Forget PCs and servers: D-Wave Systems is looking into the future with its quantum computer, up to 1,000 times faster than an earlier model. The company will start shipping a quantum computer with 2,000 qubits, twice the size of its current 1,000-qubit D-Wave 2X. The D-Wave 2X is considered one of the most advanced computers in the world today. The 2,000-qubit quantum computer will be 500 to 1000 times faster than its predecessor, said Jeremy Hilton, senior vice president of systems at D-Wave. An even larger quantum computer based on a whole new processor design will come out two to three years after that, Hilton said.

Advanced cancer research is calling on techniques used by NASA scientists who analyze satellite imagery to find commonalities among stars, planets and galaxies in space. Scientists from NASA's Jet Propulsion Laboratory (JPL) use complex machine learning algorithms to identify similarities among galaxies that may otherwise be overlooked, NASA officials said in a statement. Using similar techniques, medical professionals are able to analyze a lung sample for common cancer biomarkers. However, analyzing a biopsy specimen for biomarkers is not the only way in which JPL's complex machine learning algorithms can be used in the medical field. Cancer researchers can also use the space exploration tools to identify common chemical or genetic signatures related to specific cancers, which could revolutionize strategies for early cancer detection.

With the recent success of depth cameras such as Kinect, gesture and posture recognition has become easier. Using depth sensors, the 3D locations of body-joints can be reliably extracted to used with any machine learning framework, specific gestures or posture can be modelled and inferred. Real world applications in Virtual Reality can be used for Yoga, Ballet training, Golf, anything related to activity recognition and proper postures. I also see application of it in the Architectural, Engineering, Construction and Manufacturing Industry by sending depth sensor data to the cloud to identify correct configurations. This is a proof of concept to detect pose "Y", "M", "C", "A" and stream the result back to the browser. This video explains a littlebit how it's hooked up together.

I am starting doing research on LSTM's. So far, all sources I have found just detail the use of some libraries and just mention that they use BPTT. I want to go into details of learning in LSTM's, an exact implementation with reasoning why they do things would be great.

Microsoft says it aims to help oncologists consider the individual elements of each patient's cancer at the genetic level. Google's UK subsidiary DeepMind isn't the only group using artificial intelligence to fight cancer by helping doctors with clinical decisions. Microsoft today launched a number of key cancer-fighting projects it has under way, showing off its application of machine learning to solve bigger challenges than identifying dog breeds. While privacy and regulation will slow the pace of adoption, AI will bring some profound changes to healthcare. One such application, called Project Hanover, is seeking to make personalized, precision cancer therapy available to all cancer patients by helping oncologists sift through reams of biomedical research papers faster.

Chatbots, are a hot topic and many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible. But with all the hype around AI it's sometimes difficult to tell fact from fiction. In this series I want to go over some of the Deep Learning techniques that are used to build conversational agents, starting off by explaining where we are right now, what's possible, and what will stay nearly impossible for at least a little while. Retrieval-based models (easier) use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context. The heuristic could be as simple as a rule-based expression match, or as complex as an ensemble of Machine Learning classifiers.

US consumers are getting more comfortable using mobile devices to manage their health, a new study finds. Even in the face of privacy concerns, Americans are increasingly sharing medical information, sending photos to their doctors, using fitness or activity trackers, and using AI to become active players in their healthcare. Ketchum conducted an online survey of 2,000 smartphone-owning Americans earlier this year, and found close to 58 percent of this group uses their phone to communicate with a medical professional. Almost half of respondents have a fitness, health or medication-tracking app, and 83 percent of people who use fitness or workout apps do so at least once per week. Of course, not everyone loves health apps – about a quarter of survey respondents said health and fitness tracking apps have made them feel bad, with 21 percent ending use of the apps.

At a recent Further With Ford event their futurist, Sheryl Connelly, talked about the growth of our world's mega-cities. She used Beijing China as an example. Beijing has 20 million people living in its metropolitan area. A typical driving commute is 5 hours! They have one highway with 50 lanes.

Analysts say a handful of technologies are poised to change our lives by 2021. While Forrester Research sees 15 emerging technologies that are important right now (see the full list here), five of them could shake things up in a big way for businesses and the public in general, according to Brian Hopkins, an enterprise architecture analyst with Forrester. "The technologies we selected will have the biggest impact on your ability to win, serve and retain customers whose expectations of service through technology are only going up," Hopkins wrote in the report. "Our list focuses on those technologies that will have the biggest business impact in the next five years." Of Forrester's larger list, which includes the likes of edge computing, security automation and real-time interaction management, the five that Hopkins pulled out to highlight have the greatest potential for disruption.

This is the pattern where intelligence lives with the data in the database. Imagine a core transactional enterprise application built with a database such as SQL Server. What if you could embed intelligence, i.e. advanced analytics algorithms plus data transformations, within the database itself, to make every transaction intelligent in real time? That's now possible for the first time with R and ML built into SQL Server 2016. At the Summit, we'll illustrate this with a fascinating demo of real-time predictive fraud detection and scoring in SQL Server.