Results


The push to process vehicle sensor data

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Continued from: "Advanced image sensors take automotive vision beyond 20/20." And there are many others now in the race to process all of that vehicle sensor data. Among them, Toshiba has been evolving its Visconti line of image recognition processors in parallel with increasingly demanding European New Car Assessment Programme (Euro NCAP) requirements. Starting in 2014, the Euro NCAP began rating vehicles based on active safety technologies such as lane departure warning (LDW), lane keep assist (LKA), and autonomous emergency braking (AEB). These requirements extended to daytime pedestrian AEB and speed assist systems (SAS) in 2016.


When Artificial Intelligence Becomes Commonplace

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AI and computer learning is quickly gaining use, so what happens when AI becomes commonplace? Before we dive into this it is important to understand what AI can and can't do today and what aspect of it is already common. Computer learning is a subset of AI, but the two are often discussed together or interchanged. Computer learning is a method where a computer is trained on a set of data and then uses that training to learn a task. Facial feature recognition is a common computer learning task where the computer is trained to recognize the various features (eyes, lips, nose and mouth) of anyone's face.


How AI is paving the way for fully autonomous cars

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In order to decipher these complex situations, autonomous vehicle developers are turning to artificial neural networks. In place of traditional programming, the network is given a set of inputs and a target output (in this case, the inputs being image data and the output being a particular class of object). The process of training a neural network for semantic segmentation involves feeding it numerous sets of training data with labels to identify key elements, such as cars or pedestrians. Machine learning is already employed for semantic segmentation in driver assistance systems, such as autonomous emergency braking, though.


The robot revolution

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Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude. It should be obvious then that driverless cars will require an immense amount of data gathering and analysis; they will also need to connect to cloud-based traffic and navigation services, and will draw on leading technologies in sensors, displays, on-board and off-board computing, in-vehicle operating systems, wireless and in-vehicle data communication, analytics, speech recognition and content management. The IoT and machine learning look set to fundamentally alter the way our world works – in a manner that is exactly the opposite of a killer robot from the future.


No reason to fear the robot revolution - TechCentral

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Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it's a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude.


News in AI and machine learning

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I'm Nathan Benaich -- welcome to issue #18 of my AI newsletter! I will synthesise a narrative that analyses and links important happenings, data, research and startup activity from the AI world. Grab your hot beverage of choice and enjoy the read! If you're looking to invest, research, build, or buy AI-driven companies, do hit reply and drop me a line. In a massive deal this quarter, Intel CEO agreed to purchase Mobileye for $15.3bn.


Vehicle Artificial Perception-Building Experimental Systems

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The Sensor Acquisition Module, for example, is responsible for communicating and receiving data from sensors, while the Main Module will make predictions and make decisions to send to the engine's control module. This experimental system includes 4 main modules: The Sensor Acquisition Module, the Vision Module, the Occupant-System Communication Module and the Artificial Perception Operation Module. Data from the Sensor Acquisition Module and Vision Module is then transmitted through a communication network to an Artificial Perception Operation Module, which is a powerful computer with an intelligent software capable of predicting the behavior of surrounding objects. Also using object detection based on reflection technique; however, radar sensors use electromagnetic waves to scan objects.


How NASA's Raven Module Is Developing Machine Learning Algorithms for Autonomous Spacecraft Docking

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How do satellites get the maintenance they need to keep functioning? This is something of a trick question because many of them simply don't. Many satellites were put into orbit with the understanding that they wouldn't be retrieved, possibly ever. When they run out of fuel, they effectively become space junk. Meanwhile, companies and governments pay exorbitant prices to build a similar spacecraft and put them in orbit.


The most detailed maps of the world will be for cars, not humans

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Here started work building HD maps back in 2013, according to Sanjay Sood, the company's VP for highly automated driving. "Starting last year, we're essentially building the road network in order to have this map available for the first fleets of cars that are going to be leveraging this technology that are going to be showing up on the roads around 2020," said Sood. But a more scalable solution involves leveraging the embedded sensors in cars already using HD maps to navigate. "Here's adoption of our deep learning technology for their cloud-to-car mapping system will accelerate automakers' ability to deploy self-driving vehicles."


How fog computing pushes IoT intelligence to the edge

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Meeting these requirements is somewhat problematic through the current centralized, cloud-based model powering IoT systems, but can be made possible through fog computing, a decentralized architectural pattern that brings computing resources and application services closer to the edge, the most logical and efficient spot in the continuum between the data source and the cloud. Fog computing reduces the amount of data that is transferred to the cloud for processing and analysis, while also improving security, a major concern in the IoT industry. IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. An example is Cisco's recent acquisition of IoT analytics company ParStream and IoT platform provider Jasper, which will enable the network giant to embed better computing capabilities into its networking gear and grab a bigger share of the enterprise IoT market, where fog computing is most crucial.