New analytics engine for users allows anyone to receive fast, accurate imaging analysis for key clinical conditions, by simply uploading their scans to Zebra's online system Zebra Medical Vision (https://www.zebra-med.com/), the leading machine learning imaging analytics company, is launching Profound (http://profound.zebra-med.com) The company's new service allows people to upload their medical imaging scans such as CTs and Mammograms to Zebra's online service, and receive an automated analysis for key clinical conditions. This Smart News Release features multimedia. "We are all anxious about our health. Undergoing an imaging scan such as a CT or a Mammogram is stressful for many people, often compounded by a long wait for results, with additional follow-up tests and examinations." said Elad Benjamin, co-founder and CEO of Zebra Medical Vision.

Computer algorithms can interpret echocardiographic images and distinguish between two similar heart conditions affecting young athletes, according to research published in the Journal of the American College of Cardiology. Researchers from the Icahn School of Medicine at Mount Sinai tested three different machine-learning algorithms for their effectiveness in the discrimination of physiological versus pathological hypertrophic cardiomyopathy. Pathological hypertrophic cardiomyopathy (HCM), in which a portion of the myocardium enlarges, leading to impaired heart function, is the leading cause of sudden death in young athletes. Distinguishing between it and physiological hypertrophy (heart enlargement often due to exercise) generally requires testing for the two conditions with interpretation by a highly trained cardiologist, according to an announcement. "This demonstrates how machine-learning models and other smart interpretation systems could help to efficiently analyze and process large volumes of cardiac ultrasound data, and with the growth of telemedicine, it could enable cardiac diagnoses even in the most resource-burdened areas," said study author Joel Dudley, Ph.D.

Since I joined Stack Exchange as a Data Scientist in June, one of my first projects has been reconsidering the A/B testing system used to evaluate new features and changes to the site. Our current approach relies on computing a p-value to measure our confidence in a new feature. Unfortunately, this leads to a common pitfall in performing A/B testing, which is the habit of looking at a test while it's running, then stopping the test as soon as the p-value reaches a particular threshold- say, .05. This seems reasonable, but in doing so, you're making the p-value no longer trustworthy, and making it substantially more likely you'll implement features that offer no improvement. How Not To Run an A/B Test gives a good explanation of this problem.

Technology is advancing so rapidly that we will experience radical changes in society not only in our lifetimes but in the coming years. We have already begun to see ways in which computing, sensors, artificial intelligence and genomics are reshaping entire industries and our daily lives. As we undergo this rapid change, many of the old assumptions that we have relied on will no longer apply. Technology is creating a new set of rules that will change our very existence. Digitization began with words and numbers.

Innovative technology has developed to enhance human lives whether that be for better security, organisation, energy usage, entertainment or education, the list goes on. . . One place where all of these prospective improvements come together is in your home. Through the Internet of Things (IoT), developers have sought to make our home lives seamlessly compatible with the technology we use, from coffee machines to highly advanced house-bots. Recently, companies like Google, Apple and Amazon have jostled to become the leader in Smart Home devices, offering Artificially Intelligent, IoT connected products like Echo, Nest and Google Home. According to a survey by Accenture, 69% of homes will be installed with a connected device of some kind by 2019.

These days, it seems that nearly everything is a smart product. We have smart boards in K-12 classrooms, smart watches, and even smart refrigerators. You can immediately tell that manufacturers love to use this misguided descriptor whenever they integrate modern technology, like touch screens or internet connectivity, to a previously existing product. Do products like these deserve this term? What exactly makes them smart?

Automation and algorithms already play a big role in the media industry. Programmatic advertising, algorithm-operated front pages and machine translation are some of the ways automation has infiltrated the news desk. So-called "robot journalists" are another blooming example of automation in the newsroom. The name is a bit disingenuous, since it's not really a robot, nor really a journalist, but an algorithm. By the use of Natural Language Generation (NLG), the algorithm generates text from a pool of data, and writes the text based on a set of predefined rules and templates.

The wildebeest, hippos, gazelles and warthogs of Southern Kenya have a little less to worry about. Wildlife advocates began testing an advanced system this spring to better identify and apprehend poachers. They say the technology has led to dozens of arrests that wouldn't have happened otherwise. Brian Heath, chief executive of the Mara Conservancy in Kenya, believes expanding the use of the technology throughout Africa could significantly improve anti-poaching efforts. "Our rangers now feel completely disadvantaged and blind without it," Heath said. "They get a huge amount of reassurance by having it and the ability to [better] see and identify people and animals."

The many and old systems of record: Some banks are making major investments (in the billions) in modernizing and federating systems of record to unlock the extraordinary customer data value – giving us a glimpse of what leaders are doing (and what fast followers and others will need to contemplate) to respond to customer dynamics. New systems of insight: Some banks are making early investments in artificial intelligence and machine learning to intelligently harvest and employ the treasure trove of customer transactional and behavioral data. These banks realize gains now and put in place systems that continuously learn and improve experiences. New systems of engagement: Banks are faced with a diminishing relationship equity that threatens growth, but the answer is not more human touchpoints for customers increasingly seeking digital engagement. Rather, leveraging investments in systems of record and systems of insight, banks will implement intelligent, predictive services that can deliver just-in-time value to customers – whether that is video chat, conversational interfaces, or intelligent agents – that exploit technology to help form more valuable, stickier relationships.

A few weeks ago I posted a map of the Artificial Intelligence landscape in Ireland. The map is a visual representation of resident Irish companies in the A.I. space. I decided to go a step further and put together an overview of the Irish Artificial Intelligence industry. I used the companies on the map to form the basis of the findings covered in this blog. I use the phrase'Artificial Intelligence' as an umbrella term to categorise the industry.