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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.


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.


Diving deeper into the realm of AI

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Then, as high-bandwidth networking, cloud computing, and high-powered graphics-enabled microprocessors emerged, researchers began building multilayered neural networks--still extremely slow and limited compared to the human brain, but useful in practical ways. The best-known AI milestones--in which software systems beat expert human players in Jeopardy!, chess, Go, poker, and soccer--differ from most day-to-day business applications. A deep learning system is a multilayered neural network that learns representations of the world and stores them as a nested hierarchy of concepts many layers deep. Although it is the most similar duplication of the human brain scientists have developed, a deep learning neural network cannot be leveraged to solve all problems.


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.


The subtle, invisible AI that big Indian start-ups are using to get to know consumers better

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So, ShopClues plans to use advanced technologies to make it easier for shoppers to find the right size when buying clothes online, according to Utkarsh Biradar, vice-president of product at the company. It's also applying these technologies to help advertisers expand their reach effectively, using machine learning to identify "lookalike" targets that are similar to existing users as well as figuring out what kinds of ads users don't want to see. Ola, one of India's leading ride-hailing apps, is using data science and machine learning to track traffic, improve customer experience, understand driver habits and extend the life of a vehicle. Machine learning models log each customer's gender, brand affinity, store affinity, price preference, frequency, volume of purchases, and more, which become more accurate as the company collects more data.


The subtle, invisible AI that Indian unicorns have made a part of consumers' lives

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So, ShopClues plans to use advanced technologies to make it easier for shoppers to find the right size when buying clothes online, according to Utkarsh Biradar, vice-president of product at the company. It's also applying these technologies to help advertisers expand their reach effectively, using machine learning to identify "lookalike" targets that are similar to existing users as well as figuring out what kinds of ads users don't want to see. Ola, one of India's leading ride-hailing apps, is using data science and machine learning to track traffic, improve customer experience, understand driver habits and extend the life of a vehicle. Machine learning models log each customer's gender, brand affinity, store affinity, price preference, frequency, volume of purchases, and more, which become more accurate as the company collects more data.


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.


Where the Cloud Won't Work: Machine Learning for the Industrial Internet of Things - The New Stack

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Reed says IOx with FogDirector is a rapidly evolving platform, already capable of doing orchestration of all edge devices and acting as the DevOps layer for edge processing gateways. Most are just setting up their infra at present and working to get data collection flowing alongside complex event processing that can track which data to store, which to act on immediately, and which to discard. Tarik Hammadou, CEO and co-founder at VIMOC Technologies has built both hardware (VIMOC's neuBox that has both sensors and a compute layer included), and a hardware-agnostic software platform that operates at the cloud level where applications can be built and connected via API to sensors and gateways. VIMOC's sensors and platform have been taken up by parking garages to optimize parking spaces and already Hammadou has introduced deep learning algorithms on the gateway to better understand the sensor readings being collected.


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.