Samsung has announced super-fast removable data storage cards that could one day replace the slower micro-SD cards in devices. The UFS card, based on the Universal Flash Storage 1.0 Card Extension standard, will come in capacities from 32GB to 256GB. The storage media could be used in cameras, drones, robots, virtual reality headsets and ultimately, even mobile devices. There is a need for faster and high-capacity removable storage in electronics, and UFS media fits that requirement. UFS cards can blow away micro-SD media in performance by moving data in and out of the card much faster.

A recently discovered vulnerability in a D-Link network camera that allows attackers to remotely take over the device also exists in more than 120 other D-Link products. The vulnerability was initially discovered a month ago by researchers from security start-up firm Senrio in D-Link DCS-930L, a Wi-Fi enabled camera that can be controlled remotely through a smartphone app. The flaw, a stack overflow, is located in a firmware service called dcp, which listens to commands on port 5978. Attackers can trigger the overflow by sending specifically crafted commands and then can execute rogue code on the system. The Senrio researchers used the flaw to silently change the administrator password for the Web-based management interface, but it could also be used to install malware on the device.

Scientists have developed a new artificial intelligence system that can detect early forms of Alzheimer's disease by combining machine learning methods with a special MRI technique. Machine learning is a type of artificial intelligence that allows computer programmes to learn when exposed to new data without being programmed. Researchers coupled machine learning methods with a special magnetic resonance imaging (MRI) technique that measures the perfusion, or tissue absorption rate, of blood throughout the brain to detect early forms of dementia, such as mild cognitive impairment (MCI). "MRI can help with the diagnosis of Alzheimer's disease. However, the early diagnosis of Alzheimer's disease is problematic," said Alle Meije Wink, from the VU University Medical Centre in Amsterdam.

Google's TensorFlow machine learning engine is now available on your iPhone and iPad. The search giant has added support for Apple's iOS to its TensorFlow 0.9 released candidate, on GitHub. Support for iOS follows earlier backing for the framework on Google's own Android smart phone operating system. Other changes for TensorFlow 0.9 include support for Python 3.5 binaries and added support for processing on GPUs on MacOS. TensorFlow is Google's machine-learning library for numerical computing using data flow graphs, released late last year by Google under an Apache 2.0 license.

The code in this repository provides a framework for solving arbitrary separable convex optimization problems with Alternating Direction Method of Multipliers (ADMM). Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. The framework is built over Spark and is generic: to apply it to an arbitrary separable convex problem, a developer needs to implement only three functions (one that reads data from a file, one that evaluates the objective function, and one that solves a local optimization problem with an additional proximal penalty term). An example implementation of logistic regression is included in the code. Updated spark installation instructions can be found here.

It's no secret that the healthcare system in this country has had a few hiccups in recent years. Whether it be the long lines at emergency rooms or the expensive bills that hit you where it hurts, getting sick is one of the most costly and unpleasant experiences in the US. And with rising medication prices, the problem is only getting worse. Fortunately, one company has taken matters into their own hands by utilizing artificial intelligence to expedite the medication distribution process. And it seems to be working.

Plus, bots live in mobile messaging apps -- the fastest-growing online sector within the social landscape over the past five years. And like any advertising campaign, branded chat campaigns perform best when rooted in strategy -- specifically, a mobile messaging strategy. This means ensuring everything from the objectives, key performance indicators (KPIs), key messages, and creative content are aligned and working cohesively. Like any other branding, marketing, or advertising campaign, chatbot campaigns perform best when wrapped with other engagement experiences.

Elon Musk, owner of SpaceX, Tesla, and the only guy more Ironman than Rob Downey Jr, worries about smart computers taking over the world. Maybe he's right, but AI (or Artificial Intelligence for those of you acronym fatigued), is already a lot closer to home. I see it every day in my digital marketing life, exposed to machine learning that both scares and comforts me. Here are 5 ways I see AI racking up big wins in the digital marketing space and why you should be worried if you're on the sidelines: Win 1: AI decisioning Know what computers do really well? In a world of real time, always on marketing, the ability to constantly learn what content, campaigns, offers, channels, you name it, a customer is engaging with, and conversely not engaging with (read: the Fail Fast bit), means that AI gets smarter and smarter about decisions like what NOT to talk to individual customers about and how NOT to deliver the message.

It's impossible to talk about artificial intelligence without mentioning IBM's Watson. A pioneer in cognitive computing, the American computer giant has found multiple health applications for Watson. Pascal Sempé, senior sales consultant for Watson Health Solutions in France, explained how Watson functions and what's at stake. Pascal Sempé: Watson is a tool that helps the doctor, certainly not one that tells the doctor what to do. Using Watson doesn't mean the doctor won't examine patients or ask questions.

When most of us think of artificial intelligence in the workplace, we imagine automated assembly lines of robots managed by an algorithm. LinkedIn's Reid Hoffman has a different idea. In an essay for MIT Sloan's Management Review, Hoffman describes human applications for the technology. Among other things, it would help to use data science to improve the way we onboard new team members, organize workflow, and communicate about performance. Addressing the question of how technology will change management practices over the next five years, Hoffman explains how the use of a "knowledge graph" will become standard management practice.