Every year at CES, there is a slew of announcements from automakers about their latest connected car or their next futuristic concept. However, one thing missing was the chipmakers that make these things possible. Last year gave us a glimpse of what these companies were doing, but this year, and all announced major automotive design wins, partnerships and products. Each company has a different approach to automotive wtih a different focus on the self-driving, connected car of the future and how they plan to enable it. At CES 2017, Intel's Automated Driving Group launched its first automotive-branded Intel GO solutions.

The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

Tiny drones could scout high-rise buildings and underground tunnels for possible threats to US troops in cities of the future. But instead of spending years cooking up the necessary drone technologies in military research labs, the Pentagon might be better off shopping for the latest civilian drones coming soon to stores. US military leaders have discussed the need for a new generation of scout drones for some time. After all, kicking down doors is a dirty and dangerous business for US troops trying to clear enemy-held buildings. It would be far safer to deploy diminutive drone buddies to provide an initial peek inside, and identify any potential threats.

With another tsunami of hype about the wonders artificial intelligence can bring, IT professionals would be wise to seek higher ground until they can sort out exactly how machine learning business applications can help them. As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

Python is a popular language for machine learning. It is even the most popular one according to a study of mine recently published here and on KDnuggets. The above study generated quite a few reactions on social media. I just recently switched to Scala. Somewhat similar to python but with a number of advanced concepts.

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.