processing


Use deep learning on data you already have

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Clean, labeled data requires data analysts with a combination of domain knowledge, and infrastructure engineers who can design and maintain robust data processing platforms. Looking toward narrow AI systems, much of the recent excitement involves systems that combine deep learning with additional techniques (reinforcement learning, probabilistic computing) and components (memory, knowledge, reasoning, and planning). Many current systems based on deep learning require big compute, big data, and big models. While researchers are seeking to build tools that are less dependent on large-scale pattern recognition, companies wanting to use deep learning as a machine learning technique can get started using tools that integrate with their existing big data platforms.


Inside Microsoft's Plan to Bring AI to its HoloLens Goggles

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Microsoft Corp. says it has the answer with a chip design for its HoloLens goggles--an extra AI processor that analyzes what the user sees and hears right there on the device rather than wasting precious microseconds sending the data back to the cloud. "For an autonomous car, you can't afford the time to send it back to the cloud to make the decisions to avoid the crash, to avoid hitting a person. But the rapid development of artificial intelligence has left some traditional chip makers facing real competition for the first time in over a decade. More recently, in an effort to take on Google and Amazon.com Inc. in cloud services, the company used customizable chips known as field programmable gate arrays to unleash its AI prowess on real-world challenges.


Quest for AI Leadership Pushes Microsoft Further Into Chip Development

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Microsoft Corp. says it has the answer with a chip design for its HoloLens goggles--an extra AI processor that analyzes what the user sees and hears right there on the device rather than wasting precious microseconds sending the data back to the cloud. "For an autonomous car, you can't afford the time to send it back to the cloud to make the decisions to avoid the crash, to avoid hitting a person. But the rapid development of artificial intelligence has left some traditional chip makers facing real competition for the first time in over a decade. More recently, in an effort to take on Google and Amazon.com Inc. in cloud services, the company used customizable chips known as field programmable gate arrays to unleash its AI prowess on real-world challenges.


Qualcomm opens its mobile chip deep learning framework to all

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Mobile chip maker Qualcomm wants to enable deep learning-based software development on all kinds of devices, which is why it created the Neural Processing Engine (NPE) for its Snapdragon-series mobile processors. The NPE software development kit is now available to all via the Qualcomm Developer Network, which marks the first public release of the SDK, and opens up a lot of potential for AI computing on a range of devices, including mobile phones, in-car platforms and more. Qualcomm's NPE works with the Snapdragon 600 and 800 series processor platforms, and supports a range of common deep learning frameworks including Tensorflow and Caffe2. As more tech companies look for ways to shift AI-based computing functions from remote servers to local platforms in order to improve reliability and reduce requirements in terms of network connectivity, this could be a huge asset for Qualcomm, and a big help in maintaining relevance for whatever comes after mobile in terms of dominant tech trends.


Why businesses should pay attention to deep learning

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I think the future of Alluxio is much brighter when you flip the adjective and the noun and say that it's actually a storage-backed distributed memory system, a shared memory. For example, not every company has an image-recognition problem of scale, but I'll bet every company has transactional data, time series, a transaction log. Let's divide the world into before deep learning on time series and after deep learning on time series. With deep learning, particularly recurrent neural networks like Long Short-term Memories (LSTM), relatively new applied techniques that can model time series in a much more natural way, you don't have to specify arbitrary windows.


How AI Is Crunching Big Data To Improve Healthcare Outcomes

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Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations. By utilizing deep learning algorithms and software, healthcare providers can connect various libraries of medical information and scan databases of medical records, spotting patterns that lead to more accurate detection and greater breadth of efficiency in medical diagnosis and research. IBM Watson, which has previously been used to help identify genetic markers and develop drugs, is applying its neural learning networks to help doctors correctly diagnose heart abnormalities from medical imaging tests. Powered by Baidu's deep learning and natural language processing networks, Melody improves her communication and diagnostic skills by learning from conversations with Baidu's hundreds of millions of users.


Microsoft is building a better HoloLens with a new chip focused on machine learning

PCWorld

Microsoft's HoloLens may have largely faded from public view, but that doesn't mean that Microsoft's halted development on it. On Sunday, Microsoft researchers disclosed that HoloLens development is moving ahead, with a new chip that emphasizes machine learning. Specifically, Microsoft said the next generation of its Holographic Processing Unit, or HPU, will support Deep Neural Network processing, with an emphasis on artificial intelligence, or AI. Harry Shum, executive vice president of the Artificial Intelligence and Research Group, recently showed off the second version of the HPU.


The Reality of the Artificial Intelligence Revolution - Talend

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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 networking – continue.


Microsoft Developing Artificial Intelligence Processor For HoloLens 2

International Business Times

In a blog post Monday, Microsoft detailed plans for its HoloLens 2 mixed reality headset and confirmed that it would utilize a dedicated coprocessor for AI processing. Competitors like Google, Facebook and Nvidia have also explored including similar processors for AI. The increased investment in AI-tailored hardware speaks to the interest and importance AI now has for tech companies. For a mixed reality device like the HoloLens, the potential applications for AI are similar: AI could help power overlays to give you additional information about things you see or hear in the real world.


HoloLens 2.0 will include AI capabilities

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During a speech at the 2017 Computer Vision and Pattern Recognition Conference (CVPR) on Sunday, Microsoft artificial intelligence and research group executive Harry Shum outlined plans to incorporate artificial intelligence into the mixed-reality headset. Custom, native silicon is critical to building HoloLens 2.0's native AI capabilities, without introducing latency or draining the on-board battery too quickly. On-board AI also potentially means that the HPU, and therefore the HoloLens, could recognize new visual information more quickly and create even more impressive augmented reality interactions. Instead, they've worked with partners to create tethered mixed reality headsets that rely on connected computers for all their processing power.