AWS DeepRacer is a fun and easy way for developers with no prior experience to get started with machine learning (ML). At the end of the 2019 season, the AWS DeepRacer League engaged the Amazon ML Solutions Lab to develop a new sports analytics feature for the AWS DeepRacer Championship Cup at re:Invent 2019. The purpose for these real-time analytics was to provide context and more in-depth experience with top competitors' strategies and tactics. This helped viewers tangibly interpret how specific model strategy translated to on-track performance, which further demystified ML development and demonstrated its real-world application. This enhancement enabled fans to monitor the performance and driving style of competitors from around the world.

NEW YORK--(BUSINESS WIRE)--Legal technology leader DISCO today announced a significant update to DISCO Analytics Suite that transforms how artificial intelligence (AI) can be leveraged across all of an organization's cases. DISCO Analytics Suite now includes cross-matter AI, which empowers legal teams to leverage all of their work product from past and ongoing matters at the start of every new case. Previously, AI models were case-specific, with learnings and predictions built on how documents were tagged in a single matter. With each new case, legal teams were forced to tag documents and build new models from scratch. With DISCO's cross-matter AI, organizations can now import AI models at the beginning of every case that are powered by the tagging decisions of previous matters.

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.

Science fiction anticipated the power of AI, predictive analytics and "talking" computers. But it typically showed only a small elite (Starfleet officers, hackers, robed mutant priesthoods) getting to use this influential technology. In real life, AI and its data-driven insights can benefit people with all levels of programming expertise – not just data scientists and computational linguists but line-of-business workers such as bankers, engineers, fashion merchandisers and insurance claims processors. OpenText knows that in every industry, AI-powered analytics and text mining can streamline operations and make them more cost-efficient, optimize the customer/citizen experience, and spot new business opportunities. EP6 (or Release 16.6), the biggest upgrade to the OpenText AI & Analytics offerings since Magellan was unveiled in 2017, continues OpenText's progress in analyzing and deriving value from both structured data (figures and labels that fit neatly into spreadsheet cells) and unstructured data (the billions of words of written documents, text chats, emails, social media, scanned images, and other content that wash around us every day).

Sign up for our newsletter to not miss out on tomorrow's game-changers for your industry. Sport clubs like Paris Saint-Germain and Chelsea are going digital as they envisage a new level of sport performance analytics. And we can support this next generation sport analytics with platforms collecting any type of sensory data and with analytics suites enabling real-time correlation and presentation of data in human readable format to experts of sport domain. This will make it possible to eliminate today's limitations of vertical sensor analytics and offline correlations. There are a range of possibilities for providing these kinds of solutions to large sport arenas, like soccer, with capabilities of low latency, low energy consumption and large coverage range.

I can sift through millions of data points, analyze them using sophisticated analytics models and respond with answers in seconds! I'm also learning continuously to improve recommendations as we go along." This creates new users and unbelievably new usages for Analytics Maya's Causality Model uses Machine Learning and Neural networks to predict and explain cause and effect.