Erik Brynjolfsson has a dream of the future. A vision of a world where computers entrench the power of a wealthy elite and push the majority into poverty. A world where the rising tide of technology doesn't lift all boats, but sucks under all but the biggest ships. Brynjolfsson is an economist at the Massachusetts Institute of Technology (MIT) and co-author of The Second Machine Age, a book that asks what jobs will be left once software has perfected the art of driving cars, translating speech and other tasks once considered the domain of humans. Dystopia is only one outcome foreseen by Brynjolfsson, but why does he even think it's a possibility? New technology has upended industries for millennia. But the advent of the power loom or steam engine didn't permanently rob men of labour. So what makes today different?

As part of Interaction16, in a packed room at Finlandia Hall in Helsinki, Chris Noessel gave a fascinating and compelling talk on the dawn of agentive technology, and the implications for UX designers. The audience provided a mixed response to the opening question, "Who is afraid of Artificial Intelligence (AI)?" Chris elaborated from his experience with students that perhaps the question might be, "Who is afraid of what humans will do with AI?" Chris talked about the emergence of a new category of technology that works on behalf of users to complete tasks. This category, which he is calling agentive technology, can be seen as a particular form of Artificial Intelligence. The Roomba vacuuming robot automatically vacuums your floors, navigating the space, returns to its charging dock when needed and cleans according to a schedule you set. Get Narrative is a wearable camera that automatically captures images by default every 30 seconds.

In the early 1900s, Wilhelm von Osten, a German horse trainer and mathematician, told the world that his horse could do math. For years, Von Osten traveled Germany giving demonstrations of this phenomenon. He would ask his horse, Clever Hans, to compute simple equations. In response, Hans would tap his hoof for the correct answer. But scientists did not believe Hans was as clever as Von Osten claimed.

Today some of the smartest people in the world are queuing up to work at DeepMind, according to an article by Celemency Burton-Hill in The Guardian in February. Interestingly, the same article states that no one has ever left DeepMind, which has created a series of algorithms that can learn for themselves and beat the best humans at games like Go and "Space Invaders." Based in up-and-coming King's Cross, DeepMind now employs around 250 people. However, as Burton-Hill points out, getting a job there is far from easy. Fortunately, a number of Quora Q&As offer an insight into "What does it take to work at Google DeepMind?" and "What is it like to work at Google DeepMind?"

How do you combine historical Big Data with machine learning for real-time analytics? TIBCO outlines an approach, use cases, and tools of the trade. "Big Data" has gained a lot of momentum recently. Vast amounts of operational data are collected and stored in Hadoop and other platforms on which historical analysis is conducted. Business intelligence tools and distributed statistical computing are used to find new patterns in this data and gain new insights and knowledge for a variety of use cases: promotions, up- and cross-sell campaigns, improved customer experience, or fraud detection.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together – we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors.

Britain is on the brink of a robotics revolution. Advances in technology are unleashing a new age where computers handle many tasks previously carried out by humans. From automated manufacturing to software that does complex legal work, business is adapting to the robot economy. Some worry that this will lead to a jobs apocalypse as "thinking machines" replace workers. Others are optimistic that robots will free workers from mundane tasks and allow them to concentrate on higher-level creative and strategic work.

For the last five years, IBM has strived to reinvent itself as a cloud computing and cognitive platform company to support its large enterprise clients as they shift their operations online, including many in travel and transportation. With most large companies today evolving into digital companies, cloud computing is a booming marketplace for the big four industry providers: IBM, Microsoft, Google, and Amazon. Google, for example, stated that cloud could overtake advertising revenue in five years. Travel companies like Etihad and Lufthansa are helping drive IBM's cloud sales. The UAE carrier signed a 700 million IT deal with IBM last October, while Germany's national airline invested 1.25 billion in Big Blue in November 2014 to integrate cloud computing. Cognitive, on the other hand, is IBM's wild child savant compared to its older cloud sibling.

This article was first published in the July 2015 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online The future of artificial intelligence begins with a game of Space Invaders. From the start, the enemy aliens are making kills -- three times they destroy the defending laser cannon within seconds. Half an hour in, and the hesitant player starts to feel the game's rhythm, learning when to fire back or hide. Finally, after playing ceaselessly for an entire night, the player is not wasting a single bullet, casually shooting the high-score floating mothership in between demolishing each alien. No one in the world can play a better game at this moment. This player, it should be mentioned, is not human, but an algorithm on a graphics processing unit programmed by a company called DeepMind. Instructed simply to maximise the score and fed only the data stream of 30,000 pixels per frame, the algorithm -- known as a deep Q-network – is then given a new challenge: an unfamiliar Pong-like game called Breakout, in which it needs to hit a ball through a rainbow-coloured brick wall. "After 30 minutes and 100 games, it's pretty terrible, but it's learning that it should move the bat towards the ball," explains DeepMind's cofounder and chief executive, a 38-year-old artificial-intelligence researcher named Demis Hassabis. "Here it is after an hour, quantitatively better but still not brilliant. But two hours in, it's more or less mastered the game, even when the ball's very fast. After four hours, it came up with an optimal strategy -- to dig a tunnel round the side of the wall, and send the ball round the back in a superhuman accurate way. The designers of the system didn't know that strategy."

Milton Keynes, U.K. - 30 March 2016: Celaton today announced the release of inSTREAM version 6, its intelligent automation platform that applies sophisticated algorithms, including artificial intelligence and cognitive learning, to streamline and automate the processing of semi-structured and unstructured content. Unstructured and semi-structured unpredictable content flows into organisations every day by email, post, paper, fax, social media, web feeds and other electronic data streams and creates challenges for customers due to the cost and need for experienced staff to process it. Unique to inSTREAM is its ability to learn the pattern of content through the natural consequence of processing and monitoring human intervention. Confidence is improved through accelerated learning. Efficiency is improved through accelerated learning.