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US Army tests AI that predicts vehicle repairs

Engadget

Keeping vehicles in good working order is about more than just getting to work on time for the US Army. A breakdown in the middle of a combat zone could prove deadly. So, to help keep on top of repairs, the army is testing artificial intelligence to predict when a vehicle might need a new part. The army is monitoring several dozen Bradley M2A3 vehicles using a machine learning algorithm from Uptake Technologies. It hopes the Asset Performance Management application will reduce unscheduled maintenance and make repairs more efficient and productive, in part by predicting when components will fail.


What Is The Difference Between Deep Learning, Machine Learning and AI?

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Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there.


Age of Machine Learning and Artificial Intelligence Citrix Blogs

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Cognitive AI systems like IBM Watson are working to understand all forms of data, interact naturally with people, and learn and reason at scale. Intel's Movidius compute stick is capturing our imaginations with low power computer vision and object recognition. Google is investing heavily in the open source TensorFlow machine learning project and a computer farm of TPUs (tensor processing units) to run TensorFlow algorithms at scale. Google also recently acquired our friends at API.ai, a natural language conversation engine with search capabilities. Machine learning focuses on the development of computer programs that can access data and use it to automatically learn and improve from experience without being explicitly programmed.. Artificial Intelligence is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.


Solution guide: Building connected vehicle apps with Cloud IoT Core

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With the Internet of Things (IoT), vehicles are evolving from self-contained commodities focused on transportation to sophisticated, Internet-connected endpoints often capable of two-way communication. The new data streams generated by modern connected vehicles drive innovative business models such as usage-based insurance, enable new in-vehicle experiences and build the foundation for advances such as autonomous driving and vehicle-to-vehicle (V2V) communication. Through all this, we here at Google Cloud are excited to help make this world a reality. We recently published a solution guide that describes how various Google Cloud Platform (GCP) services fit into the picture. Vehicles can produce upwards of 560 GB data per vehicle, per day.


What Is The Difference Between Deep Learning, Machine Learning and AI?

#artificialintelligence

Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there. Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has "learned", it can classify, with a certain probability of accuracy, passing vehicles by their make and model.


Flipboard on Flipboard

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What Is The Difference Between Deep Learning, Machine Learning and AI? Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is.


News in artificial intelligence and machine learning

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A designer's guide to AI. Leveraging user centered design principles, the author rightly states that AI will enable designers to create bespoke experiences right out of the box for each user. Importantly, these experiences need to a) create emotionally-aware relationships with the user, b) respond to needs that haven't yet been explicitly expressed, c) prevent negative emotional responses when a user is upset with an AI-caused result and d) be sensitive to sociology. A list of further reading resources is included. There's been a resurgence of neuroscience-inspired AI architectures in the past few years, with Numenta being one of the leaders. Their VP of Research, Subutai Ahmad, argues that environmental sensory inference and behavior generation are intricately tied together, and critical for learning really general purpose representations.


Navistar takes command of big data for truck design, breakdown prevention

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In 2015, Navistar was considering new lighter duty Class 8 truck designs for its International Truck brand. Specifically, it saw an opportunity in regional applications. On average, Class 8 trucks in this market have less usage -- in terms of miles and hours driven -- than long-haul applications. In concept, a truck with lighter weight components would cost less and deliver greater fuel efficiency for regional freight haulers without trade offs in durability. In 2013 or 2014, Navistar would likely have conducted an expensive market study to test the concept.


Optimization for machine learning and monster trucks

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Optimization for machine learning is essential to ensure that data mining models can learn from training data in order to generalize to future test data. Data mining models can have millions of parameters that depend on the training data and, in general, have no analytic definition. In such cases, effective models with good generalization capabilities can only be found by using optimization strategies. Optimization algorithms come in all shapes and sizes, just like anything in life. Attempting to create a single optimization algorithm for all problems would be as foolhardy as seeking to create a single motor vehicle for all drivers --there is a reason we have semi-trucks, automobiles, motorcycles, etc.


From airplane engines to street lights, transportation is becoming more intelligent - Transform

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Airlines around the world are eager to take advantage of rapidly emerging technologies to improve their passengers' experience and become more efficient. But while executives recognize the opportunities, they know they can't do it alone. The two industry leaders in aircraft engines and technology are collaborating to offer carriers their expertise and ideas in a business where cutting 1 percent of fuel usage amounts to 250,000 in annual savings per plane. A recent PricewaterhouseCoopers report estimates digital tools in aircraft maintenance could save more than 100 million a year for a large carrier with a fleet of about 500 planes. "Our TotalCare maintenance program was revolutionary in the '90s, so we're pioneers ourselves, and by collaborating with a fellow pioneer like Microsoft, we can absolutely bring innovative digital solutions to airlines now," says Alex Dulewicz, head of marketing for services at Rolls-Royce's civil aerospace division.