Results


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.


Artificial Intelligence and Deep Learning Quotes - Supply Chain Today

#artificialintelligence

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.


Samsung secures self-driving car permit in California

Daily Mail

The South Korean electronics maker has recently been approved to test it deep-learning based autonomous vehicles on public roads in Korea. Samsung received approved to test it deep-learning based autonomous vehicles on public roads. For the small companies and students, the race course offered a large, safe testing environment. For the small companies and students, the race course offered a large, safe testing environment.


Deep Learning – what is it? Why does it matter?

#artificialintelligence

This is why in the image you can see that both models result in some errors with reds in the blue zone and blues in the red zone. The theory is that the more hidden layers you have the more you can isolate specific regions of data to classify things. GPU based processing allows for parallel execution, on large numbers of relatively cheap processors, especially when training an artificial neural network with many hidden layers and a lot of input data. That means having them able to understand images, understand speech, understand text etc.


Deep learning weekly piece: testing autonomous driving (virtually)

@machinelearnbot

Let me cut to the chase: below's a video of my fully-autonomous car driving around in a virtual testing environment. To train that software, SDCs must drive for thousands of hours and millions of miles on the road to accumulate enough information to learn how to handle both usual road situations, as well as unusual ones (such as when a woman in an electric wheelchair chases a duck with a broom in the middle of the road). To save on the incredibly expensive training (that requires thousands of hours of safety drivers plus the safety risks of having a training vehicle on public roads), SDC developers turn to virtual environments to train their cars. To train the deep learning algorithm, I'll drive a car with sensors drives around a track in simulator a few times (think: any car racing video game), and record the images that the sensors (in this case, cameras) "see" inside the simulator.


Matching Cars with Siamese Networks – Gab41

#artificialintelligence

Our second method of feature extraction was based on deep learning. For Pelops that specific task was make, model, and color identification of cars in a labeled dataset. Once we added a deep learning classifier on top of our deep learning feature extractor, we had a Siamese neural network. The Siamese network performs the feature extraction and matching in one step, and so allows optimizing both portions at the same time.


Driverless cars: Tim Cook says Apple AI is applicable to more than just cars

#artificialintelligence

The firms have established a startup support programme at Volkswagen's Data Lab to provide technical and financial support for international startups developing machine learning and deep learning applications for the automotive industry. Volvo Cars, Autoliv and Zenuity will use Nvidia's AI car computing platform as the foundation for their own advanced software development. Nvidia has partnered with automotive supplier ZF and camera perception software supplier Hella to deploy AI technology on the New Car Assessment Program (NCAP) safety certification for the mass deployment of self-driving vehicles. The firms will use Nvidia's Drive AI platform to develop software for scalable modern driver assistance systems that connect their advanced imaging and radar sensor technologies to autonomous driving functionality.


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.


Diving deeper into the realm of AI

#artificialintelligence

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.