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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.


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.


How deep learning will transform the future of the auto industry ZDNet

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The biggest threat to artificial intelligence: Human stupidity Snowden says Petraeus shared'far more highly classified material than I ever did' Snowden says Petraeus shared'far more highly classified material than I ever did' Deep learning is an important enabler of building self-driving vehicles that can operate without human intervention. We're at the start of what Silberg calls a new era in automotive product development and manufacturing -- one that emphasizes a vehicle's nervous system including a computer "brain," sensors, controls, driver interaction, and data storage even more than the powertrain. Because of deep learning, autonomy and mobility, car ownership is moving from individually-owned vehicles to shared driving experiences, with a growing consumer focus on mobility and transportation on demand. Automotive product development and manufacturing will emphasize vehicles' smarts: the computers, sensors, controls, driver interfaces, and data storage components.


Exploring the Artificially Intelligent Future of Finance

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Exploring the Artificially Intelligent Future of Finance With technological enhancements increasing computing power and decreasing its cost, easing access to big data and innovating algorithms, there has been a huge surge in interest of artificial intelligence, machine learning and its subset, deep learning, in recent years. What have been the leading factors enabling recent advancements and uptake of deep learning? Yuanyuan: Customer experience could be significantly improved using AI by analyzing individual level attributes to make traditional service much more tailor-made. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it.


Nexar Joins Berkeley DeepDrive Consortium to Shape the Future Of Driving

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Alongside fellow industry leaders participating in the BDD Consortium, the Nexar team will apply its rapidly expanding data network and industry know-how to infuse state-of-the-art deep learning techniques for the optimal and safest driving experience." Backed by the BDD Consortium's advanced research, Nexar can now better analyze and understand this data in order to ultimately gain the best perception of driving and road conditions. With this information, Nexar further develops its vehicle-to-vehicle network, essential for the future of autonomous and human-driven cars. The BDD Industry Consortium investigates state-of-the-art technologies in computer vision and machine learning for automotive applications.


Exploring the Artificially Intelligent Future of Finance

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In the long run AI, will completely change our investment industry, but (certainly on the institutional investment side) we are only at the beginning of a long and slow transition of 50 years. Financial advisory is another under developing area, where in future, individuals could expect a machine to suggest best investment portfolios based on their own family balance and consumption behaviors. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it. To continue on the above examples, advances in NLP assure that bots will be able to handle simple tasks in customer service and AI systems will on the other hand provide automated information from news, press releases and other textual documents to prices.