Continuing its commitment to academic excellence in fundamental research in artificial intelligence (AI), Borealis AI today announced the launch of the Borealis AI Graduate Fellowship Program, which will offer financial support to domestic and international students wishing to pursue graduate-level work in the fields of machine learning or artificial intelligence at a Canadian university. "We truly believe there's no better place in the world right now than Canada to be studying AI-related fields," says Foteini Agrafioti, Chief Science Officer of RBC and Head of Borealis AI. "Between our top-tier universities, booming AI ecosystem, inclusivity and overall quality of life, Canada is a highly valued destination for students and RBC is continuing its commitment to create these opportunities." The 10 fellowships will be awarded to outstanding students in Master's and PhD-level areas of study who have already been admitted to a Canadian school. Requirements for participation include a demonstration of strong research capabilities and a sound research proposal for graduate studies. Borealis AI will commit $100,000 annually to the fellowships, which will be administered directly to the school where the winners are registered.

I think it is a good program but I'm just not interested in web development anymore. I was originally positioning myself to become a UX Designer/Researcher, which I'm still interested in, but not particularly as it relates to web. Recently I've been getting much more interested in AI, since it touches on so many subjects I'm interested in, and obviously because it is such an important topic for the future. I definitely want to start positioning myself more towards this field as I continue my undegraduate field. But I don't really know where to go from here.

Robots Can cost as low as $25,000* 250,000 purchased globally in 2016** *Source: Robots: The new low-cost worker, Dhara Ranasinghe, CNBC, April 10, 2015. The "average" US worker now spends 25% of their day reading or answering emails Fewer than 16% of companies have a program to "simplify work" or help employees deal with stress. The average mobile phone user checks their device 150 times a day. The "average" US worker works 47 hours and 49% work 50 hours or more per week, with 20% at 60 hours per week 40% of the US population believes it is impossible to succeed at work and have a balanced family life. FOMO We are all suffering from…….

The fourth industrial revolution stands out from its predecessors in a critical way: rather than making it easier for humans to use their surroundings more effectively for their own benefit, technology is displacing humans in the workplace. The question is who will benefit now. Automated or otherwise technology-enabled services can increase profit margins for companies, while representing for users cheaper, more convenient or more reliable options than those produced exclusively by humans. But, of course, this comes at a high cost for the humans who previously filled those roles. People all over the world have embraced ride-sharing and transport services such as Uber, to the detriment of traditional taxi drivers. In stock trading, 79 per cent of market transactions are now performed by software, according to Frank Zhang of the Yale School of Management, reflecting the hope that machines will be able to identify patterns more effectively than a human could – a hope that may have contributed to the recent stock market correction.

US President Donald Trump has said America's legislators must "do something" to tackle the issue of violence in video games. Speaking at a White House meeting of local and state officials on security in the aftermath of the massacre at Marjory Stoneman Douglas High School in Parkland, Florida, the President criticised gaming culture and the ease with which young people can see films featuring graphic violence and bloodshed. "We have to look at the Internet because a lot of bad things are happening to young kids and young minds and their minds are being formed, and we have to do something about maybe what they're seeing and how they're seeing it," he said. "I'm hearing more and more people say the level of violence on video games is really shaping young people's thoughts. And you go the further step, and that's the movies. You see these movies, and they're so violent. And yet a kid is able to see the movie if sex isn't involved, but killing is involved, and maybe they have to put a rating system for that."

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

First, the futurist Amy Webb told the audience of journalists, librarians and foundation managers that they could easily be duped by the ever-growing purveyors of artificial intelligence. Images of their faces could be affixed to others' bodies, their voices to impostors. Media people have acknowledged to pollsters that they are so focused on the present that they don't pay close attention to what might be in store for them in five, 10 or 20 years. Later in her talk Wednesday before the Knight Media Forum in Miami, Webb told people of color that they weren't thought about when the creators of self-driving cars, GPS navigators, robotics and other such technologies were being developed. "My question is, what does all this mean for communities of color?" (video) asked Sara Lomax-Reese, president and CEO of black talk-formatted WURD radio in Philadelphia. She was one of about 500 at the sold-out conference sponsored by the John S. and James L. Knight Foundation. "It's not good," replied Webb. "Any person of color who's ever felt invisible, you're totally invisible to the networks. Right?" said the author of the 2016 book "The Signals Are Talking: Why Today's Fringe Is Tomorrow's Mainstream: Forecast and Take Action on Tomorrow's Trends, Today."

After recent school shootings in parts of the country and more than a dozen threats made against school districts here in Central Pennsylvania, many wonder how safety within schools could be improved. A professor from Harrisburg University says he knows something that could help: technology. Advanced technology is right at our fingertips, and Ron Jones, a cyber security professor at Harrisburg University says lawmakers should be pushing to have it within our schools. "I don't see anybody in any political spectrum standing up, making that kind of profound statement," he said. Jones says technology like facial recognition could save lives.

Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional, costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters.

In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from its counterparts that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate $\mathcal{O}(1/T^2)$. Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, leading eigenvalue computation, and neural networks. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha.