Recognizing emotions in facial expressions is relatively straightforward for humans, and in recent times machines are getting better at it too. The applications of emotion-detecting computers are numerous, from improving advertising to treating depression, the possibilities are limitless. Motivated mainly by the impact in mental health that such technology can have, I started building my own emotion recognition technology. In a previous post I described a quick test in which I used ideas drawn from research on how facial expressions are decomposed. In this simplified scenario a computer distinguished between sad and happy faces by detecting facial landmarks (points of eyes, mouth, etc …) and using one simple geometric feature of the mouth (representing a Lip Corner Puller).

If its latest video is anything to go by, Magic Leap's technology is going to be a very big deal in the augmented reality arena, placing interactive digital objects in the real world. The hardware is yet to be revealed (but it'll presumably use a headset). This video, and previous footage, takes place entirely from the wearer's point-of-view. The mysterious Google-backed startup released a video in March that showcased an augmented-reality video game that involved shooting virtual robots that appeared in the wearer's real-world environment, as opposed to virtual-reality that takes place in a fully digital environment. At the time, the seamlessness of the video led to speculation that it was just a concept, or had been doctored in some way.

Over time, Caroline's focus has morphed from tweaking staffing schedules and other operational details to influencing clinical decisions. His group is developing a portal that uses machine learning and natural-language processing to push information to physicians and researchers about which patients may qualify for one of the hundreds of clinical trials under way at the hospital at any given time. Of course, plenty of companies are eager to mine health care data, from startups to IBM. But Caroline's team has unfettered access to raw data and expertise that outsiders must beg to receive. When engineers in his group are stumped on how to write an algorithm that properly classifies triple-negative breast cancers, they catch a shuttle to the hospital's main campus and consult with physicians.

The development centre of the global telecommunications operator Orange, Orange Silicon Valley (OSV), starts cooperation with Finnish artificial intelligence (AI) startup Valossa. Orange, which is one of the world's leading telecommunications operators, will be using Valossa's Val.ai platform for identifying objects and people in real-time across more than 25 simultaneous HD video streams. Val.ai platform is based on years of research at the computer vision and AI labs of the University of Oulu in Finland. The platform is capable of analysing movies and streaming videos in real-time and identifying thousands of concepts, like places, objects and unique topics, from any video stream. "We are excited to collaborate with Valossa and OSV to bring High Performance Server and Storage platforms, which normally are only available in Supercomputing Data Centers, to the Media and Entertainment industry," says Andy Lee, Echo Streams' Director of Product Marketing.

The most important question for Game of Thrones fans belongs to quantum mechanics: Schrödinger's Jon Snow. Or in other words, is...

Artificial intelligence and wearables thrive across healthcare sectors, says Frost & Sullivan MOUNTAIN VIEW, Calif. While pharma companies have been seeking tools for tracking the effectiveness of therapeutics, imaging companies have been augmenting the interpretability of scans through cognitive pattern interpretation. For complimentary access to more information on this research, please visit: http://frost.ly/2b. Approximately 24 percent of consumers currently use mobile apps to track health and wellness, 16 percent use wearable sensors and 29 percent use electronic personal health records. Significantly, 47 percent of consumers reported they would consider using wearables in the near future, prompting technology developers to design wearables with advanced sensing, capture and analytical functionalities.

There is an unreasonable amount of information that can be extracted from what people publicly say on the internet. At Heuritech we use this information to better understand what people want, which products they like and why. This post explains from a scientific point of view what is Knowledge extraction and details a few recent method on how to do it. Highly structured databases make it easy to reason with and can be used for inference. For example in WikiData or YAGO, entities are isolated and linked together with relations.

PRESS RELEASE–CAMPBELL, Calif.–[24]7 announced yesterday that it has launched the [24]7 Customer Acquisition Cloud, a suite of marketing technology solutions that enable marketers to better connect with and influence consumers at every touch point. The suite is based on the acquisition of Campanja, a paid search bid optimization company, and EngageClick, a personalized marketing company. Combined with [24]7's predictive customer engagement platform, the Customer Acquisition Cloud gives marketers insight into the entire customer journey, from initial search to the point of purchase. Marketers in large organizations are increasingly challenged to provide the personalization that consumers have come to expect. The Customer Acquisition Cloud empowers companies to achieve more effective customer acquisition at lower cost, increase their understanding of how media spending is driving revenues through marketing campaigns and leverage the consumer interactions that happen annually on the [24]7 platform.

Artificial intelligence (see the Wikipedia definition), specifically machine learning, is an increasingly integral part of many industries, including marketing. Here are a whole bunch of case studies and use cases, as a complete primer for AI in our industry. Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Uniting information from diverse datasets is a common use of AI.

Today's security systems usually fall into one of two categories: man or machine. So called "analyst driven solutions" rely on rules created by human experts and therefore miss any attacks that don't match the rules. Meanwhile, today's machine learning approaches rely on "anomaly detection," which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway. But what if there was a solution that could merge those two worlds? What would it look like?