If there was ever something worthy of being called a chopper, this crazy contraption is truly it. The radio-controlled helicopter not only has rotary blades, but it also has a chainsaw attached to it. The three friends from Finland who made it have nicknamed the invention their'killer drone', and used the test flight to hack at trees, icicles and snowmen. Test-run: The three friends from Finland who made it have nicknamed the invention their'killer drone, and used the test flight to hack at trees, icicles and snowmen Beheaded: The snowmen's heads were immediately reduced to nothing when the chainsaws hit them, sending a cloud of ice and slush flying through the air Miika Ullakko, Henri Kiviniemi and drone pilot Antti Junnari came up with the idea as a joke after seeing videos of drones delivering mail. Miika said: 'Anyone who's ever flown a drone knows how ridiculous the idea of a drone delivering mail is.

As of February 1st, Nvidia has announced five finalists to compete for its 2016 Global Impact Award, a yearly 150,000 research grant that goes to any researcher or institution that has used Nvidia GPU technology to make a positive social or humanitarian impact. This year's finalist teams come from Stanford University, Imperial College London, George Mason University, Duke University and Sweden's Chalmers University of Technology. Stanford finalist: "GPUs Help Map Worldwide Poverty" One of the five selected finalists this year is machine learning expert Stefano Ermon, who partnered with food security specialists David Lobell, Marshall Burke and some Stanford engineering students for their work in using GPU-accelerated deep learning to turn regular Google Earth images into statistical poverty models. The team trained a neural network to accurately predict poverty levels in sub-Saharan Africa from satellite image features like roads, farmlands and homes. "There are countries in sub-Saharan Africa for which the most recent data we have is 20 years old, so we're still extrapolating from early '90s estimates," says Ermon.

" A revolution will happen when tools like Siri can truly serve as your personal assistant and you start relying on such an assistant throughout your day. To get there, these systems need more knowledge about your life and preferences, more knowledge about the world, better conversational interfaces and at least basic commonsense reasoning capabilities. I have interviewed Alon Halevy, Executive Director at Recruit Institute of Technology. What is the mission of the Recruit Institute of Technology? Alon Halevy: Before I describe the mission, I should introduce our parent company Recruit Holdings to those who may not be familiar with it.

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

Access to data is one thing. Moving from data to insight is another entirely. Jane Zavalishina, CEO of the Yandex Data Factory wants to make it easier for businesses to gain real insight from their data. Built on the real time personalization and predictive analytics technology of Russia's largest Internet business Yandex- Yandex Data Factory helps clients improve their business through the exploitation of their own data by machine learning. Disproportionate growth of figures suggesting that 90% of the world's data was generated over the course of the past two years has led Zavalishina to be of the opinion that "we should not really care about data anymore," rather, we should see it as a thing of the past.

At a glance, machine learning and statistics seem to be very similar, but many people fail to stress the importance of the difference between these two disciplines. Machine learning and statistics share the same goals--they both focus on data modeling--but their methods are affected by their cultural differences. In order to empower collaboration and knowledge creation, it's very important to understand the fundamental underlying differences that reflect in the cultural profile of these two disciplines. To gain a deeper understanding of these differences, we need to take a step back and look at their historical roots. In 1946, the first computer system, ENIAC, was developed with the vision of reforming numerical computation using a machine (instead of manual numerical computation using pencil and paper).

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.

We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of incrementally constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.

Of all the fast and powerful computers in the world, our brain remains by far the most impressive. Now an interdisciplinary team of scientists, led by Baylor College of Medicine, aims to reveal the computational building blocks of our brain and use them to create smarter learning machines. To enable this ambitious project, the U.S. government's Intelligence Advanced Research Projects Activity(IARPA) has awarded a 21 million contract to an interdisciplinary team of neuroscientists, computer scientists, physicists and mathematicians, led by principal investigator Dr. Andreas Tolias, associate professor of neuroscience at Baylor. The research team includes scientists from Baylor, the California Institute of Technology, Columbia University, Cornell University, Rice University, the University of Toronto and the University of Tuebingen. The program supporting this research is known as Machine Intelligence from Cortical Networks (MICrONS) and was envisioned and organized by Jacob Vogelstein, a neuromorphic engineer and program manager with IARPA.

Look closely at what many journalists write about artificial intelligence – from AlphaGo's triumph at the ancient Chinese board game Go to Microsoft's accidentally racist Twitter bot – and you might detect some smugness. Research by Oxford University has predicted that journalism is among the jobs least likely to be replaced by a machine in the near future. And yet, as Columbia University prepares to celebrate 100 years of the Pulitzer prize, intelligent robots will publish financial reports, sports commentaries, clickbait and myriad other articles formerly the preserve of trained journalists. "A machine will win a Pulitzer one day," predicts Kris Hammond from Narrative Science, a company that specialises in "natural language generation". "We can tell the stories hidden in data."