Atlanta's traffic congestion infamy -- the city regularly makes every annual top list due to its bottlenecks -- is partially caused by car accidents at large, busy intersections. After a few accidents occur, the Georgia Department of Transportation (GDOT) may swoop in to assess the situation to make the intersection safer. But what if they could do that before the accidents happen? "Most crashes are preventable, which is the concept behind the'Vision Zero' Initiative," says Dr. Jidong Yang, assistant professor of civil engineering and director of Kennesaw State's Georgia Pavement and Traffic Research Center. Vision Zero was originally created in Sweden in the 1990s in order to streamline mobility within cities while eliminating traffic fatalities and severe injuries.

Artificial intelligence could help people live longer by detecting your internal age and designed a tailor-made medical regime, according to new research. Scientists developed a'simple and cheap' computer algorithm that can calculate people's biological age, and reveal whether certain lifestyle changes and medical products could increase the chance of living a long and healthy life. The formula, called Aging.AI, has provided accurate results for 130,000 individuals based on their blood samples. New research, led by the AI company Insilico Medicine, says artificial intelligence could determine a person's risk of developing age-related diseases like cancer and heart disease. Scientists created a formula that can calculate a person's risk of developing age-related diseases, and give medical advice based on those risks'The artificial intelligence is just as good at predicting your age as if you looked at a picture of the person and had to guess the person's age,' said Dr Morten Scheibye-Knudsen, a professor at University of Copenhagen's Center for Healthy Aging.

We present an efficient second-order algorithm with $\tilde{O}(\frac{1}{\eta}\sqrt{T})$ regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm of the competitor. The family of loss functions ranges from hinge loss ($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the open problem of (J. Abernethy and A. Rakhlin. An efficient bandit algorithm for $\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our algorithm experimentally, showing that it also performs favorably against earlier algorithms.

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops a scalable multi-kernel learning scheme (termed Raker) to obtain the sought nonlinear learning function `on the fly,' first for static environments. To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed using weighted combinations of advices from hierarchical ensembles of experts. The weights account not only for each kernel's contribution to the learning, but also for the unknown dynamics. Performance is analyzed in terms of both static and dynamic regrets. AdaRaker is uniquely capable of tracking nonlinear learning functions in environments with unknown dynamics, with analytic performance guarantees. Tests with synthetic and real datasets are carried out to showcase the effectiveness of the novel algorithms, and their performance.

In Belgium, Volvo is promoting its S90 model in an unusual way - it's getting the car to do its recruitment. They refitted a Volvo S90 as the "HR90," equipping it with artificial intelligence that allows it to interview prospective technicians. The car will be "recruiting" at the Brussels Motor Show, and will then continue with a tour of job expos, schools and Volvo dealerships in search of new hires. Volvo asked candidates to submit their job application on a website in order to be considered for an interview. The car quizzes them via image recognition, mapping and analysis of preset parameters, analysing the candidate's facial expressions and word use in order to assess their knowledge, motivation and social skills.

Preface: This technique can be used for shady stuff like Article Spinning, so please don't do that. To generate semantically similar sentences, you can search for nearest word2vec match. If you want to also adhere to the original syntax, then use a sentence parser (so you don't change "Great Britain" into "Good Britain") for correct handling of nouns, verbs, and proper names. "Mark went to the movie yesterday" becomes "Steve came to the film earlier", "Jane has gone to shopping" becomes "Sally had went to mall", and "Mary went to school" becomes "Catherine gone to elementary_schools". Not as much variation as your own examples, but with a little tweaking you should be able to get closer to what you want.

While it may be a bit premature to expect collaborative humanoid robots to be doing anything useful in a warehouse environment, the only way we're going to make it happen is by encouraging the difficult transition between research labs and industry. The European Union is doing a pretty good job of providing support for things like this through its Horizon 2020 program, and one of the projects it's supporting is called SecondHands, intended to "design a robot that can offer help to a maintenance technician in a pro-active manner… as a second pair of hands that can assist the technician when he/she is in need of help." SecondHands is a collaboration between Ocado (a U.K. company that operates highly automated warehouses), Karlsruhe Institute of Technology (which has a bunch of experience building capable humanoid robots), and other research institutions including EPFL, UCL, and Sapienza University of Rome. Together, they're using the first prototype of the SecondHands collaborative robot, which also happens to be the sixth version of ARMAR, and one that's ready (we hope) to do something practical. ARMAR was created by Professor Tamim Asfour and his team at the High Performance Humanoid Technologies Lab (H²T) at KIT's Institute for Anthropomatics and Robotics.

The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). EuRobotics regularly publishes video interviews with projects, so that you can find out more about their activities. The project aims at aligning roboticists' visions of a future with robots with empirically-based knowledge of human needs and societal concerns through a new proximity-based human-machine ethics that take into account how individuals and community connect with robot technologies. At the core of these guidelines is the concept of collaborative learning, which permeates all aspects of REELER and will guide future SSH-ICT research. Integrating the recommendations of the REELER Roadmap for responsible and ethical learning in robotics in future robot design processes will enable the European robotics community to addresses human needs and societal concerns.

With all the hype surrounding artificial intelligence, you would be forgiven for thinking that developing the algorithms powering deep learning are where the toughest challenges in the industry are. The actual challenge for most algorithms though is not their mathematics, but rather their inputs -- collating high-quality data that is well-labeled and allows for the training of these models as quickly and efficiently as possible. That's where DefinedCrowd comes in. The company, which is based in Seattle and Portugal, was founded in 2015 by Daniela Braga, a data scientist and natural language processing expert, and Amy Du, who has since moved on from the company to start a global entrepreneurship network. We've talked about the company back when it participated in Microsoft's startup accelerator and also when it was featured in the Battlefield at TechCrunch Disrupt New York this past year.

Artificial intelligence (AI) is making significant waves across the globe, with experts predicting that it will increasingly change and reshape the way people live their daily lives. AI is also likely to shake up the legal industry; triggering a profound shift in the delivery of legal services. However, with such potential and power to drive seismic change to ordinary life and professional services, it has led to some debate over whether AI should be regulated. Professor Sylvie Delacroix, of the University of Birmingham, spoke to Thomson Reuters' Legal Solutions UK & Ireland Blog about her views on AI and the call for regulation. How significant is artificial intelligence and its role within society? The most significant development today is the extent to which we are capable of gathering and exploiting data to develop new kinds of knowledge which radically transform the way we live, for better or for worse.