As part of this development, OMI is becoming part of the University's Department of Engineering Science from 1 August 2016. The development of the OMI's focus will create a hub for machine learning and data analysis at Eagle House, the current home of the OMI and Man AHL's Oxford research lab. The OMI's researchers will be joined by the Department of Engineering Science's Machine Learning Group, a body of around 20 leading machine learning researchers who will relocate to Eagle House. The aim is to foster a stimulating environment composed of researchers focused on machine learning techniques, whereby machine learning and data analytics expertise can be shared and leveraged. In addition, the intention is for the OMI to appoint two new Senior Research Fellows/Associate Professors in machine learning with a specific focus on quantitative finance.

The White House is doing a lot more thinking about the arrival of automated decision-making -- super-intelligent or otherwise. No one in government is yet screaming "Skynet," but in two actions this week the concerns about our artificial intelligence future were sketched out. The big risks of A.I. are well-known (a robot takeover), but the more immediate worries are about the subtle, or not-so-subtle, decisions made by badly coded and designed algorithms. President Barack Obama's administration released a report this week that examines the problem associated with poorly designed systems that, increasingly, are being used in automated decision making. Algorithmic systems can affect employment, education, access to credit -- anything that relies on computer-assisted decisions.

That said, current technology won't allow us to make anything larger than the printing machines themselves. Some smart people have suggested that we should look to nature to see how it builds things--specifically, spiders, and the way they can swarm together to build massive nests for themselves. Siemens, the German engineering and telecommunications company, has taken this concept to heart. A team of researchers at its Princeton, New Jersey, laboratory are creating autonomous spider-like robots that can work together to 3D-print structures on command. While Siemens isn't known for its robotics research, Livio Dalloro, the head of the company's "Product Design, Simulation & Modeling Research" group in Princeton, said in an interview that the company views the bots as a "moonshot."

And pretty soon, they'll come for us. That seems to be the story today, whether from Hollywood or in breathless articles in popular tech magazines about artificial intelligence and nanotechnology. Our biggest ever edition of TNW Conference is fast approaching! In a world where machines can learn, once humans push the "on" button, there's no stopping our robot overlords, right? When machines become more intelligent, humans are freed to become more creative.

College of Computing Professor Ashok Goel teaches Knowledge Based Artificial Intelligence (KBAI) every semester. And every time he offers it, Goel estimates, his 300 or so students post roughly 10,000 messages in the online forums -- far too many inquiries for him and his eight teaching assistants (TA) to handle. That's why Goel added a ninth TA this semester. Her name is Jill Watson, and she's unlike any other TA in the world. Jill is a computer -- a virtual TA --implemented on IBM's Watson platform.

Now WATSON HEALTH AI is being used in 16 cancer institutes across the country, helping to diagnose and treat patients. Meanwhile Google, not to be outdone, has launched DeepMind, which recently earned the title world champion of the complex game of Go. Now, DeepMind Health will create innovative new apps for healthcare professionals alerting them to patient emergencies, and the risk of complications when considering possible treatment options. Progenitors say that someday, it should even be able to predict a patient's needs down the pike. Other tech companies such as Dell, Hewlett-Packard, Apple, and Hitachi are also putting together AI programs for the healthcare field. Within the next five years, AI's use in medicine is expected to increase tenfold.

Almost a year after it came out in the US, Facebook is releasing its facial recognition-powered photo app Moments in Europe. Except the new version won't actually include any facial recognition technology, thanks to the company's long-running fight with the Irish data protection commissioner over whether the technology is actually legal in the EU. Launched in June, Moments is Facebook's answer to dedicated photo management applications like Google Photos and Apple's Photos. The app bundles pictures together by the event they're taken at, and applies facial recognition technology to identify who's in each picture. Facebook takes the offering a step further than Apple or Google, by leveraging its social network: once you've created your "moments", you can share them with other people at the same event, to ensure that they have the photos of them, and you have the photos of you.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

What do law enforcement, sports, healthcare, retail and agriculture all have in common? Thanks to big data and advanced analytics, these are just a few industries where predictive modeling is poised to change the playing field. Big data keeps getting bigger. And continual advances in computing, warehousing and associated technologies make it ever more useful. We know more about the behaviors of people and the outcome of events than ever before.

Editor's note: Register for the free webcast "How the machine learning wave is changing the way organizations look at analytics," hosted by Patrick Hall, senior machine learning scientist at SAS, and Andrew Pease, principal business solutions manager at SAS, to learn how different organizations are finding success with machine learning. The inherent trade-off between accuracy and interpretability in predictive modeling can be a catch-22 for analysts and data scientists working in regulated industries. Professionals in the regulated verticals of banking and insurance often feel locked into using traditional, linear modeling techniques to create their predictive models. This is mainly due to strenuous regulatory and documentation requirements. As machine learning becomes more mainstream, the forces of innovation and competition often drive these same analysts and data scientists to break out of the mold and try new algorithms with more predictive capacity.