatys of the University of Tubingen in Germany, can extract the style from one image (say a painting by Van Gogh), and apply it to the content of another image (say a photograph). Generative adversarial networks (GANs), introduced by Ian Goodfellow, are capable of synthesizing novel images by modeling the distribution of seen images.

Deep neural networks have progressed immensely in recent years. Far from being moonshots, they are now at a point where they are better than humans at several tasks. They are capable of understanding text without previously encountering the words, of translating different languages, they can even cut transcription errors by half. And now, it seems they can recognize and replicate art (and a number of art styles). A team from the University of Freiburg in Germany have used deep neural networks to copy the artistic style of famous painters and paste them into video clips from movies and TV shows.