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Drones and Robots Are Taking Over Industrial Inspection

MIT Technology Review

The effort shows how low-cost drones and robotic systems--combined with rapid advances in machine learning--are making it possible to automate whole sectors of low-skill work. Avitas uses drones, wheeled robots, and autonomous underwater vehicles to collect images required for inspection from oil refineries, gas pipelines, coolant towers, and other equipment. Nvidia's system employs deep learning, an approach that involves training a very large simulated neural network to recognize patterns in data, and which has proven especially good for image processing. It is possible, for example, to train a deep neural network to automatically identify faults in a power line by feeding in thousands of previous examples.


Visteon's HABIT is a concept infotainment system that puts road trip copilots out of a job (video)

AITopics Original Links

A good acronym also hints at what it does, and Visteon's new intelligent in-car concept, HABIT, is a good example of that. The Human Bayesian Intelligence Technology system -- to give it its full name -- learns the behaviour of drivers so it can automatically change the temperature, heat the seats and drop that Biohazard album just when you need it most. Factors such as weather, time of day and real-time road conditions all play a part, plus, of course a log of all your typical in-car interactions. It promises to go above just warming your behind on a cold morning though, offering intelligence that would be able to divine local radio stations that play your kind of jam when you're out of town. It could also seamlessly mix these with your local / tablet / smartphone library and internet sources.


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. 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.


Global Bigdata Conference

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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. But what exactly is it?


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.


End-to-End Deep Learning for Self-Driving Cars

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The system is trained to automatically learn the internal representations of necessary processing steps, such as detecting useful road features, with only the human steering angle as the training signal. We train the weights of our network to minimize the mean-squared error between the steering command output by the network, and either the command of the human driver or the adjusted steering command for off-center and rotated images (see "Augmentation", later). Figure 5 shows the network architecture, which consists of 9 layers, including a normalization layer, 5 convolutional layers, and 3 fully connected layers. We follow the five convolutional layers with three fully connected layers, leading to a final output control value which is the inverse-turning-radius.


One Button to Help Them All

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How do you satisfy the "one button" trick to help solve the menu and button configuration dilemma on the vehicle dashboard? One reason I chose to move into the automotive group at SAS is their proven approaches and experience with applying machine learning techniques to help these situations. Driverless vehicles, connected cars, e-hailing, car sharing, and other innovative offerings are reshaping our industry. In the case of getting the dashboard to work intuitively, conveniently and effectively with the driver, machine learning techniques are a wise choice.


IoT and Machine Learning are invading our lives. Is it a good thing?

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The Internet of Things (IoT) and Machine Learning are two of the hottest technologies of our time. At first glance, I really like some of the ideas that are being proposed by the combination of Internet of Things and Machine Learning: smart light bulbs that know when to turn themselves on and off; smart kettles that will make sure you'll have freshly made coffee at the exact time you want it without having thought about it; smart fridges that will do the grocery for you; smart locks that will recognize you and unlock with the tap of a phone. I don't have to worry about leaving the door unlocked because my smart lock will automatically lock the door when it senses that the house is empty. Combined with the power of the fast evolving VR technology, IoT will enable us to travel to distant locations, feel things, meet people and do a lot more without ever setting foot outside our homes.