The Arterys Cardio DLTM application is vendor agnostic and was developed using data from several thousand cardiac cases. The software produces editable automated contours, providing precise and consistent ventricular function in seconds. The trained deep learning algorithm was validated as producing results within an expected error range comparable to that of an experienced clinical annotator. This clearance enables Arterys to make use of its unique clinical annotation platform, which collects ground-truth data every time a user views a study on Arterys.com. In the future, the deep learning model can be optimized as new data is collected from all global users.

Welcome to the second in a new series of brief reports. Every Monday, we might look back at last week; look ahead to this week; share a few thoughts (our own or others); or discuss anything that catches our eye. Anything goes, so here goes. Last week I was in Hong Kong, but that region has probably been discussed enough for the moment. As part of that trip there was the 2017 StartmeupHK Festival, which produced a few interesting points from a presentation and a panel discussion.

He worked in isolation, hiding his progress from his teacher and his fellow scientists. Thus, when his Creature went on a murderous rampage, killing all of those close to him, there was no one to help Frankenstein destroy the creature or, at the very least, modify the Creature's behavior. When crisis struck, there was no one to whom Frankenstein could turn for guidance. And when Frankenstein died, his Creature continued to roam the earth, enraged and embittered, poised to wreak more damage. If Frankenstein had been a member of a research group, his fellow scientists could have stepped in to help control the Creature and to support Frankenstein in the challenges that came to light the moment the Creature attained autonomy.

What are the actual and potential applications of Machine Learning in Healthcare? Are you using Machine Learning and AI to the best of their capacities in your company? Is there anything else you should be doing? Do you have the right roadmap? During this master class you'll have the chance to learn, discuss and be inspired by Grant Allen, a Principal Data Architect in Google for 10 years, who will be visiting us from New York City.

Open source technologies are reshaping global enterprises: Incorporating open source technologies is a major contributor to digital transformation of enterprises. It is rapidly becoming the central driver of digital disruption among successful enterprises. Global companies have quickly adopted open-source frameworks to develop their apps and mobile-friendly websites. Open-source technologies have created immense opportunities for business innovation, ranging from operating systems to programming languages. In addition to transforming individual enterprises, open-source development helps reshape the entire industry.

The insurance sector is one of the most old-fashioned and resistant to change -- so artificial intelligence will have an even greater effect. The insurance sector is one of the most old-fashioned and resistant to change, and this is why AI will have a greater impact on that with respect to more receptive industries. The collection of data of new types (i.e., unstructured data such as reports, images, contracts, etc.) and the use of new algorithms are disrupting the sector in several ways. This is a really simplistic representation of the insurance business in the last fifty years, and I am aware that insurance experts might disagree with me in many different ways. There are a couple of further features to be pointed out: first of all, insurance has historically been sold not bought, which means that brokers and agents were essential to tracking new customers and to even retain old ones. In addition, it is an industry which is by definition rich of data because they collected anything they could, but is also one of the less advanced because either many of those data are unstructured or semi-structured, or the model used are quite old and simple.

Artificial intelligence (AI) on its own isn't enough to compete – companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM's senior vice-president and chief financial officer, on the company's quarterly earnings call. Cognitive computing technology (IBM's term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

Clear link found between AI adoption and business growth. Infosys has released its multinational research findings on the business impact of Artificial Intelligence, market maturity and expectations. A clear link has been shown to exist between a company's revenue growth and its AI maturity, with those reporting faster revenue growths more likely to be ahead in AI adoption. The report also found that AI is perceived as a long-term strategic priority in innovation. The majority of respondents, totalling to 85 percent, aim to train employees on the benefits and use of AI.

Scientists have created what they say is a vaccine against the kind of fake and hyper-partisan news that is spreading quickly across Facebook. The researchers claim that by showing people lies, they can teach them to better see the truth. The solution works in a similar way to a real vaccine – exposing people to a small amount of the problem to help them better respond to larger amounts of it. The solution could help social networks and news organisations battle against fake news, which has been credited with helping the vote for both Brexit and for Donald Trump. The study claims that if people are shown well-established facts about climate change and then lies about it, the latter will cancel out the former.

The field of machine learning arose somewhat independently of the field of statistics. As a result, machine learning experts tend not to emphasize probabilistic thinking. Probabilistic thinking and understanding uncertainty and variation are hallmarks of statistics. By the way, one of the best books about probabilistic thinking is Nate Silver's The Signal and The Noise: Why So Many Predictions Fail But Some Don't. In the medical field, a classic paper is David Spiegelhalter's Probabilistic Prediction in Patient Management and Clinical Trials.