SEATTLE, Oct. 24, 2019 (GLOBE NEWSWIRE) -- Global supercomputer leader Cray, a Hewlett Packard Enterprise company (NYSE: HPE), today announced that the High-Performance Computing Center of the University of Stuttgart (HLRS) in Germany has selected a new Cray CS-Stormä GPU-accelerated supercomputer to advance its computing infrastructure in response to user demand for processing-intensive applications like machine learning and deep learning. The new Cray system is tailored for artificial intelligence (AI) and includes the Cray Urika -CS AI and Analytics suite, enabling HLRS to accelerate AI workloads, arm users to address complex computing problems and process more data with higher accuracy of AI models in engineering, automotive, energy, and environmental industries and academia. "As we extend our service portfolio with AI, we require an infrastructure that can support the convergence of traditional high-performance computing applications and AI workloads to better support our users and customers," said Prof. Dr. Michael Resch, director at HRLS. "We've found success working with our current Cray Urika-GX system for data analytics, and we are now at a point where AI and deep learning have become even more important as a set of methods and workflows for the HPC community. Our researchers will use the new CS-Storm system to power AI applications to achieve much faster results and gain new insights into traditional types of simulation results." Supercomputer users at HLRS are increasingly asking for access to systems containing AI acceleration capabilities.

Google search is advancing a reading grade. Google says it has enhanced its search-ranking system with software called BERT, or Bidirectional Encoder Representations from Transformers to its friends. It was developed in the company's artificial intelligence labs and announced last fall, breaking records on reading comprehension questions that researchers use to test AI software. Pandu Nayak, Google's vice president of search, said at a briefing Thursday that the muppet-monickered software has made Google's search algorithm much better at handling long queries, or ones where the relationships between words are crucial. You're now less likely to get frustrating responses to queries dependent on prepositions like for" and "to," or negations such as "not" or "no." "This is the single biggest positive change we've had in the last five years," Nayak said--at least according to Google's measures of how ranking changes help people find what they want.

Arnold Schwarzenegger means it when he says: "I'll be back," but not everyone is thrilled there's a new Terminator film out this week. In labs at the University of Cambridge, Facebook and Amazon, researchers fear Terminator: Dark Fate could mislead the public on the actual dangers of artificial intelligence (AI). AI pioneer Yoshua Bengio told BBC News he didn't like the Terminator films for several reasons. "They paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future," says Prof Bengio, who is sometimes called one of the "godfathers of AI" for his work on deep learning in the 1990s and 2000s. "We are very far from super-intelligent AI systems and there may even be fundamental obstacles to get much beyond human intelligence."

Unsupervised learning methods summarize data or transform it such that some desirable properties are enforced. These properties are often easily achieved analytically but are harder to enforce when working in a stochastic optimization (e.g. Before a model is created or a method is defined, some groundwork needs to be laid. What assumptions do we make about the data or the model? How do we know that the model we end up with is good and what do we exactly mean by good?

As more businesses adopt advanced AI tools, they are being prompted to overhaul their existing IT landscape to cope with the added complexity. Claranet's Tanaz Gould explains how they're helping customers create the right infrastructure to cater for these new Machine Learning and Deep Learning workloads.Filmed at Future Decoded 2019.

Artificial Intelligence (AI) is expected to revolutionize the world in the coming decades. An example of this comes from Google's AI research division (DeepMind) which has used the technology to decipher broken and fragmentary ancient Greek texts. The DeepMind project has proven that it is superior to humans when it comes to understanding and deciphering ancient texts that have puzzled researchers for years. Google's artificial intelligence (AI) research arm, DeepMind, and the University of Oxford conducted a project that involved the analysis of Greek inscriptions . Many of them date back millennia and they are often broken and missing letters.

OctoML, a new deep learning startup, has secured $3.9 million in seed financing. Madrona Venture Group led the round with participation from Amplify Partners.

SAN FRANCISCO, Oct. 24, 2019 (GLOBE NEWSWIRE) -- MICRON INSIGHT -- Micron Technology, Inc. (Nasdaq: MU), today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.

It's pretty easy to figure out what I mean if __ few symbols are missing from this sentence. According to a new paper by researchers from DeepMind and the University of Oxford's Faculty of Classics, AI can help restore, understand, and recreate ancient Greek texts that have been damaged and left with gaps that make them nearly impossible to understand. The work will be presented next month at the Empirical Methods in Natural Language Processing conference in Hong Kong. The researchers used an algorithm named after Pythia, the woman who in Greek mythology was a vessel for Apollo's prophecies. They found that it outperformed historians trained in restoring fragmented stone, clay, or metal tablets.

We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions. Test-time training turns a single unlabeled test instance into a self-supervised learning problem, on which we update the model parameters before making a prediction on this instance. We show that this simple idea leads to surprising improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts. Theoretical investigations on a convex model reveal helpful intuitions for when we can expect our approach to help.