Andrew Studer was admiring a massive lava fire hose at Hawaii Volcanoes national park when he spotted something unusual: a small quadcopter drone flying very close to the natural wonder pouring hot molten rock. "There were other visitors sitting out relaxing in somewhat of a meditative state, just trying to enjoy this phenomenon," said Studer, who recently captured a viral image of a drone hovering near the lava. "I do feel like drones are extremely obnoxious, and I'm sure it was frustrating for some of the people there." In recent years, there have been growing concerns about technology invading national parks, with drones and other noisy gadgets disrupting wilderness areas, wildlife habitats and other recreational areas. While the boom in drones has increasingly spoiled the natural sound that the National Park Service (NPS) is charged with protecting, there has also been a rising number of reports of social media use leading hikers to snap inappropriate and dangerous selfies, threatening wildlife and the environment in the process.

SYS-CON Events announced today that Juniper Networks (NYSE: JNPR), an industry leader in automated, scalable and secure networks, will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Juniper Networks challenges the status quo with products, solutions and services that transform the economics of networking. The company co-innovates with customers and partners to deliver automated, scalable and secure networks with agility, performance and value. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it's not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

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And just last week, the data science competition site Kaggle announced the winners of a $1 million contest in which more than 10,000 researchers competed to build machine learning models that could detect lung cancer from CT scans. This is an old idea, dating back to the 1950s, but now that operations like Google and Facebook have access to such enormous amounts of data and computing power, neural networks can achieve far more than they could in the past. Through the Kaggle contest, run in tandem with the tech-minded consultancy Booz Allen, thousands of data scientists competed to build the most accurate neural networks for the task. Before a neural network can start learning the task from a collection of images, trained doctors must label them--that is, use their human intelligence and knowledge to identify the images that show signs of lung cancer.

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to the IDG News Service about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. IDGNS: Is it true that you and Deep Blue joined IBM at the same time?

Hello, I am working on a highly imbalanced dataset (negative examples over 20K and positive examples about 100). I am trying to build a logistic regression model. My current approach includes undersampling of negative examples. However with this approach there are a couple of problems: 1) Several LR models are possible with different samples. How to generalize these models and interpret the output?

Automation and artificial intelligence are poised to free up millions of hours of manpower and save billions of dollars across all levels of government, according to a recent report from Deloitte University Press. The 28-page report, titled AI-augmented Government, examines several case studies, provides a taxonomy of AI systems, and concludes that in the federal government alone, automation with "high investment" could free up as many as 1.2 billion hours of work and save up to $41.1 billion annually. Through the use of rules-based systems, machine translation, computer vision, machine learning, robotics and natural language processing, the report notes the unusual but "tantalizing" paradigm presented by AI in which speed is increased, quality is improved, and cost is reduced -- all in parallel. Researchers said they identified a potential 30 percent savings in government worker time that could happen within five to seven years of implementing an AI solution. The report concludes that AI will fundamentally change how every level of government works, and will do so much sooner than most people believe.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

The Apple Watch could be used to detect a heart condition that causes over 100,000 strokes every year, according to a new study. Heart health app Cardiogram and researchers from the University of California, San Francisco (UCSF) Cardiology Health eHeart project teamed up to take a closer look at just how effective the Watch can be at tracking the most clinically common heart abnormality, atrial fibrillation (AF). The irregularity, which is treatable but tough to diagnose using current medical standard practices, is the leading cause of heart failure. The mRhythm project that resulted from the pairing looked at the Apple Watch-sourced heart rate readings from 6,158 Cardiogram users. The data was then used to build an algorithm to detect the distinct heart rate variability pattern caused by AF.