It's not only drones and driverless cars that may become the norm someday -- ocean-faring ships might also run without captains or crews. The Pentagon on Monday showed off the world's largest unmanned surface vessel, a self-driving 132-foot ship able to travel up to 10,000 nautical miles on its own to hunt for stealthy submarines and underwater mines. The military's research arm, the Defense Advanced Research Projects Agency, or DARPA, in conjunction with the Navy will be testing the ship off the San Diego coast over the next two years to observe how it interacts with other vessels and avoids collisions. Unlike smaller, remote-controlled craft launched from ships, the so-called "Sea Hunter" is built to operate on its own. "It's not a joy-stick ship," said DARPA spokesman Jared B. Adams, standing in front of the sleek, futuristic-looking steel-gray vessel docked at a maritime terminal in the heart of San Diego's shipbuilding district, where TV crews filmed the robotic craft.

With more than 8 year experience in Information Security, Competitive Intelligence and Data Sciences, for Testing, securing the business & infrastructure, designing and developing the solutions for the said line of business, he created and worked at "Voice of Green Hats"; LiFi Research & Development; Competitive Intelligence, Testing environment Robotics software and automation (Virtualization). He has been working as under: Information Communication Technology (Cloud computing, Virtualization, Networking) Information Security (Ethical Hacking & Digital Forensic Investigation) International Business (Trade supporting IT Engineering) Competitive Intelligence (Digital branding, business success axis, upgrading expertise and businesses) Business Intelligence (Data Sciences) Artificial Intelligence (IoT, Robotics) With vast business professional networking of chambers of commerce, business council and professional associations in especially in Malaysia, Canada, Australia, New Zealand, EU and Middle East.

Arjun Pratap is founder and Chief Executive Officer at EdGE Networks. Fueled by the vision to build innovative, future-focused HR technology solutions – which re-engineer Human Resource Management to positively impact business outcomes – Arjun leads EdGE Networks to be a disruptor in the skill development space. Prior to EdGE Networks, Arjun worked with organizations such as SpeedERA Networks and Akamai Technologies, where he was responsible for building their India and international businesses. He also headed the sales function at Dexler Information Solutions to provide strategic direction in building the company. Arjun holds a post graduate degree in Information Systems and International Business, from The University of Sydney, Australia.

The Turing test is a test, developed by Alan Turing in 1950, of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. When I joined Slack, my vision was to help developers build bots that pass the Turing test 2–3 times a day. As time passes, I understand the "beer test" might be much more important. Now the Beer Test is much less complex than the Turing Test: When I worked in New Zealand, I was asked to interview several engineers and product managers. In addition to assessing their tech and product skills, I had to answer this question: "Would you go out for a beer with this person?"

With venture capitalists more guarded about where to put their cash amid a global economic slowdown, many technology start-ups are having to shed costs and generate profits sooner than expected. "If you want to become profitable at all costs, you're leaving users and growth on the table," said Farhad Mohit, chief executive of the image-sharing app. His Los Angeles company raised 70 million from investors about a year ago, a big haul that's still fueling the business today. The app enables people to create a slideshow from photos and videos, place samples of popular music in the background, add effects and share it with friends. Flipagram can get a small cut of sales if users buy a full song.

Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording processes. We investigate the efficacy of three most effective randomized machine learning techniques - random forests, gradient boosting machines, and deep neural nets with dropout - in predicting suicide risk. Using a cohort of mental health patients from a regional Australian hospital, we compare the predictive performance with popular traditional approaches - clinician judgments based on a checklist, sparse logistic regression and decision trees. The randomized methods demonstrated robustness against data redundancies and superior predictive performance on AUC and F-measure. Keywords: Suicide risk, Electronic medical record, Predictive models, Randomized machine learning, Deep learning 1. Introduction Every year, about 2000 Australians die by suicide causing huge trauma to families, friends, workplaces and communities[1].

Technological change has accelerated in the past few decades. The digital revolution has significantly changed manufacturing processes, and the ways in which people work, consume and live. Machines powered by computer programs can be designed to plan, reason, present knowledge and learn human responses. Such machines are called intelligent machines, and the intelligence they possess is known as artificial intelligence or AI. During the industrial revolution of the 19th century, machines replaced skilled weavers in the textile industry.

I've written a few times recently about a number of projects that are using drone technology to monitor vast environments. As you can perhaps imagine, with such endeavors, there is a huge amount of data generated, and while it presents rich pickings from a scientific perspective, nonetheless raises challenges about how that data can be managed. A recent study tested the role automation could play in both easing the burden on research teams and making data analysis more effective. The paper revealed that when teams are looking for optimal speed and accuracy that an approach that combines both machine and human can be the best. The researchers used the analysis of aerial images taken by camera drones in the Kuzikus wildlife reserve as their testing ground. The drones were used to count the wildlife in the park, and generated a huge amount of images over the course of the study.

Using stock market prediction algorithm to forecast energy stocks: This Energy Stocks forecast is designed for investors and analysts who need predictions of the best-performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Forecast Length: 30 Days (03/29/16 – 04/29/16) I Know First Average: 36.82% Cliffs Natural Resources Inc.(CLF) grew by 88.89% in just 1-month, was the top performing stock in the Energy Stocks forecast for that time period. Another top performing stock was DNR that grew by 71.56%, with an astonishing return of ten out of the ten stocks that increased in accordance with the algorithm's prediction. CDE and VALE also offered strong returns of 48.90% and 37.29%, Within the predicted 30-days it performed very well in the Energy Package.

Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy c-means algorithms for data described by distributional variables is proposed. The algorithms use the $L2$ Wasserstein distance between distributions as dissimilarity measures. Beside the extension of the fuzzy c-means algorithm for distributional data, and considering a decomposition of the squared $L2$ Wasserstein distance, we propose a set of algorithms using different automatic way to compute the weights associated with the variables as well as with their components, globally or cluster-wise. The relevance weights are computed in the clustering process introducing product-to-one constraints. The relevance weights induce adaptive distances expressing the importance of each variable or of each component in the clustering process, acting also as a variable selection method in clustering. We have tested the proposed algorithms on artificial and real-world data. Results confirm that the proposed methods are able to better take into account the cluster structure of the data with respect to the standard fuzzy c-means, with non-adaptive distances.