information technology hardware


A look at Intel's big-picture AI on 7nm

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Intel's third quarter earnings report comes out on Oct. 24, but some industry experts have set their sights on years down the line to when investments in artificial intelligence could begin to pay off. "In three-plus years, the real competition will be in AI and machine learning-related systems," said Jack Gold, an analyst at J. Gold Associates, in an email to FierceElectronics. "That means if you're a semi company, it's where you need to have viable products going forward." Some analysts have focused heavily on Intel's competition with AMD and Nvidia for performance in the graphics space, often on a PC or workstation. AMD is growing much faster than Intel, but still has less than one-tenth the revenues.


Artificial Intelligence Hands-On Training - MWC Los Angeles

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Artificial intelligence is the single most transformative tool used across industries today. At this year's MWC Los Angeles, the Global System for Mobile Communications (GSMC) has partnered with the NVIDIA Deep Learning Institute (DLI) to offer hands-on, self-paced training on intelligent video analytics, signal processing, data science, and more, powered by GPUs in the cloud.


Nvidia Stock Is Benefiting From the Next Big Artificial Intelligence Trend – IAM Network

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Bank of American Merrill Lynch analyst Vivek Arya reaffirmed his Buy rating for Nvidia stock on Tuesday and raised his price target to $250 from $225.


Chinese AI Players Face Blacklist Roadblocks Enterprise IT News

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In widening its punishment of China in the ongoing US-China trade war, the US has extended its blacklist to directly handicap its biggest competition in a much higher stakes race, for world domination in AI. Recently, almost ten more AI companies – including providers of video surveillance, facial and speech recognition and data recovery; were added to US trade black list. The reasons cited were related to the violation of human rights by the supposed usage of AI technology in China's repression the Muslim ethnic minority groups of the Uygur region. Here is an interesting digression; that one of the most notable Chinese AI companies in this most recent US blacklist is SenseTime Group (known for its facial recognition AI tech), whose founder Tang Xiao'ou was appointed as the foreign national to Malaysia's sovereign wealth fund, Khazanah Nasional. SenseTime is the top AI'unicorns' startup from China with a valuation of over USD7 billion.


GTC DC 2019 - The Premier AI Conference Returns to D.C.

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NVIDIA's GPU Technology Conference is the premier event on artificial intelligence. Connect with experts to get hands-on technical training and insights into the latest AI and data science approaches, applications and breakthroughs. Choose from 100 talks, panels, posters and demos covering deep learning, machine learning, cybersecurity, autonomous machines, HPC, intelligent video analytics, healthcare, 5G, VR and more.


Sensor Analytics at Micro Scale on the xPU - Data Makes Possible

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Written by Dr. Kirk Borne We often think of analytics on large scales, particularly in the context of large data sets ("Big Data"). However, there is a growing analytics sector that is focused on the smallest scale. That is the scale of digital sensors -- driving us into the new era of sensor analytics. Small scale (i.e., micro scale) is nothing new in the digital realm. After all, the digital world came into existence as a direct consequence of microelectronics and microcircuits.


DRIVE Labs: Eliminating Collisions with Safety Force Field - NVIDIA Developer News Center

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Safety Force Field (SFF) vehicle software is designed specifically for collision avoidance. It acts as an independent supervisor on the actions of the vehicle's primary planning and control system, which could be either human-driven or autonomous. Specifically, SFF performs real-time double-checks of the controls that were chosen by the primary system. If SFF deems the controls to be unsafe, it will veto and correct the primary system's decision. SFF is provably safe, in the sense that, if all road participants comply with SFF and the perception and vehicle controls are within expected design margins, then it can be mathematically proven that no collisions can occur.


King's College London, NVIDIA launch federated learning system for neural networks

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Federated learning allows multiple collaborators to develop and train neural networks using a decentralized platform. The technique eliminates the need to directly distribute patient data across institutions and is considered much more secure.


UCSF, Nvidia partnership will develop new AI tools for radiology

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The University of California, San Francisco is employing Nvidia technology to help develop artificial intelligence tools for clinical radiology. WHY IT MATTERS The two organizations will work together on several AI projects, including brain tumor segmentation, liver segmentation and clinical deployment, leveraging Nvidia's Clara healthcare toolkit and the tech giant's DGX-2 AI system. Clara Medical Imaging provides developers with the tools to build, manage and deploy intelligent imaging workflows and instruments, while Clara Genomics addresses the growing size and complexity of genomics sequencing and analysis with accelerated and intelligent computing. Powered by DGX software and the scalable architecture of Nvidia NVSwitch, the DGX-2 is a 2 petaFLOPS system combining 16 interconnected graphical processing units – the system could help UCSF researchers significantly cut the time to train AI models. The number of images acquired during common studies such as MRI and CT scans has swelled in recent years corresponding with the growing number of patients being imaged.


Nvidia uses federated learning to create medical imaging AI

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AI researchers from Nvidia and King's College London have used federated learning to train a neural network for brain tumor segmentation, a milestone Nvidia claims is a first for medical image analysis. The technique can allow data-sharing between hospitals and researchers while preserving patient privacy. Federated learning is an approach to machine learning that -- when using a client-server approach -- can eliminate the need to create a single data lake in order to train models. Instead, models are trained locally on devices that then transfer insights from multiple machines to a central model. "You need to get to these innovations, and I believe there's kind of two ways. One, which we released last August, is create the best generalizable model that you have today and just send it to each one of these hospitals, where they can localize it for their own patients," Nvidia director of healthcare Abdul Halabi told VentureBeat in a phone interview.