Undergraduate Winners for their invention SignAloud, gloves that translate sign language into text and speech. Researchers have floated possibilities in this space that could end up transforming health care, like using Google Glass to give doctors more treatment information in real time. And health care wearables promise to be a billion-dollar industry. Just this month, IBM announced the latest in a string of health and tech partnerships, a project with pharmaceutical giant Pfizer to us wearable sensors to help study and treat patients with Parkinson's disease. But last week, two new wearable technologies gained a spotlight when their creators were awarded the Lemelson-MIT student prize, a yearly cash award to a handful of collegiate inventors.

A team of scientists from the Moscow Institute of Physics and Technology (MIPT) have created prototypes of "electronic synapses" based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems. The paper has been published in the journal Nanoscale Research Letters. The group of researchers from MIPT have made HfO2-based memristors measuring just 40x40 nm2. The nanostructures they built exhibit properties similar to biological synapses.

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The most realistic risks about the dangers of artificial intelligence are basic mistakes, breakdowns and cyber attacks, an expert in the field says – more so than machines that become super powerful, run amok and try to destroy the human race. Thomas Dietterich, president of the Association for the Advancement of Artificial Intelligence and a distinguished professor of computer science at Oregon State University, said that the recent contribution of 10 million by Elon Musk to the Future of Life Institute will help support some important and needed efforts to ensure AI safety. But the real risks may not be as dramatic as some people visualize, he said. "For a long time the risks of artificial intelligence have mostly been discussed in a few small, academic circles, and now they are getting some long-overdue attention," Dietterich said. "That attention, and funding to support it, is a very important step."

Facebook on Tuesday announced the opening of a new artificial intelligence lab in Paris to expand a push to make its online social network smarter and more profitable. The new Paris AI lab--the third after two it operates in the United States--has six researchers at work and will more than double that number by the end of the year, executives from the US-based company said. The recruits will come from France's top public and private technological institutions. Since 2013, Facebook has been looking to push the envelope for artificial intelligence. It hired Yann LeCun, a renowned French professor at New York University specialised in "deep learning" algorithms, to run the initiative.

"You can know everything about jumping out of a plane, but until you've actually experienced it, you don't know what the f*ck you're talking about." For many years, cognitive and computer scientists have been interested in building computers that can mimic human actions. As technology has become increasingly advanced, this once futuristic goal seems to be finally within reach. IBM's Watson can beat a human at jeopardy, cars can drive themselves, and more and more tech companies are focused on providing virtual services that mimic human capacities. To this end, author and futurist, Ray Kurzweil claims Artificial Intelligence will be equal to human intelligence by 2029.

Google said on Monday that it had agreed to buy British artificial intelligence start-up company DeepMind for an undisclosed amount. "I can confirm that the acquisition has indeed gone ahead but unfortunately we are not commenting beyond that for now," a Google spokeswoman told AFP. Reports put the deal at between 400 million and 500 million (292-365 million euros). On its one-page website, DeepMind describes itself as "a cutting edge artificial intelligence company" which combines "the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms". Artificial intelligence can help computers "think" in ways similar to the way humans think and help solve problems.

PAVIS department at Istituto Italiano di Tecnologia (IIT) (http://www.iit.it/pavis) is looking for a highly qualified candidate with a strong background in Computer Vision, Pattern Recognition and Machine Learning, with particular emphasis on recognition, video analysis, behavior understanding and prediction. As the activities may be carried out in collaboration with other research units inside IIT, previous multidisciplinary experience is an added value which will be duly considered. The main mission of PAVIS (Pattern Analysis and Computer Vision) is to design and develop innovative video surveillance systems, characterized by the use of highly-functional smart sensors and advanced video analytics features. PAVIS also plays an active role in supporting the other research units inside IIT providing scientists in Neuroscience, Nanophysics and other departments/centers with ad-hoc solutions. To this end, the group is involved in activities concerning computer vision and pattern recognition, machine learning, multimodal\multimedia data analysis and sensor fusion, and embedded computer vision systems.

These use cases–and so many more–all owe a debt to machine learning. By automatically discovering patterns that lead to insights and creating predictive models that drive actions, the technology has proven its value many times, and to many industries. More recently, machine learning has begun to make a name for itself in the field of cybersecurity. There are multiple reasons that manual cybersecurity practices on their own are no longer sufficient to protect businesses, governments and other organizations. All of these issues are further aggravated by the fact that threats are growing in scale and complexity.

Pattern analytics, deep learning, and machine learning have fueled a rapid rise in interest in GPU computing, in addition to GPU computing applications in high performance computing (HPC) and cloud-based data analytics. As a high profile example, Facebook recently contributed its "Big Sur" design to the Open Compute Project (OCP), for use specifically in training neural networks and implementing artificial intelligence (AI) at scale. Facebook's announcement of Big Sur says "Big Sur was built with the Nvidia Tesla M40 in mind but is qualified to support a wide range of PCI-e cards," pointing out how pervasive Nvidia's Tesla platform has become for AI research. Big Sur is a 4U high chassis housing a two-processor (2P) board connected to a daughter card featuring eight full-height double-width PCIe Gen3 x16 300W accelerator card slots intended to house GPU or other PCIe-based compute accelerators. The processor board and daughter card are linked via one PCIe Gen3 x16 slot in the initial implementation.