Computers Don't Know When You Are Happy--Apple Is Adding a Previously Unseen Dimension To Your Device From the moment you are born, assuming normal eyesight, we open our eyes and fixate on the 43 muscles that control 1000s of nuances of facial expressions and emotion intent in the face of our parents. They inform a reaction to how to interpret the world, an extended sensor to help learn the basic emotions and reactions to the world around us. "Emotient is the leading authority on facial expression recognition and analysis technologies that are enabling a future of emotion aware computing." In the Spring of 2013 a team of scientists and researchers at the Machine Perception Lab at University of California, San Diego, was forming the technology and the basic elements of what was to become Emotient. The founding team were widely regarded as spearheading the use of machine learning for facial expression analysis with over 20 years of experience pioneering machine learning and computer vision technology for facial behavior analysis. The team has published hundreds of peer reviewed scientific publications, starting in 1995, which have been cited by thousands of other researchers in the field. Building around the work of Paul Ekman, Ph.D.[1] a pioneer in the study of emotions and facial expressions, and a professor emeritus of psychology in the Department of Psychiatry at the University of California Medical School (UCSF) where he has been active for 32 years, Emotient used AI to machine learn his ground breaking research in micro-emotions.

Justine Cassell has taken her virtual assistant Sara on a road trip. They're in Tianjin, China, where Carnegie Mellon University's associate dean of technology strategy and impact traveled to offer a glimpse of tomorrow at this week's Annual Meeting of New Champions. Sara, for "socially aware robot assistant," has spent the past several days greeting hundreds of people coming to the event, hosted by the World Economic Forum, at a station showcasing the office of the future. A life-size face and torso on a big-screen TV, Sara served as the front end to the event app. That presentation might make you think of Max Headroom, the stuttering AI character from the 1980s show.

A 750,000 grant will enable several engineers to employ the super-sensitive sense of smell in locusts to construct a bio-robotic nose of sorts. The problem is that biological systems possess a level of complexity that cannot be reached by their AI counterparts. While the sense of olfaction is a pretty primitive one, it extends across the board in all species. It is almost as if the field of biology made extra room for sensing chemicals in the air in order to warn the species of any danger or prey in the locality. A thorough understanding of the olfactory sense is very crucial for artificial intelligence.

MedyMatch Technology, a startup from Israel that specializes in medical imaging analysis for emergency medicine, has its first U.S. hospital partner. Capital Health, a two-hospital system in New Jersey, will deploy MedyMatch's artificial intelligence-based analytics in the emergency department and help the Tel Aviv-based vendor develop a clinical decision support tool for stroke care. To accomplish the latter, Capital Health, based in Hopewell Township, New Jersey, has agreed to provide MedyMatch with anonymized data from patients, the organizations said Monday. "The data Capital Health will provide will allow us to move closer to providing this decision support tool which can help ensure appropriate diagnosis, critical for treatment," MedyMatch Chairman and CEO Gene Saragnese said in a prepared statement. Saragnese was CEO of Philips Imaging before joining the startup a year ago.

Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives. Or, like Stephen Hawking, do you see little hope for us all. On Monday, the famed physicist caught up with famed softball interviewer Larry King. They'd last talked six years ago, so has anything improved? "We have certainly not become less greedy or less stupid," Hawking mused.

In 1986, while pursuing my Master's Degree in Computer Science, I selected Artificial Intelligence (AI) as my area of emphasis and my thesis topic. During that time, I learned to program systems using Boolean logic, syntax trees and relational databases to model "intelligent" decision making, like solving a problem for a monkey to use a chair and a stick to reach bananas hanging from the ceiling. This enabled the machine to foresee the best path to achieve a desired outcome. While not exactly "intelligence," this fast comparison of alternative options was unprecedented. Many others shared this view that in the future, science and technology would create systems that would drive massive impacts and societal change, but timelines were foggy.

An A.I. named "ALPHA," made by a company called Psibernetix, has apparently impressed the U.S. Air Force by repeatedly splashing a (human) fighter pilot in dogfight simulations. I swear I'm not tricking you into reading my movie script pitch here. Retired U.S. Air Force Colonel Gene "Geno" Lee is a U.S. Fighter Weapons School graduate, an experienced combat pilot, and an instructor who's apparently trained thousands of other pilots in the American armed services. He's shot down his share of targets, simulated and presumably otherwise, but in a series of simulated air combat missions against ALPHA he could not prevail, Lee told the University of Cincinnati Magazine: I was surprised at how aware and reactive it was. It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment.

The moment you say K-means, it indicates you have knowledge of number of clusters (i.e. K) in advance and you are not going to change it later on. I believe, what you intend to ask is, is there an automatic way to adaptively changing the number of clusters as new data arrives. Normally, in online clustering you start with one sample (hence one cluster) and based on *some* criteria, you either merge or break clusters to adaptively change number of clusters; this process is not online k-means clustering but only online clustering. In online K-means clustering, you update the cluster center information for every sample and do not wait for all the samples to arrive (or else it becomes traidtional offline k-means clustering).

Integrating Azure Data Lake with other services in the Microsoft Cortana Intelligence Suite, especially Azure Machine Learning, enables you to build end-to-end advanced analytics solutions and intelligent applications that impact revenue-critical decisions and actions. An end-to-end complete solution involves Azure Event Hub and Azure Stream Analytics to provide highly scalable data ingestion and event processing service, uses ADLS to archive native data, and utilizes ADLA to transform native data into structured data that can be used by Azure ML to develop business insights. Azure ML provides a fully managed cloud service to build, deploy and share advanced analytics, including predictive maintenance, energy demand forecasting, customer profiling, anomaly detection and many other possibilities. Advanced analytics results are stored in Azure SQL Data Warehouse, which provides high-performance query on your structured data. Power BI renders visualization on your streaming data and data in Data Warehouse to show business insights.

Many IT executives in North America currently have--or plan to have--machine learning programs in place, according to research. Predictive analytics and recommender systems are some of the leading implementations. Data from 451 Research and Blazent revealed that more than two-thirds (67.3%) of respondents said they currently have machine learning programs for predictive analytics in place, or are planning to implement them. Additionally, 66.7% said they are currently using machine learning for recommender systems--or are planning to. Furthermore, more than half (58.9%) of IT executives said they are using machine learning for cluster analysis and segmentation currently in place or plan to soon.