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IBM's Power9-based AC922 system designed for AI workloads

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IBM is ready to start shipping the first commercial server systems built around its recently released Power9 processor. Dubbed the AC922 Power Systems, these servers will ship by the end of December, and are specifically designed for artificial intelligence (AI) workloads, reports Enterprise Cloud News (Banking Technology's sister publication). The AC922 is the commercial version of the same severs that IBM, along with Nvidia and Mellanox Technologies is using to build two new supercomputers for the US Department of Energy. The "Summit" and "Sierra" supercomputers are expected to go online in 2018, and could reinvigorate the US's standing in the world of high-performance computing. At the heart of the AC922 is IBM's recently released Power9 processor.


Singtel to open SG$42m AI lab in Singapore

ZDNet

Singtel has announced that it will be establishing a SG$42.4 million Cognitive and Artificial Intelligence Lab for Enterprises (SCALE) to work on researching and developing applications across public safety, smart city, transportation, healthcare, and manufacturing solutions. The lab, to be built under a five-year partnership with Nanyang Technological University Singapore (NTU Singapore), and the National Research Foundation Singapore, will focus on AI, robotics, smart computing, and advanced data analytics. Around 100 Singtel and NTU researchers will work at SCALE, with 200 research engineers and students to be trained there too. "This collaboration marks a significant step for Singtel to develop intellectual property in emerging technologies to support enterprises in their digital transformation and Singapore's Smart Nation objectives," Singtel Group Enterprise CEO Bill Chang said. The lab is part of driving Singapore's smart nation initiative, Singtel said, with the telecommunications carrier also signing an agreement with the Singaporean Agency for Science, Technology and Research (ASTAR).


H2O.ai Raises $40 Million to Democratize Artificial Intelligence for the Enterprise

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WIRE)--H2O.ai, the leading company bringing AI to enterprises, today announced it has completed a $40 million Series C round of funding led by Wells Fargo and NVIDIA with participation from New York Life, Crane Venture Partners, Nexus Venture Partners and Transamerica Ventures, the corporate venture capital fund of Transamerica and Aegon Group. The Series C round brings H2O.ai's total amount of funding raised to $75 million. The new investment will be used to further democratize advanced machine learning and for global expansion and innovation of Driverless AI, an automated machine learning and pipelining platform that uses "AI to do AI." H2O.ai continued its juggernaut growth in 2017 as evidenced by new platforms and partnerships. The company launched Driverless AI, a product that automates AI for non-technical users and introduces visualization and interpretability features that explain the data modeling results in plain English, thus fostering further adoption and trust in artificial intelligence.


Mainstreaming HPC Codes Will Propel The Next GPU Wave

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All the shiny and zippy hardware in the world is meaningless without software, and that software can only go mainstream if it is easy to use. It has taken Linux two decades to get enterprise features and polish, and Windows Server took as long, too. So did a raft of open source middleware applications for storing data and interfacing back-end databases and datastores with Web front ends. Now, it is time for HPC and AI applications, and hopefully, it won't take this long. As readers of The Next Platform know full well, HPC applications are not new.


H2O.ai raises $40 million to democratize data science

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Artificial intelligence and machine learning are two phrases that are thrown around a lot in the tech world these days. It has gotten the point where every company has to say they're an AI company, even if they really don't have AI capabilities, just to be taken seriously. The problem is that the best data scientists all want to work for the same few companies: Google, Facebook or Apple. So what are the smaller companies to do? That's the problem that H2O.ai is solving.


Deep Learning Technologies Enabling Innovation Contexti

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"Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day." With innovation driving business success, the demand for community-based, open-source software that incorporates AI & deep learning is taking over start-ups and enterprises alike. We've rounded up a few successful deep learning technologies that are making a big impact. TensorFlow is an open source software library that uses data flow graphs for numerical computation. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays communicated between them.


Lenovo says AI crucial for enterprise as it announces new tech for training machine-learning systems

ZDNet

Lenovo has announced new hardware and software for firms building machine-learning systems, as the Chinese tech giant double down on AI. Lenovo expects firms will increasingly rely on AI systems to make rapid decisions based on the vast amount of data being generated, predicting will be 44 trillion gigabytes of data will exist by 2020. To serve the fast-growing market, Lenovo today announced new hardware and software for streamlining machine-learning on high-performance computer systems. The ThinkSystem SD530, a two-socket server in a 0.5U rack form factor, is now available with the latest NVIDIA GPU accelerators and Intel Xeon Scalable family CPUs. By including the option of adding NVIDIA's Tesla V100 GPU accelerator, Lenovo is giving businesses the ability to massively boost the performance of AI-related tasks.


A future of AI-generated fake news photos, hands off machine-learning boffins – and more

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Good morning, or afternoon, wherever you are. Here's a roundup of recent AI developments on top of everything else we've reported over the past week or so. Researchers at Nvidia have developed and described a new way to train generative adversarial networks (GANs) in a more stable manner to generate a series of, what appears at first glance, seemingly realistic convincing photos. In other words, this is a neural network that can produce, at a decent resolution, fairly plausible photos of things – from couches to buildings – on demand from scratch. The computer can invent or fabricate scenes for you or anyone else, from a description: pretty much on-demand fake news.


NVIDIA opens up its Holodeck VR design suite

Engadget

That helps engineers and designers build and interact with photorealistic people, objects and robots in a fully simulated environment. For instance, NVIDIA showed how the engineers that built the Koenigsegg supercar could explore the car "at scale and in full visual fidelity" and consult in real time on design changes. With Holodeck, NVIDIA is taking on Microsoft and its Hololens in the enterprise and design arena -- though the latter AR system is more about letting engineers interact with real and virtual objects at the same time. Much of this very advanced tech is bound to trickle down to consumers, hopefully making VR and AR good enough to actually become popular.


Will machine learning save the enterprise server business?

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Neural networks apply computational resources to solve machine learning linear algebra problems with very large matrices, iterating to make statistically accurate decisions. Most of the machine learning models in operation today started in academia, such as natural language or image recognition, and were further researched by large well-staffed research and engineering teams at Google, Facebook, IBM and Microsoft. Enterprise machine learning experts and data scientists will have to start from scratch with research and iterate to build new high-accuracy models. It is a specialty business because the enterprises need four characteristics not necessarily found together: a large corpus of data for training, highly skilled data scientists and machine learning experts, a strategic problem that machine learning can solve, and a reason not to use Google's or Amazon's pay-as-you-go offerings.