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How Mercedes Is Preparing For The 4th Industrial Revolution: Big Data, Machine Learning And Drones

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In an era of great uncertainty and disruption for automotive manufacturers, Mercedes and its parent company Daimler are jumping in full throttle as leaders of the 4th Industrial Revolution. Not only are they designing new vehicles, but their services, influence in the transportation industry and factories are transforming to embrace the new opportunities and demands of their customers. Other companies should follow their lead to thrive in the new industrial revolution. What is the 4th Industrial Revolution? Often referred to as industry 4.0, the 4th Industrial Revolution is the shift to smart factories that use a combination of cyber-physical systems, the Internet of Things and the Internet of Systems to connect the entire production chain and make decisions on its own.


The race to own the autonomous super highway: Digging deeper into Broadcom's offer to buy Qualcomm

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Governor Andrew Cuomo of the State of New York declared last month that New York City will join 13 other states in testing self-driving cars: "Autonomous vehicles have the potential to save time and save lives, and we are proud to be working with GM and Cruise on the future of this exciting new technology." For General Motors, this represents a major milestone in the development of its Cruise software, since the the knowledge gained on Manhattan's busy streets will be invaluable in accelerating its deep learning technology. In the spirit of one-upmanship, Waymo went one step further by declaring this week that it will be the first car company in the world to ferry passengers completely autonomously (without human engineers safeguarding the wheel). As unmanned systems are speeding ahead toward consumer adoption, one challenge that Cruise, Waymo and others may counter within the busy canyons of urban centers is the loss of Global Positioning System (GPS) satellite data. Robots require a complex suite of coordinating data systems that bounce between orbiting satellites to provide positioning and communication links to accurately navigate our world.


Machine Learning at HPC User Forum: Drilling into Specific Use Cases

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Dr. Weng-Keen Wong from the NSF echoed much the same distinction between the specific and general case algorithm during his talk "Research in Deep Learning: A Perspective From NSF" and was also mentioned by Nvidia's Dale Southard during the disruptive technology panel. Tim Barr's (Cray) "Perspectives on HPC-Enabled AI" showed how Cray's HPC technologies can be leveraged for Machine and Deep Learning for vision, speech and language. Fresh off their integration of SGI technology into their technology stack, the talk not only highlighted the newer software platforms which the learning systems leverage, but demonstrated that HPE's portfolio of systems and experience in both HPC and hyper scale environments is impressive indeed. Stand-alone image recognition is really cool, but as expounded upon above, the true benefit from deep learning is having an integrated workflow where data sources are ingested by a general purpose deep learning platform with outcomes that benefit business, industry and academia.


Artificial Intelligence and Deep Learning Quotes - Supply Chain Today

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We are in the crawling stages of Artificial Intelligence and Deep Learning. So everyone is aware, Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of Artificial Intelligence. Companies like Tesla, Uber, and Google are using Deep Learning to make self driving vehicles a reality. We hope you like the Artificial Intelligence and Deep Learning quotes.


Robots with better eyesight and intelligent drones

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Researchers in the west of Scotland have developed an artificial intelligence system that can automatically recognise different types of cars - and people. Thales' head of algorithms and processing Andrew Parmley explains what is going on. "The image itself is actually quite small, so the deep learning neural network is identifying what it sees." The concept underlying this technology is deep learning: a computer's neural networks learning on the job.


No reason to fear the robot revolution - TechCentral

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Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it's a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude.


Machine learning: Should we be excited or fearful for our jobs?

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Nicola Mortimer, head of business products, marketing and operations at Three Ireland, on how machine learning can drive efficiency rather than drive people out of their jobs. Machine learning is predicted to be an integral part of more than 300m new smartphones sold this year. So, should we be excited or fearful for our jobs? It has been predicted that machine learning capabilities will be present in more than 20pc of smartphones sold globally in 2017. With few devices more ubiquitous in the developed world than the smartphone, machines that learn will now be at the fingertips of a large percentage of the population.


How is predictive data shaping the auto industry

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How is predictive data changing the automotive industry and what changes can we expect to see in the future? Connected and autonomous cars are going to benefit most from the inclusion of predictive data because their design centers on data collection and processing. As more and more connected cars hit the roads, data management is going to become an essential tool. Predictive data has already shown potential for preventative maintenance, but this same application could be used to predict software problems and security flaws as well.


Deep Learning Accelerates Self-Driving Truck Revolution Trucks.com

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Written by Tom Mayor, national strategy leader for consulting firm KPMG's Industrial Manufacturing practice, and Todd Dubner, a principal in KPMG's Strategy practice. In conjunction with an expanding footprint of regional distribution centers and a growing fleet of Prime-Air freighters, Amazon promises to change the parcel delivery game by lowering delivery costs while simultaneously enabling same-day delivery in major metro markets. Early pilots by Daimler, Uber's Otto and others have demonstrated the feasibility of fully autonomous, on-highway operation and offer the potential to safely open four to six productive, on-road travel hours a day during which today's two-driver rigs are parked for crew rest – often while idling and burning fuel to maintain cabin air conditioning or heat. Editor's note: Tom Mayor is the national strategy leader for KPMG's Industrial Manufacturing practice.


AI in self-driving cars - NVIDIA and Bosch collaboration

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Intelligent machines powered by artificial intelligence (AI) computers that can learn, reason and interact with people and the surrounding world are no longer science fiction. Thanks to a new computing model called deep learning using powerful graphics processing units (GPUs), AI is transforming industries from consumer cloud services to healthcare to factories and cities. Many of these are in place already, providing new services to millions around the world. However, no industry is poised for such a significant change as the $10 trillion transportation industry. The automotive market is next, and the opportunity to develop advanced self-driving vehicle holds the promise to the world of dramatically safer driving and new mobility services.