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AI and IoT: Taking Data Insight to Action - DZone IoT

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Recent Gartner estimations lead us to believe that up to 20 billion connected things will be in use by 2020. Data is the oil of our century -- but should we be concerned with a "data spill hazard"? Will artificial intelligence curb this threatening phenomenon, or rather, will it reveal the full potential of IoT data value? If my calculations are correct, when artificial intelligence hits the Internet of Things... you're gonna see some serious sh*t." The question is no longer whether companies should embrace big data analytics technologies.


An Intelligent Claims Process

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Artificial intelligence, Machine Learning, and Deep Learning are more than futuristic concepts. These technologies are impacting the insurance industry in a significant way right now and this impact is likely to increase in the near future. The idea of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) may fascinate consumers who enjoy talking to their digital while admiring a Nest thermostat. But for the insurance industry, these terms are business-changers that affect products and services offered and interactions with consumers and other industry partners. The definitions of these terms may be a bit confusing to the uninitiated (see sidebar).


Adopting AI in the Enterprise: Ford Motor Company

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Ford researchers developed and implemented, in mass-produced cars, an innovative misfire detection system--a neural-net-based classifier of crankshaft acceleration patterns for diagnosing engine misfire (undesirable combustion failure that has a negative impact on performance and emissions). In our supply chain, neural networks are the main drivers behind the inventory management system recommending specific vehicle configurations to dealers, and evolutionary computing algorithms (in conjunction with dynamic semantic network-based expert systems) are deployed in support of resource management in assembly plants. We can expect in the near future a wide range of novel deep-learning-based features and user experiences in our cars and trucks, innovative mobility solutions, and intelligent automation systems in our manufacturing plants. Building centers of excellence in AI and ML was not too challenging since, as I mentioned earlier, we had engineers and researchers with backgrounds and experience in conventional neural networks, fuzzy logic, expert systems, Markov decision processes, evolutionary computing, and other main areas of computational intelligence.


Adopting AI in the Enterprise: Ford Motor Company

#artificialintelligence

Ford researchers developed and implemented, in mass-produced cars, an innovative misfire detection system--a neural-net-based classifier of crankshaft acceleration patterns for diagnosing engine misfire (undesirable combustion failure that has a negative impact on performance and emissions). In our supply chain, neural networks are the main drivers behind the inventory management system recommending specific vehicle configurations to dealers, and evolutionary computing algorithms (in conjunction with dynamic semantic network-based expert systems) are deployed in support of resource management in assembly plants. We can expect in the near future a wide range of novel deep-learning-based features and user experiences in our cars and trucks, innovative mobility solutions, and intelligent automation systems in our manufacturing plants. Building centers of excellence in AI and ML was not too challenging since, as I mentioned earlier, we had engineers and researchers with backgrounds and experience in conventional neural networks, fuzzy logic, expert systems, Markov decision processes, evolutionary computing, and other main areas of computational intelligence.


Applications Of Machine Learning For Designers – Smashing Magazine

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As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. This will help with making actual design decisions and identifying the right design patterns, including situations when no directly applicable solution exists and you must transfer ideas across domains. In rare cases, machine learning might enable a computer to perform tasks that humans simply can't perform because of speed requirements or the scale of data.


IEEE Xplore: IEEE Transactions on Intelligent Transportation Systems

AITopics Original Links

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors.


The $1 Trillion Deep Learning Race for Smarter Cars - RTInsights

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To learn to drive like a human, driverless cars will require massive amounts of data, programming, and computing power. According to a new report from KPMG, deep learning and other machine learning technologies could significantly change the automotive and transportation industries. The former is a modular system, and it helps programmers know whether the guardrail detection system or the stop sign detection system is responsible for a certain error. Very few people are capable of creating deep learning systems, and they will be in high demand in the coming years.


Deep Learning for Object Detection with DIGITS

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DIGITS 4 introduces a new object detection workflow and DetectNet, a new deep neural network for object detection that enables data scientists and researchers to train models that can detect instances of faces, pedestrians, traffic signs, vehicles and other objects in images. You can use NVIDIA DIGITS to interactively perform common deep learning tasks such as managing data, defining networks architectures, training multiple models in parallel, and monitoring training performance in real time. To create your model, navigate to the DIGITS homepage, select the Models tab and click Image Object Detection as Figure 8 shows. NVIDIA DIGITS makes it easy for scientists, engineers and domain experts who are not deep learning experts to easily perform common deep learning tasks such as managing data, defining network architectures, training models in parallel, and assessing model performance.


Have You Tried Using a 'Nearest Neighbor Search'?

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While I learned a great deal over the course of the semester, there was one minor point that she made to the class which stuck with me more than I expected it to at the time: before using a really fancy or sophisticated or "in-vogue" machine learning algorithm to solve your problem, try a simple Nearest Neighbor Search first. In addition, if you don't have very many points in your initial data set, the performance of this approach is questionable (though such a case in general is enough to give most machine learning researchers pause). Neural networks require a notoriously massive amount of data; this Google Neural Network paper is capable of classifying 1,000 different types of images and was trained on over a million photos. So the next time you're faced with an unknown machine learning problem, remember to give Nearest Neighbor Search a try.