MIT has published details of "VoxelMorph", a new machine-learning algorithm, which is over 1,000 times faster at registering brain scans and other 3-D images. Medical image registration is a common technique that involves overlaying two images – such as magnetic resonance imaging (MRI) scans – to compare and analyse anatomical differences in great detail. If a patient has a brain tumour, for instance, doctors can overlap a brain scan from several months ago onto a more recent scan to analyse small changes in the tumour's progress. Unfortunately, this process can often take hours, as traditional systems meticulously align each of potentially a million pixels in the combined scans. In a pair of upcoming conference papers, however, researchers from the Massachusetts Institute of Technology (MIT) describe how to overcome this problem.

Abstracting away low-level infrastructure by one-click deployment and automatically provisioning the entire stack to run an application. Abstraction and automation are only possible if we come up with common patterns and turn these patterns into a single concept. Developers will lose some flexibility, but gain a lot of speed and don't have to know about the underlying details. My job with the Mendix R&D team is to find the right patterns to include in our platform and to balance speed, ease-of-use, flexibility, and control. The result should be that we continuously improve the productivity of a broad spectrum of developers. It is easy to say that everyone needs to contribute to software because it is the core of the business, but how do we get all these people started and productive? That's where Machine Learning comes into play in the form of AI-assisted development. It is the logical next step for low-code platforms as it adds the next level of abstraction and automation.

Researchers from the Massachusetts Institute of Technology (MIT) in Cambridge have been studying a machine learning algorithm they say makes the process of medical image registration more than 1,000 times faster.

Researchers on Microsoft's Bing team have developed a novel way of generating high-quality data for training machine learning models. In a blog post and paper published ahead of the Computer Vision and Pattern Recognition Conference (CVPR) in Salt Lake City, they describe a system that can discriminate between accurately labeled data and poorly labeled data with impressive consistency. "Getting enough high-quality training data is often the most challenging piece of building an AI-based service," the researchers wrote. "Typically, data labeled by humans is of high quality (has relatively few mistakes) but comes at high cost -- both in terms of money and time. On the other hand, automatic approaches allow for cheaper data generation in large quantities but result in more labeling errors ('label noise')."

Commodity artificial intelligence-as-a-Service (AI-aaS) offerings are popping up everywhere. Just as you can whip out a credit card and spin up a virtual data center in Amazon, Microsoft, or Google's cloud, you can now call on previously trained machine learning clusters to handle your AI chores. Using an API, you can upload a photo library to Google Cloud Vision or Amazon Rekognition to have the program scan it for objects, faces, logos, or terms of service violations in seconds, for fractions of a penny per image. Any business can now deploy the same technology used by the Google Photos app and Amazon Prime Photos to automatically categorize and label smartphone snaps based on the people, objects, and landmarks inside them. Real estate companies use image recognition to allow prospective home buyers to search for houses whose appearance pleases them. Car companies like Kia use AI to customize marketing campaigns based on the photos people post to social media.

The applications of image analytics are endless. Organizations are starting to realize the possibilities of how to extract value from unstructured data, such as images or video footage, to create a new and enticing customer experience within retail, entertainment, transportation and airport security, insurance claims, and more. Here are five image analytics applications that are unexpected, disruptive, and creative. On June 26th, I'll be talking about image analytics use cases at the Boston Area SAS Users Group in my Image Processing: Seeing the World through the Eyes of SAS Viya talk. Curious to know who attended the Royal Wedding?

Our brains are wired in a way that they can differentiate between objects, both living and non-living by simply looking at them. In fact, the recognition of objects and a situation through visualization is the fastest way to gather, as well as to relate information. This becomes a pretty big deal for computers where a vast amount of data has to be stuffed into it, before the computer can perform an operation on its own. Ironically, with each passing day, it is becoming essential for machines to identify objects through facial recognition, so that humans can take the next big step towards a more scientifically advanced social mechanism. So, what progress have we really made in that respect?

Intelligent machines (don't get it literal) are everywhere. Machine learning has developed, somewhat maturely, pretty fast in the last two decades. The applications of Machine Learning are virtually everywhere. From computers playing and winning in highly competitive games to self-driving cars to surveillance to drones and most of all in medical science, financial sector, security sector, we see a rising trend of Machine Learning applicability. "My personal challenge for 2016 is to build a simple AI -- like Jarvis from Iron Man -- to help run my home and help me with work. Artificial intelligence may seem like something out of science fiction, but most of us already use tools and services every day that relies on AI. When you do a voice search on your phone, put a check into an ATM, or use a fitness tracker to count your steps, you're using basic forms of pattern recognition and artificial intelligence. More sophisticated AI systems can already diagnose diseases, drive cars and search the skies for planets better than people. This is why AI is such an exciting field -- it opens up so many new possibilities for enhancing humanity's capabilities."

Last month an image purporting to show children in cages as a result of current immigration policies went viral on social media, accelerated by a number of high profile journalists, activists and former government officials who shared it widely – their visibility and stature leading many to trust the image at face value without the level of suspicion and verification that users might apply to other viral images. The image was real, but taken out of context and spread virally before users began to realize it actually dated from a 2014 news article. Yet, when I first saw the image I simply right-clicked on it and ran a reverse Google Images search that immediately turned up the original 2014 source. Could social media outlets like Twitter and Facebook automate such image searches to help combat fake news at scale? Social media today is an ocean of false and misleading information spread for nefarious purposes, but far more often by well-meaning individuals who share first and ask questions later.

The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important type of data, namely rank data, has received very little attention in data mining so far. In this paper, we therefore addresses the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent rankings and dependencies between such rankings in the form of association rules. Algorithms for mining frequent rankings and frequent closed rankings are proposed and tested experimentally, using both synthetic and real data.