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.

It has become a semi-annual tradition here at DATAVERSITY to publish our top twenty most popular data management content year-to-date. Though we monitor topic popularity daily, we like to stop every six months or so to do a deep dive into what subscribers are reading. Our top twenty includes Data Blogs, Articles, Data Dailies (News), and other formats published throughout the year. In January 2015 we merged DATAVERSITY.net We put the Data Topic category of Semantic Technologies under the larger new category of "Smart Data" along with Cognitive Computing, Machine Learning, Deep Learning, Internet of Things (IoT), and Artificial Intelligence.

OpenAI, a nonprofit created by Elon Musk and other tech entrepreneurs to make fundamental breakthroughs in artificial intelligence, has said that one of its big goals will be teaching robots to do the laundry and other household chores. OpenAI doesn't want to make robot hardware itself but, rather, to supply the brains for off-the-shelf bots. You might think that learning to fold underpants is a modest goal, but such dexterity and adaptability is one of the grand challenges of robotics. It also fits with the organization's stated objective to "advance digital intelligence in the way that is most likely to benefit humanity as a whole." Applying the sort of machine-learning techniques OpenAI is working on to robotics should, in fact, have huge practical benefits, and it will be a necessary component of any more general form of artificial intelligence.

Have you recently got a parking ticket? This chatbot might be able to help overturn it. The free service, called DoNotPay, has successfully challenged 160,000 parking tickets--around 4 million worth--in London and New York. It was launched just 21 months ago by London-born Stanford University student Joshua Browder, who says it took him three months to program and who describes DoNotPay as "the world's first robot lawyer." "As far as I know, it's the first of it's kind," Browder told Quartz.