There's no doubt Stephen Hawking is a smart guy. But the world-famous theoretical physicist recently declared that women leave him stumped. "Women should remain a mystery," Hawking wrote in response to a Reddit user's question about the realm of the unknown that intrigued him most. While Hawking's remark was meant to be light-hearted, he sounded quite serious discussing the potential dangers of artificial intelligence during Reddit's online Q&A session: A superintelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we're in trouble. Hawking's comments might seem unrelated.

San Francisco: Google announced a deal to buy Moodstocks, a French startup behind technology that helps smartphones recognise whatever they are aimed at. Moodstocks caught the US technology giant's eye for its work in computer vision and machine learning, as well for accomplishments in enabling smartphones or other mobile devices to recognise images and objects. Google is among Silicon Valley titans investing in ways to get computers to see and understand the world around them the way people do. Machine learning has been woven into an array of Google offerings, such as its free language translation and photo services. "There's a lot more to be done to improve machine vision," Google France tech site lead Vincent Simonet said in a blog post.

Adaptive systems are a special class of open systems that are continuously evolving. They feature dynamic networks of agents that interact with each other and their environments. Examples of complex, adaptive systems include ant and bee colonies, the stock market, and biological ecosystems, as well as human organizations such as political parties, companies, and cities. And because of its ability to shift and change, the behavior of the whole system cannot be predicted by just analyzing its parts in isolation. So what happens when we reimagine a company more like an adaptive organism, rather than a stable machine?

The discovery of major software flaws could render thousands of fMRI brain studies inaccurate. The use of fMRI is a common method for scanning the brain in neuroscience and psychology experiments. To make sense of the data produced, researchers sometimes use a technique called spatial autocorrelation to identify areas of the brain that appear to "light up" during particular tasks or experiences. But some software flaws in the popular fMRI data analysis packages SPM, FSL and AFNI meant this technique routinely produced false positives, resulting in errors 50 per cent of the time or more. Anders Eklund and Hans Knutsson at Linköping University in Sweden and Thomas Nichols at the University of Warwick, UK, calculated this by analysing brain data from a collaborative open fMRI project called 1000 Functional Connectomes.

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE. The simple task of picking something up is not as easy as it seems. Roboticists aim to develop a robot that can pick up anything--but today most robots perform "blind grasping," where they're dedicated to picking up an object from the same location every time. If anything changes, such as the shape, texture, or location of the object, the robot won't know how to respond, and the grasp attempt will most likely fail.

Self-driving cars in Germany might end up having "black boxes" that can record details of accidents, similar to planes, Reuters reports. Sources say that the proposal from Germany's Transport Minister, Alexander Dobrindt, will also require that riders stay seated in front of the steering wheel, though they won't have to pay attention to traffic or actually steer. As you've probably guessed right now, the legislation follows the recent Tesla Autopilot-related driver death. Regulators and car companies alike will have to work even harder to prove the safety of self-driving cars.

For clear understanding of machine learning algorithms, strong grasp of some mathematical concepts is essential. Let's begin with gradient concept Shoot me a message if you'd like me to explain a math concept with a story.

When evaluating a machine learning algorithm on a regression problem, you are given a number of different performance measures to review.

The U.S. Air Force has awarded BAE Systems' U.S. subsidiary a three-year, 9.4 million contract to help the service branch develop, integrate and demonstrate a machine learning platform. BAE Systems Inc. will explore latest machine learning advancements to support the Air Force's efforts to apply analytics to address space situational awareness challenges as well as leverage high-performance computing and reasoning visualization architectures, the Defense Department said Friday. The Air Force also seeks to increase its ability to detect and predict new complex scenarios through the project. BAE systems will perform the services in Massachusetts and Virginia and is scheduled to finish July 15, 2019. The Air Force Research Laboratory obligated 868,750 in fiscal 2016 research, development, test and evaluation funds at the time of award.

Imagine if you could predict people's behaviour. Even if just for simple tasks like texting (and adding emojis, a brand new only-beta feature) to your chat. Well, now come back to reality as Swiftkey is already doing it. SwiftKey learns from previous SMS messages and output predictions based on currently input text and what it has learned giving you word suggestions following your writing style. Swiftkey has been funded in 2008 and bought by Microsoft earlier this year.