Leaked marketing materials may have revealed some new interesting information about the Huawei P11. Apparently, Huawei's next flagship Android smartphone might arrive with a triple-lens camera system. The rumor on the Huawei P11's camera was based on some images from a digital artist that's working at one of Huawei's creative agencies, according to notorious leaker Evan Blass (@evleaks). The images are part of "PCE Series" ads, and they appear to confirm that Huawei is once again working with Leica, the German company known for high-end cameras and lenses. The involvement of Leica with the Huawei P11 isn't a huge surprise since Leica has already co-engineered the dual-camera system on Huawei's previous smartphones.

Artificial intelligence companies have shown off their tracking technology at the World Internet Conference in China - giving a glimpse of how citizens are being watched everyday. Companies such as China's facial recognition start-up Face showed how their technology is already being used to identify and describe people. For the first time, the'Big Brother' conference was attended by top executives like Tim Cook of Apple, Sundar Pichai of Google and Jack Ma of Alibaba. Guided by the hand of the state, tracking technology has become widely accepted in China and this vast data collection could mean these companies have an edge over American competitors. One company present at the World Internet Conference in Wuzhen was Face which used its technology to identify people in the audience's gender, hair length, colour and clothing.

With the strong demand for iPhone X, a Taiwanese supplier is predicting that the Cupertino giant's rivals will also adopt the 3D sensing technology Apple used on the 10th anniversary iPhone to their flagship handsets. The supplier is even predicting a rise in the demand for 3D sensing modules in the next 5 to 8 years. On Tuesday, DigiTimes reported that the chairman and CEO of digital camera and imaging solution supplier Altek, Alex Hsia, is expecting strong demand for 3D sensing modules in the following years now that Apple has introduced Face ID with its iPhone X flagship phone. "The structure-light 3D sensing technology adopted by Apple's iPhone X features high accuracy at short distances, and other first-tier smartphone vendors are expected to follow suit by launching 3D sensing-enabled devices," Hsia said. The CEO predicts that consumers will see more smartphones with 3D sensing technology starting next year.

Artificial intelligence is coming to your phone. The iPhone X has a Neural Engine as part of its A11 Bionic chip; the Huawei Kiri 970 chip has what's called a Neural Processing Unit or NPU on it; and the Pixel 2 has a secret AI-powered imaging chip that just got activated. So what exactly are these next-gen chips designed to do? As mobile chipsets have grown smaller and more sophisticated, they have started to take on more jobs and more different kinds of jobs. Case in point, integrated graphics -- GPUs now sit alongside CPUs at the heart of high-end smartphones, handling all the heavy lifting for the visuals so the main processor can take a breather or get busy with something else.

Artificial intelligence is coming to your phone. The iPhone X has a Neural Engine as part of its A11 Bionic chip; the Huawei Kiri 970 chip has what's called a Neural Processing Unit or NPU on it; and the Pixel 2 has a secret AI-powered imaging chip that just got activated. So what exactly are these next-gen chips designed to do? As mobile chipsets have grown smaller and more sophisticated, they've started to take on more jobs and more different kinds of jobs. Case in point, integrated graphics--GPUs now sit alongside CPUs at the heart of high-end smartphones, handling all the heavy lifting for the visuals so the main processor can take a breather or get busy with something else.

With the widespread use of information technologies, information networks have increasingly become popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of society, information diffusion, and different patterns of communication. However, the large scale of information networks often makes network analytic tasks computationally expensive and intractable. Recently, network representation learning has been proposed as a new learning paradigm that embeds network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a thorough review of the current literature on network representation learning in the field of data mining and machine learning. We propose a new categorization to analyze and summarize state-of-the-art network representation learning techniques according to the methodology they employ and the network information they preserve. Finally, to facilitate research on this topic, we summarize benchmark datasets and evaluation methodologies, and discuss open issues and future research directions in this field.

Over a third of the nearly 22 million comments that poured into the Federal Communications Commission regarding its plan to repeal net neutrality protections included one of seven identical messages. More than half were associated with duplicate or temporary emails, including some 7,500 affiliated with the address "example@example.com." Dozens included references to the animated film Bee Movie, a film about a disillusioned worker bee that has become fodder for several popular memes. Roughly one million comments came from Pornhub.com email addresses. And more than 7,000 comments were submitted by a gentleman--or woman--named, simply, The Internet.

Huawei just unveiled a new smartphone called the Honor V10, and it also announced that it is working on its own version of Apple's Face ID technology. Huawei claims that its new facial recognition technology is better than Apple's and it can also be used for animated emojis. Huawei's new facial recognition technology is made possible with the depth-sensing camera system. The Chinese phone maker says that it uses a combination of infrared and a projector to create a 3D map of a user's face. Although the process is similar to Apple's Face ID technology on the iPhone X, Huawei's camera system can capture 300,000 points of the user's face in 10 seconds.

Huawei has a history of trying to beat Apple at its own game (it unveiled a "Force Touch" phone days before the iPhone 6s launch), and that's truer than ever now that the iPhone X is in town. At the end of a presentation for the Honor V10, the company teased a depth-sensing camera system that's clearly meant to take on Apple's TrueDepth face detection technology. It too uses a combination of infrared and a projector to create a 3D map of your face, but it can capture 300,000 points in 10 seconds -- that's 10 times as many as the iPhone X captures. It's secure enough to be used for payments (unlike the OnePlus 5T), and almost as quick to sign you in as the company's fingerprint readers at 400 milliseconds. Even the silly applications of the tech promise to be better.

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help. For many businesses that offer subscription based services, it's critical to both predict customer churn and explain what features relate to customer churn.