DeepMind and Blizzard Entertainment are collaborating to open up StarCraft II to artificial intelligence and machine learning researchers globally. According to a DeepMind blog post by research scientist Oriol Vinyais, StarCraft II continues the series' renowned eSports tradition, as the original StarCraft was played in the late 1990s yet remains popular today. StarCraft is a good testing environment to work with because it provides a "useful bridge to the messiness of the real world," and the skills needed to play in this environment could transfer easily to real-world tasks, wrote Vinyais. DeepMind is looking to work with Blizzard in order to create "curriculum" scenarios, which means researchers will be faced with complex tasks that researchers will need to complete in order to get an agent up and running. Agents will play directly from pixels, and to get DeepMind there, a new image-based interface that outputs a simplified low-resolution RGB image data for the map and minimap was created, according to Vinyais.

The Russian machine learning firm Tselina Data Lab developed a deep learning-based camera algorithm called Fraudoscope that detects lies on facial emotions. Trained with CUDA and TITAN X GPUs, the lie-detecting app uses a high-definition camera to observe an interrogation and decode the results. The camera focuses on the interviewee -- the software maps changing pixels in the camera feed that correspond to breathing, pulse, pupil dilation, facial tics -- and the work-in-progress already has a 75 percent accuracy rate. As with traditional polygraph tests, Fraudoscope requires a set of calibration questions with well-known answers and the interviewee is also asked to imagine they've just won an Olympic medal – as they make up their imaginary answer, the system learn to recognize the individual's lie. The firm hopes one day the algorithm will be smart enough to not require calibration and if fed enough information, it may eventually be able to identify poker players and shoplifters from a glance.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

When you perform a Google search for every day queries, you don't typically expect systemic racism to rear its ugly head. Yet, if you're a woman searching for a hairstyle, that's exactly what you might find. A simple Google image search for'women's professional hairstyles' returns the following: Your questions answered by founders, experts and thought leaders in business, design and tech. Here, you'll find hairstyles, generally done in a professional setting by stylists. It returns what it thinks you're looking for based on contextual clues, citations and link data.

It will come as no surprise to anyone familiar with the technology world that the rate of cyber attacks, the development of malware, and the exploitation of zero-day flaws makes is very difficult for IT teams and security specialists to keep up with let alone get ahead of cyber threats. Research from Symantec noted that nearly one million new malware threats emerge daily, and while there are many tools to make detecting rogue code an easier process, dealing with such an enormous amount of new threats appears to be an almost insurmountable task even for the best security teams and anti-virus systems. The answer to this, and the potential future of cyber security, looks to be the use of machine learning and artificial intelligence (AI) to apply clever computers and smart software to a problem that leaves humans on the back foot in the fight against hackers. Rather than sift through data harvested from across IT networks, machine learning algorithms can be trained to detect certain malware and threat signatures and proactively sniff out threats, bypassing the need for cyber security experts to disappear into a warren of file paths and scripts to find tell-tale signs of malware. Webroot is one such cyber security company applying machine learning techniques to power its threat intelligence service without requiring resource sapping and time-consuming manual processes.

By Ashwini Anand When virtually everyone claims to use "artificial intelligence", "big data" or "machine learning" to "disrupt"/ "revolutionize" one industry or the other, I would not blame you for being sceptical about the much-touted AI revolution in Fintech. But, before you throw the baby out with the bathwater, let us delve a little deeper and understand if there is a real problem in lending and if AI can help solve it. Is there really a problem in the banking system? Banks and financial institutions, with some notable exceptions, are struggling with bad loans. According to India Ratings the average Impaired Asset Ratio - the sum of gross NPAs and restructured advances (a measure of the stress on a lender's balance sheet) stands at 12% of advances and is slated to rise to 12.5%.

"Every aspect of our lives will be transformed. In short, success in creating AI could be the biggest event in the history of our civilization." From self-driving vehicles to virtual assistants, artificial intelligence (AI) is evolving at a rapid pace. It has the potential for tremendous good – IBM's Watson improving cancer treatment with genomic sequencing as an example. Additionally, AI has been used to bring new art into the world – "Symphonologie" is an orchestra piece created with the help of AI.

Leading figures in the field of artificial intelligence discussed its present and potential future impact on individuals and nations at the John F. Kennedy, Jr. Forum Friday. Kennedy School lecturer and national security expert Juliette N. Kayyem introduced the topic by discussing the current prevalence of artificial intelligence and the significant shifts the technology may cause in relations between various groups and industries. She noted the tensions that may arise when trying to find a role for artificial intelligence in everyday life. Panelists then discussed how artificial intelligence has influenced their particular work and society more generally. Edward W. Felten, the deputy chief technology officer of the White House Office of Science and Technology Policy, commented on a recent White House report that explained the challenges faced when trying to incorporate artificial intelligence into the government.

Today, businesses need to be able to predict demand and trends to stay in line with any sudden market changes and economy swings. This is exactly where forecasting tools, powered by Data Science, come into play, enabling organizations to successfully deal with strategic and capacity planning. Smart forecasting techniques can be used to reduce any possible risks and assist in making well-informed decisions. One of our customers, an enterprise from the Middle East, needed to predict their market demand for the upcoming twelve weeks. They required a market forecast to help them set their short-term objectives, such as production strategy, as well as assist in capacity planning and price control.

Machine Learning for Artists ml4a.github.io is an in-development book about machine learning, being written by Gene Kogan (@genekogan) and Francis Tseng (@frnsys). The delays in the release schedule can be attributed to two factors. Changes in scope: initially, ml4a was to be a collection of reading materials for a single class, and now seeks to be more broadly useful, necessitating the development of new features and chapters. Over time, Demos and Guides were elevated from supporting materials to the book into full-fledged sections of their own. The goalposts keep moving: since the initial version of this page went up, we've seen AlphaGo, the announcement of TensorFlow, deep generator nets, synthetic gradients, stacked approximate regression machines, and many other major milestones in the field.