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"Scientists are still suspicious of AI" - Globes English

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In March 2016, Google's Alphago artificial intelligence (AI) program stunned the world by beating the human world champion Go player in front of 200 million spectators. This was living proof of the potential in AI technology and the level of maturity reached by neural network and deep learning technologies. Those astounded by the success included quite a few engineers and managers who have been leading the AI revolution in the world in recent years. One of these was Intel VP Naveen Rao, general manager of the company's Artificial Intelligence Products Group, which was founded last year. "When I studied at college in the 1990s, we regarded artificial intelligence as'creative work'," Rao relates.


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.


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

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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.


Artificial Intelligence vs. Machine Learning: What's the Difference

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So when a machine takes decisions like an experienced human being in similarly tough situations are taken by a machine it is called artificial intelligence. You can say that machine learning is a part of artificial intelligence because it works on similar patterns of artificial intelligence. Finally in the 21st century after successful application of machine learning artificial intelligence came back in the boom. As machine learning is giving results by analyzing large data, we can assure that it is correct and useful and time required is very less.


Robots with better eyesight and intelligent drones

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Researchers in the west of Scotland have developed an artificial intelligence system that can automatically recognise different types of cars - and people. Thales' head of algorithms and processing Andrew Parmley explains what is going on. "The image itself is actually quite small, so the deep learning neural network is identifying what it sees." The concept underlying this technology is deep learning: a computer's neural networks learning on the job.


The Reality of the Artificial Intelligence Revolution - DZone AI

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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.


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.


Where the Cloud Won't Work: Machine Learning for the Industrial Internet of Things - The New Stack

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Reed says IOx with FogDirector is a rapidly evolving platform, already capable of doing orchestration of all edge devices and acting as the DevOps layer for edge processing gateways. Most are just setting up their infra at present and working to get data collection flowing alongside complex event processing that can track which data to store, which to act on immediately, and which to discard. Tarik Hammadou, CEO and co-founder at VIMOC Technologies has built both hardware (VIMOC's neuBox that has both sensors and a compute layer included), and a hardware-agnostic software platform that operates at the cloud level where applications can be built and connected via API to sensors and gateways. VIMOC's sensors and platform have been taken up by parking garages to optimize parking spaces and already Hammadou has introduced deep learning algorithms on the gateway to better understand the sensor readings being collected.


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.


Tech Talk: Qualcomm's Zeroth Could Divorce The Cloud Androidheadlines.com

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Qualcomm has been talking about their deep learning machine intelligence platform, Zeroth, for a number of months now. When the company first started talking about including the technology into System-on-Chips, this might have meant that the Snapdragon 820 was going to include a Qualcomm Neural Processing Unit, or NPU, as part of the hardware. Instead, Qualcomm included the Zeroth software on the device rather than a core. Over the weekend, Qualcomm has announced that they are releasing the Zeroth SDK so that developers can start to utilize this specialist software on the device rather than relying on cloud computing to process this data. Neural processing has been used for a number of years now.