Information Technology


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.


One More Reason For Running Machine Learning Jobs In The Cloud: GPUs

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Top public cloud vendors want you to store massive data sets in their platforms to run complex Machine Learning algorithms. Apart from offering affordable compute and storage services based on pay-as-you-go pricing model, they are also luring the customers by bringing the latest GPU technology to the cloud. Why is the sudden rush in offering GPUs in the cloud? The answer is simple – It's the rise of Machine Learning. Amazon, Google, IBM, and Microsoft want you to make their cloud the preferred platform for storing, processing, analyzing, and querying data.


Rapid GPU Evolution at Chinese Web Giant Tencent

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Like other major hyperscale web companies, China's Tencent, which operates a massive network of ad, social, business, and media platforms, is increasingly reliant on two trends to keep pace. The first is not surprising--efficient, scalable cloud computing to serve internal and user demand. The second is more recent and includes a wide breadth of deep learning applications, including the company's own internally developed Mariana platform, which powers many user-facing services. When the company introduced its deep learning platform back in 2014 (at a time when companies like Baidu, Google, and others were expanding their GPU counts for speech and image recognition applications) they noted their main challenges were in providing adequate compute power and parallelism for fast model training. "For example," Mariana's creators explain, "the acoustic model of automatic speech recognition for Chinese and English in Tencent WeChat adopts a deep neural network with more than 50 million parameters, more than 15,000 senones (tied triphone model represented by one output node in a DNN output layer) and tens of billions of samples, so it would take years to train this model by a single CPU server or off-the-shelf GPU."


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.


Azure N-Series: General availability on December 1

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I am really excited to announce that the general availability of the Azure N-Series will be December 1st, 2016. Azure N-Series virtual machines are powered by NVIDIA GPUs and provide customers and developers access to industry-leading accelerated computing and visualization experiences. I am also excited to announce global access to the sizes, with N-series available in South Central US, East US, West Europe and South East Asia, all available on December 1st. We've had thousands of customers participate in the N-Series preview since we launched it back in August. We've heard positive feedback on the enhanced performance and the work we have down with NVIDIA to make this a completely turnkey experience for you.


The five upstarts that are leading the AI and machine learning revolution ZDNet

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In the past few years, the market for artificial intelligence (AI) and machine learning technologies has gained strong momentum. "Salesforce is making use of customer data including emails within Salesforce, activity data from tools such as Chatter, as well as external sources like social media and signals from IoT devices to train its machine-learning models, which can in turn drive features within applications, such as predicting churn, predicting close rates and real-time personalized marketing," said 451 Research's Patience. The company is no stranger to new tech trends, but it has recently made a strong pivot to invest heavily in chips made specifically for AI and machine learning applications. How machine learning and AI will'save the entire security industry' (TechRepublic) The 7 biggest myths about artificial intelligence (TechRepublic) Google DeepMind wins again: AI trounces human expert in lip-reading face-off (ZDNet) Microsoft's AI can now understand speech better than humans (TechRepublic) Microsoft's AI can now understand speech better than humans (TechRepublic)


The five upstarts that are leading the AI and machine learning revolution ZDNet

#artificialintelligence

In the past few years, the market for artificial intelligence (AI) and machine learning technologies has gained strong momentum. "Salesforce is making use of customer data including emails within Salesforce, activity data from tools such as Chatter, as well as external sources like social media and signals from IoT devices to train its machine-learning models, which can in turn drive features within applications, such as predicting churn, predicting close rates and real-time personalized marketing," said 451 Research's Patience. The company is no stranger to new tech trends, but it has recently made a strong pivot to invest heavily in chips made specifically for AI and machine learning applications. How machine learning and AI will'save the entire security industry' (TechRepublic) The 7 biggest myths about artificial intelligence (TechRepublic) Google DeepMind wins again: AI trounces human expert in lip-reading face-off (ZDNet) Microsoft's AI can now understand speech better than humans (TechRepublic) Microsoft's AI can now understand speech better than humans (TechRepublic)


IBM and Nvidia make deep learning easy for AI service creators with a new bundle

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On Monday, IBM announced that it collaborated with Nvidia to provide a complete package for customers wanting to jump right into the deep learning market without all the hassles of determining and setting up the perfect combination of hardware and software. The company also revealed that a cloud-based model is available as well that eliminates the need to install local hardware and software. To trace this project, we have to jump back to September when IBM launched a new series of "OpenPower" servers that rely on the company's Power8 processor. The launch was notable because this chip features integrated NVLink technology, a proprietary communications link created by Nvidia that directly connects the central processor to a Nvidia-based graphics processor, namely the Tesla P100 in this case. Server-focused x86 processors provided by Intel and AMD don't have this type of integrated connectivity between the CPU and GPU.


Microsoft releases new version of Microsoft Cognitive Toolkit

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"We've taken it from a research tool to something that works in a production setting," according to Frank Seide, a principal researcher at Microsoft Artificial Intelligence and Research and a key architect of Microsoft Cognitive Toolkit. "Microsoft Cognitive Toolkit represents tight collaboration between Microsoft and NVIDIA to bring advances to the deep learning community," said Ian Buck, general manager of the Accelerated Computing Group at NVIDIA. As expected, the new version of the toolkit will offer its customers a better and faster performance compared to its previous version. Furthermore, Microsoft recently released Windows 10 Creators Update, which will enable anyone to capture, create, and share in 3D.


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.