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Element AI, a platform for companies to build AI solutions, raises $102M

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Element AI -- a Montreal-based platform and incubator that wants to be the go-to place for any and all companies (big or small) that are building or want to include AI solutions in their businesses, but lack the talent and other resources to get started -- is announcing a mammoth Series A round of $102 million. They include Fidelity Investments Canada, Korea's Hanwha, Intel Capital, Microsoft Ventures, National Bank of Canada, NVIDIA, Real Ventures, and "several of the world's largest sovereign wealth funds." But the basic model is not: Element AI is tackling this problem essentially by leaning on trends in outsourcing: systems integrators, business process outsourcers, and others have built multi-billion dollar businesses by providing consultancy or even fully taking the reins on projects that businesses do not consider their core competency. Element AI says that initial products that can be picked up there include predictive modeling, forecasting models for small data sets, conversational AI and natural language processing, image recognition and automatic tagging of attributes based on images, 'aggregation techniques' based on machine learning, reinforcement learning for physics-based motion control, compression of time-series data, statistical machine learning algorithms, voice recognition, recommendation systems, fluid simulation, consumer engagement optimization and computational advertising.


Kinetica Delivers Advanced In-Database Analytics, Opening the Way for Converged AI and BI Workloads Accelerated by GPUs

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Kinetica's advanced in-database analytics make it possible for organizations to affordably converge Artificial Intelligence, Business Intelligence, Machine Learning, natural language processing, and other data analytics into one powerful platform. "In response to customer demand, we have combined the power of GPU-acceleration technology with UDFs, so customers can perform in-database advanced analytics and machine learning operations on massive datasets in real time, right alongside BI workloads," said Nima Negahban, CTO and co-founder, Kinetica. With Kinetica Reveal data exploration framework, business analysts can make faster decisions by visualizing and interacting with billions of data elements instantly. The new UDF capability, Reveal data exploration framework and VRAM Boost Mode are immediately available in version 6.0 of Kinetica's GPU-accelerated database.


How AI Is Accelerating Retail Transformation

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Forbes Shutterstock Image READ MORE 6. "There an estimated 3,000 AI startups worldwide, and many of them are building on NVIDIA's platform. They're using NVIDIA's GPUs to put AI into apps for trading stocks, shopping online and navigating drones." Read more … Aaron Tilley Writer 7. The retail sector is now best positioned to leverage AI and Deep Learning, as these new technologies are developing… 8. READ MORE AI software such as Computer Vision is being developed by startups to help retail consumers find the perfect and individualized fit. THIRD LOVE A app that enables women to find the right fitting bra from home using a mobile device and deep learning. VOLUMENTAL Offers computer vision applications for sizing shoes and eyewear to create a individualized retail experience for customers.


NVIDIA and Microsoft Accelerate AI Together

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This jointly optimized platform runs the new Microsoft Cognitive Toolkit (formerly CNTK) on NVIDIA GPUs, including the NVIDIA DGX-1 supercomputer, which uses Pascal architecture GPUs with NVLink interconnect technology, and on Azure N-Series virtual machines, currently in preview. Faster performance: When compared to running on CPUs, the GPU-accelerated Cognitive Toolkit performs deep learning training and inference much faster on NVIDIA GPUs available in Azure N-Series servers and on premises. Faster performance: When compared to running on CPUs, the GPU-accelerated Cognitive Toolkit performs deep learning training and inference much faster on NVIDIA GPUs available in Azure N-Series servers and on premises. Certain statements in this press release including, but not limited to the impact and benefits of NVIDIA's and Microsoft's AI acceleration collaboration, Tesla GPUs, DGX-1, the Pascal architecture, NVLink interconnect technology and the Microsoft Cognitive Toolkit; the availability of Azure N-Series virtual machines; and the continuation of NVIDIA's and Microsoft's collaboration are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations.


Supermicro(R) Introduces NVIDIA(R) Pasca(TM) GPU-Enabled Server Solutions Featuring NVIDIA Tesla(R) P100 GPUs

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Super Micro Computer, Inc. (SMCI), a global leader in compute, storage, networking technologies and green computing today announced the general availability of its SuperServer solutions optimized for NVIDIA Tesla P100 accelerators with the new Pascal GPU architecture. "The new SuperServers deliver superior energy-efficient performance for compute-intensive data analytics, deep learning and scientific applications while minimizing power consumption." With the convergence of Big Data Analytics, the latest GPU architectures, and improved Machine Learning algorithms, Deep Learning applications require processing power of multiple GPUs that must communicate efficiently and effectively to expand the GPU network. Supermicro (SMCI), the leading innovator in high-performance, high-efficiency server technology is a premier provider of advanced server Building Block Solutions for Data Center, Cloud Computing, Enterprise IT, Hadoop/Big Data, HPC and Embedded Systems worldwide.


Intel's data center chief talks about machine learning without GPUs

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If you want to get under Diane Bryant's skin these days, just ask her about GPUs. The head of Intel's data center group was at Computex in Taipei this week, in part to explain how the company's latest Xeon Phi processor is a good fit for machine learning. Machine learning is the process by which companies like Google and Facebook train software to get better at performing AI tasks including computer vision and understanding natural language. It's key to improving all kinds of online services: Google said recently that it's rethinking everything it does around machine learning. "It's a big opportunity, and there will be a hockey stick where every business will be using machine learning," she said in an interview.


Intel's data center chief talks machine learning -- just don't ask about GPUs

PCWorld

If you want to get under Diane Bryant's skin these days, just ask her about GPUs. The head of Intel's powerful data center group was at Computex in Taipei this week, in part to explain how the company's latest Xeon Phi processor is a good fit for machine learning. Machine learning is the process by which companies like Google and Facebook train software to get better at performing AI tasks including computer vision and understanding natural language. It's key to improving all kinds of online services: Google said recently that it's rethinking everything it does around machine learning. "It's a big opportunity, and there will be a hockey stick where every business will be using machine learning," she said in an interview.


Nvidia CEO bets on artificial intelligence as the future of computing

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Huang said deep learning will be the basis for the entire computer industry, including data centers and the cloud, for years to come. Huang also said he believes AI and deep learning will transform data centers and cloud services. Rajat Monga, a Google technical lead and manager of TensorFlow, an open source software library for machine learning that was developed at Google, said the company thinks deep learning will infuse every Google service, including new areas such as robotics. It's what he called the world's first car computing platform powered by deep learning.