The Carnegie Endowment for International Peace (CEIP) and Carnegie Mellon University held the first part of the two-part joint Carnegie Colloquium on Digital Governance and Security in Washington D.C. on Oct. 31. The first part of the colloquium was titled "The Rise of Artificial Intelligence: Implications for Military Operations and Privacy," and the second part, titled "The Future of the Internet: Governance and Conflict," will be held this year in Pittsburgh on Dec. 2. CEIP is a series of foreign policy-based research centers located in Russia, China, Europe, the Middle East, India, and the United States, with headquarters in Washington D.C., that collects itself under the phrase, "The Global Think Tank." It was established in 1960 by Andrew Carnegie and, according to a report by the University of Pennsylvania, is the third most influential think tank in the world. The colloquium, held for the benefit of both Carnegie Mellon and the CEIP, aimed to allow for communication between the academics at Carnegie Mellon and the foreign policy and ethics experts from all the CEIP stations across the world to discuss the implication of artificial intelligence on foreign policy and the challenges posed by it. The second part of the colloquium will focus on cyber-security norms and internet governance. "Designing safe software systems and attempting to create the learning abilities of the human brain are natural progressions towards the two of the modern world's most pressing concerns -- cyber security and privacy," said President Subra Suresh, in the welcome address.

Google and the Royal Free London NHS Foundation Trust have announced a fresh five-year collaboration today, which will see the former's DeepMind AI used to improve patient care across the trust's various hospital sites. The partnership will focus on Streams, a mobile app the pair have been working on since late last year that's been approved as a medical device by the UK's health regulator. DeepMind will analyse blood test results as they come in and flag when patients might be at risk of acute kidney injury, proactively alerting carers through the Streams app. It'll go live across the trust in early 2017, and there are plans to expand the blood analysis to look for signs of sepsis and other causes of organ failure. The pair hope to add messaging and task management features over the course of the collaboration too, and Streams is said to be built on open standards that will allow other developers to easily add new services.

Cognitive systems - in the form of ANN is a technology that High-Tech Bridge is particularly familiar with, because the company's ImmuniWeb web security testing platform is based on machine learning technology. High-Tech Bridge uses artificial neural networks (ANN) and advanced human augmentation to implement intelligent automation of vulnerability scanning, but each scenario needs careful preparation, and unsupervised ANN is not on the menu, as Kolochenko explained: "We continuously aggregate knowledge and skills of humans to feed into the ANN. You have to teach the network to make the decisions. Our intelligence needs to be very specific, so we use supervised learning."

Small drones are already effective weapons for urban warfare--when armed with miniature warheads, these stealthy spies can turn into lethal assassins. So far their biggest limitation is battery life, but Boeing's patent for a drone battle station sets out to overcome that. The aerospace giant's'Vehicle Base Station' resembles Amazon's proposed recharging stations on street lights, but with a different mission. John Vian, a research fellow at Boeing, says the station's main applications are likely to be civil and commercial--used for firefighting and search-and-rescue, for example--but the patent has a decidedly military slant. "The unmanned aerial vehicles may monitor for undesired activity… [which] may be the placement of an improvised explosive device in roadway."

And in the next few years, it will transform every industry. Soon, self-driving cars will reduce congestion and improve road safety. AI travel agents will know your preferences and arrange every detail of your family vacation. And medical instruments will read and understand patient DNA to detect and treat early signs of cancer. Where engines made us stronger and powered the first industrial revolution, AI will make us smarter and power the next. What will make this intelligent industrial revolution possible? A new computing model -- GPU deep learning -- that enables computers to learn from data and write software that is too complex for people to code. 3. NVIDIA -- INVENTOR OF THE GPU The GPU has proven to be unbelievably effective at solving some of the most complex problems in computer science.

Apple released its latest laptops on Thursday, a new range of computers to replace the ageing range of Retina MacBook Pros. They are thinner and lighter than their predecessors, with a new touch bar at the top of the keyboard and a fingerprint sensor replacing the power button. They are £750 more than the machines they replace were – though their price has also gone up. The larger of the two new MacBook Pros, the 15in with Touch Bar, is the first laptop the company has released with a starting price of more than £2,000 for more than a decade: it begins at an eye-watering £2,349, with build-to-order options taking it well north of £4,000. Apple's former cheapest laptop, the £749 MacBook, is also being retired.

The National Transportation Safety Board is looking into an accident involving an enormous experimental drone belonging to Facebook which crashed earlier this year. The drone was built to bring internet to far-flung parts of the planet and designed to hover 60,000 ft above the surface of the Earth. However, in June the drone known as Aquila, ended up plummeting to earth after suffering a'structural failure' as it was coming into land. No one was hurt in the incident. The drone, called Aquila, suffered from a'structural failure' right before it landed According to Bloomberg the crash is one of several hiccups that the social network has faced in recent months, following an explosion earlier this year that destroyed one of its satellites.

A London NHS hospital trust has teamed up with tech giant Google to share patient data so it can save more lives. Doctors at the Royal Free say partnering with the artificial intelligence arm of Google - DeepMind - could free up over half a million hours per year, currently spent on paperwork, towards direct patient care. Medical staff will get'breaking news' style alerts about their patients. Privacy campaigners are concerned about data breaches. Information on more than 1.6 million patients a year will be shared with a subsidiary of Google.

DeepMind Health, the division of the Google-owned AI company that's focused on building links to healthcare providers to drive the application of machine learning algorithms for preventative medicine, has inked a fresh data-sharing agreement with the NHS Royal Free Hospital Trust in London. It's the second agreement signed between the pair -- and it supersedes their original agreement inked last year, which ran into controversy after a freedom of information request by New Scientist revealed the volume of patient identifiable medical data (PID) flowing from the Royal Free to DeepMind, and raised questions about whether NHS information governance principles were being correctly followed. The data in question was being used to power an app called Streams, built by DeepMind but using an NHS algorithm to generate alerts on patients at risk of Acute Kidney Injury (AKI). At the time the collaboration was made public, last February, no details were provided about how much PID was being shared between DeepMind and the NHS -- leading to huge consternation when the scope of the arrangement emerged. The U.K.'s data watchdog, the ICO, began investigating complaints about the data-sharing agreement. The Streams app also ran into trouble when it was revealed DeepMind and the Royal Free had not registered it as a medical device with the oversight body, the MHRA, despite piloting the app in the Royal Free's hospitals.

Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. Unfortunately, most methods do not come with out of the box straight forward interpretation. Even linear prediction functions are not straight forward to explain if features exhibit complex correlation structure. In this paper, we propose the Measure of Feature Importance (MFI). MFI is general and can be applied to any arbitrary learning machine (including kernel machines and deep learning). MFI is intrinsically non-linear and can detect features that by itself are inconspicuous and only impact the prediction function through their interaction with other features. Lastly, MFI can be used for both --- model-based feature importance and instance-based feature importance (i.e, measuring the importance of a feature for a particular data point).