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.

In our previous blog posts of this series, we have introduced Topic Models, BigML's latest resource that helps you find thematically related terms in your unstructured text data, explained how to use it through the BigML Dashboard and the API, and lastly showed how to apply Topic Models in a real-life use case. This post will focus on automating LDA workflows by using WhizzML, a DSL for Machine Learning that provides programmatic support for all the resources you work with in our platform. Let's dive in by creating a Topic Model and making a prediction with it. In BigML, you can perform single instance predictions (referred to as a Topic Distribution) or in batch mode, which is called Batch Topic Distribution. Firstly, we will create a Topic Model without specifying any particular configuration option, that is, relying on default settings.

AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. That's the finding of a new report from Tractica which also outlines what the authors say will be the top tend trends in AI implementations next year. The report's authors say, the majority of use cases they studied take existing processes like predictive maintenance, anomaly detection, algorithmic trading, customer service, search engine queries, or cybersecurity threat detection and apply machine learning techniques or other AI techniques that can adapt rules and provide better results than previous static rule techniques. "At the same time, AI is also enabling new capabilities like image classification or natural language understanding, which are being plugged into photo storage solutions, or into virtual digital assistants (VDAs). The majority of the new capabilities around vision and language are new, and are being offered as incremental improvements to consumer products and services using a freemium model, rather than creating new disruptive business models."

PARIS--(BUSINESS WIRE)--PIQ, a leading French start-up in sports wearables today unveiled a breakthrough innovation with the introduction of a genuine Artificial Intelligence interface dedicated to sports activities. After 2 years of R&D and €13m investments, PIQ's 50 engineers developed a revolutionary technology, protected by 10 international patents allowing to identify athletes' Winning Factors, highlighting the key strength they should leverage on to succeed. From a world where connected sports were limited to the capture of basic data, PIQ's two cutting edge innovations are opening new horizons to the Sport Wearables industry: The combination of GAIA and PIQ ROBOTTM enables athletes to identify their Winning Factors, highlighting the key strength they should leverage on to succeed. GAIA – GAIA is the first Artificial Intelligence system that autonomously understands sports movement. GAIA is capable of breaking down and analyze sports movements via specific motion-capture algorithms.

It may seem illogical, but letting the water flow between your fingers is the most effective way to swim. In what experts admit seems to go against common sense, spreading the fingers was found to be up to five per cent more efficient than keeping the fingers together. What scientists call the'rake' style is more efficient than the'paddle' or'closed' approach. Scientists believe they have cracked the secret to the perfect swimming stroke. Scientists from the Eindhoven University of Technology and colleagues found that spreading fingers increases the drag of the hand through the water.

Amazon, Baidu, Facebook, Google and Microsoft, among other technology companies, have been investing heavily in artificial intelligence and related disciplines like machine learning because they see the technology enabling services that become a source of revenue. Consultancy Accenture earlier this week quantified this enthusiasm, predicting that AI "could double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between man and machine" and by boosting labor productivity by 40 per cent. Certainly things could work out well for Accenture, which a day later announced a partnership with Google to help companies deploy Google technology like machine learning. It's as if the global services firm has a stake in the future it foresees. But the machine learning algorithms underpinning this harmonious union of people and circuits aren't secure. In a paper [PDF] presented in August at the 25th Annual Usenix Security Symposium, researchers at École Polytechnique Fédérale de Lausanne, Cornell University, and The University of North Carolina at Chapel Hill showed that machine learning models can be stolen and that basic security measures don't really mitigate attacks.

According to the Financial Times, Pablo Picasso once said, "Computers are useless. They can only give you answers." Unfortunately for us, computers may now be asking more questions than they answer. As a result, the possibilities are rather overwhelming, with answers more ambiguous and uncertain than straightforward. Similarly, we might ask ourselves where we draw the line when it comes to what we find ethically acceptable in terms of artificial intelligence (AI) as it relates to composition/creation in the worlds of art, writing, performing arts and music--as well as liberal arts education. Most of us are aware of music streaming services that select songs for us based on data about users' listening preferences.

Toronto-based IT software developer announced today the pilot program, Karen by Karen.ai, a cognitive recruiting chatbot that engages candidates throughout the application process, matches candidates to alternative positions and provides screening support before, during and after the recruiting process. "Karen derives concepts and personality traits from candidates' resumes and cover letters and assesses the level of engagement and gains deeper insights through a simple text chat conversation. This conversation improves the candidate's experience of the recruitment process and represents the company's brand," said founder Noel Webb. Webb also noted Karen can follow up with candidates during and after the requisition and provide alternative options within the company for which they may be better suited. While automated solutions have existed in recruitment for nearly a decade, Karen takes these automations one step further, because Karen can learn from conversations with candidates and predict other positions for which they might be a match.