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Ericsson developing autonomous vehicle software cloud platform

ZDNet

Zenuity, a joint venture between Volvo and automotive safety system company Autoliv, will use Ericsson's mobile connectivity management Internet of Things (IoT) solution, IoT Accelerator, with its own connected cloud solution for data management and security. According to Ericsson, the Zenuity Connected Cloud will integrate vehicle software and systems with connected safety data gathered from surrounding infrastructure and vehicles. Ericsson launched its Connected Vehicle Marketplace back in February, with the cloud-based service aimed at allowing equipment manufacturers to share data and applications with third parties. Ericsson's head of Media Processing and Delivery for its Media Solutions business Arpad Jordan said MediaFirst Content Processing is "the industry's first software-based, multi-application media processing platform" aimed at the contribution market.


How big data is transforming the automotive industry

#artificialintelligence

By 2020, the connected car market report states that connected car services will account for approximately $40 billion annually. These services include infotainment, navigation, fleet management, remote diagnostics, automatic collision notification, enhanced safety, usage based insurance, traffic management and, lastly, autonomous driving. The root of these applications is big data, as increasing amounts of data are collected from remote sensors; this information is being interpreted and leveraged to transform the automotive industry into one of automation and self-sufficiency. By using the information gleaned from smart sensors, the industry can benefit from compiling custom insurance plans, monitoring driver behaviour, performance and safety.


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.


R&D Special Focus: Robotics/A.I.

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We expanded on that idea in Creating Robots That Are More Like Humans, which features a research group at Northeastern University focused on creating software that makes robots more autonomous, so eventually they are able to perform tasks on their own with little human supervision or intervention. Our article, Creator of'Suicidal Robot' Explains How Robot Security Could Prevent'The Next Sandy Hook', focused on the robotic security company Knightscope, which made headline recently for a humorous mishap involving one of its robots falling into a fountain. We wrapped up our robotics coverage with, Robotic Teachers Can Adjust Style Based on Student Success, which focuses on the development of socially assistive robotics-- a new field of robotics that focuses on assisting users through social rather than physical interaction. In our article Algorithm Improves Energy Efficiency of Autonomous Underwater Vehicles, we focused on researchers from Oregon State University, who developed a new algorithm to direct autonomous underwater vehicles to ride the ocean currents when traveling from point to point.


The Reality of the Artificial Intelligence Revolution - Talend

#artificialintelligence

The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. There have been obvious failings of this technology (the unfiltered Microsoft chatbot, "Tay," as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered. Learning from repetition, improving patterns, and developing new processes is well within reach of current AI models, and will strengthen in the coming years as advances in Artificial Intelligence – specifically machine learning and neural networking – continue.


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.


The Reality of the Artificial Intelligence Revolution - DZone AI

#artificialintelligence

The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. There have been obvious failings of this technology (the unfiltered Microsoft chatbot "Tay" as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered. Learning from repetition, improving patterns, and developing new processes is well within reach of current AI models, and will strengthen in the coming years as advances in artificial intelligence -- specifically machine learning and neural networks -- continue.


Solution guide: Building connected vehicle apps with Cloud IoT Core

#artificialintelligence

GCP services, including the recently launched Cloud IoT Core provides a robust computing platform that takes advantage of Google's end-to-end security model. Device Management: To handle secure device management and communications, Cloud IoT Core makes it easy for you to securely connect your globally distributed devices to GCP and centrally manage them. Applications: Compute Engine, Container Engine and App Engine all provide computing components for a connected vehicle platform. Predictive Models: TensorFlow and Cloud Machine Learning Engine provide a sophisticated modeling framework and scalable execution environment.


CARNAC program researching autonomous co-piloting

Robohub

DARPA, the Defense Advanced Research Projects Agency, is researching autonomous co-piloting so they can fly without a human pilot on board. RE2, the CMU spin-off located in Pittsburgh, makes mobile manipulators for defense and space. "Our team is excited to incorporate the Company's robotic manipulation expertise with proven technologies in applique systems, vision processing algorithms, and decision making to create a customized application that will allow a wide variety of existing aircraft to be outfitted with a robotic pilot," stated Jorgen Pedersen, president and CEO of RE2 Robotics. This application will open up a whole new market for our mobile robotic manipulator systems."