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Navy Block V submarine deal brings new attack ops and strategies

FOX News

The Virginia-class, nuclear-powered, fast-attack submarine, USS North Dakota (SSN 784), transits the Thames River as it pulls into its homeport on Naval Submarine Base New London in Groton, Conn - file photo. Bringing massive amounts of firepower closer to enemy targets, conducting clandestine "intel" missions in high threat waters and launching undersea attack and surveillance drones are all anticipated missions for the Navy's emerging Block V Virginia-class attack submarines. The boats, nine of which are now surging ahead through a new developmental deal between the Navy and General Dynamics Electric Boat, are reshaping submarine attack strategies and concepts of operations -- as rivals make gains challenging U.S. undersea dominance. Eight of the new 22-billion Block V deal are being engineered with a new 80-foot weapons sections in the boat, enabling the submarine to increase its attack missile capacity from 12 to 40 on-board Tomahawks. "Block V Virginias and Virginia Payload Module are a generational leap in submarine capability for the Navy," Program Executive Officer for Submarines Rear Adm. David Goggins, said in a Navy report.


Why Machine Learning at the Edge? - Predictive Analytics Times - machine learning & data science news

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Originally published in SAP Blogs, October 16, 2019. For today's leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020. Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively.


120 AI Predictions For 2020

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Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...


Machine learning research may aid industry

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What do these topics have in common? The answer can be found in machine learning research at Binghamton University. Dana Bani-Hani, a doctoral student studying industrial and systems engineering, has spent the past few years teaching machines how to read data sets in any industry. The system she coded, called a Recursive General Regression Neural Network Oracle (R-GRNN Oracle), takes data inputs and creates prediction outputs. Classification models are not new in data science and analytics, but what Bani-Hani created goes beyond the basics.


The Most In Demand Tech Skills for Data Scientists

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In my original 2018 article I looked at demand for general skills such as statistics and communication. I also looked at demand for technologies such as Python and R. Software technologies change must faster than demand for general skills, so I include only technologies in this updated analysis. I searched SimplyHired, Indeed, Monster, and LinkedIn to see which keywords appeared with "Data Scientist" in job listings in the United States. This time I decided to write the code to scrape the job listings instead of searching by hand. This endeavor proved fruitful for SimplyHired, Indeed, and Monster.


New robotic contact lenses can be powered wirelessly without raising the temperature

Daily Mail - Science & tech

Researchers at the Yonsei University of Seoul have developed a new type of robotic contact lens that can be recharged wirelessly and which could bring a wide variety of futuristic uses for contact lenses one step closer to reality. The new devices are built around a circular translucent antenna and super capacitor system that can receive continual power without needing to be plugged in to an external power source. These experimental new contact lenses will also be able to draw electricity without raising the temperature of the lens, eliminating a potential long-term cause of harm to wearers and the device itself. According to a report from Yonhap News Agency, because the lenses are completely self-enclosed they can be maintained with standard contact solutions without any risk of degradation. The team used soft contact lens material instead of rigid material to ensure the tools could be used in as wide a variety of circumstances as possible.


Study reveals we tend to twist facts and statistics on controversial issues to fit our own beliefs

Daily Mail - Science & tech

From news outlets to social media sites, there are numerous places that spread fake news, but a study has uncovered a new source – you. Researchers found that people will misremember numerical statistics on a controversial topic in a way that fits their own commonly held beliefs. For example, when people were shown that the number of Mexican immigrants in the United States declined recently during the study--which is true but goes against many people's beliefs--they tended to remember the opposite. And the team also found that as people pass along this misinformation, the numbers can become further and further from the truth. The study was conducted by a team at Ohio State University, who carried out two studies to investigate how people perceive and spread fake news.


How StreetLight Data uses machine learning to plug cities into the mobility revolution

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The mobility revolution may have the potential to transform cities, but in the short term the rise in ride-hailing apps, bike sharing, and electric scooters is giving many local officials fits. A healthy dose of data and machine learning may help get this movement back on track. That's the bet that San Francisco-based StreetLight Data is making. The company is helping cities harness the explosion of data being generated by everything from smart city sensors to mobile phones to new transportation modes, in a bid to reinvent urban planning. As cities groan under rising populations and pollution, making more effective use of data could be the key to making them habitable over the long run.


Philips extends AI portfolio with launch of IntelliSpace AI Workflow Suite to seamlessly integrate applications across imaging workflows

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Philips announces the launch of IntelliSpace AI Workflow Suite to enable healthcare providers to seamlessly integrate AI applications into the imaging workflow. Part of Philips' new enterprise imaging informatics solution, the comprehensive AI workflow platform provides a full suite of applications for integration and centralized workflow management of AI algorithms, delivering structured results wherever they're needed across the healthcare enterprise. Partners at launch include Aidoc, MaxQ AI, Quibim, Riverain Technologies and Zebra Medical. IntelliSpace AI Workflow Suite was unveiled at the 2019 Radiological Society of North America Annual Meeting (RSNA). Leiden University Medical Center (LUMC) in the Netherlands recently signed an agreement to be the first healthcare provider to install the platform.


Kartik Talamadupula - IBM

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Kartik Talamadupula is a research staff member at IBM's T.J. Watson Research Center in Yorktown Heights, New York in the AI Science - Reasoning group in IBM Research AI. His research interests lie in the field of Automated Planning, within the wider umbrella of Artificial Intelligence (AI), and in examining the issues inherent in using planning and reasoning technologies as mediators in human-machine teams. He also has research interests in reinforcement learning, conversation and dialog systems, crowdsourcing/human computation, AI for IoT, and information retrieval on social media (specifically Twitter). He received his Ph.D. in Computer Science in Fall 2014 from Arizona State University, where he worked on extending the frontiers of AI planning methods and technologies. His research focused on understanding, analyzing, and extending the role that automated planners can play as part of integrated AI systems that interact directly and cooperatively with humans.