Telstra has announced the acquisition of Readify, a developer of Microsoft software applications, saying it will bolster the telecommunications provider's cloud offerings. "As we know, apps and software in general are playing an increasingly important role in businesses. Readify is recognised globally for its innovative software solutions and will further help us create software-led digital transformations with our customers," Bendschneider said. "Readify will provide application development and data analytics services, nicely complementing Kloud's existing services. It will enable Telstra to add incremental value to customers in enterprise cloud applications, API-based customisation, and extensions, as well as business technology advisory services."
The acquisition will bring Zementis' predictive analytics to Software AG's real-time streaming analytics platform. Software AG has acquired California-based Zementis for an undisclosed sum in a move designed to bolster its internet of things capability. Zementis offers software for'deep learning' which plays a crucial role in the development of machine learning, data science and fundamental technology that drives artificial intelligence (AI) development. According to Software AG, the advances in machine learning and AI are being applied in the next generation Internet of Things (IoT) such as self-driving cars, personal digital assistants, medical diagnosis, predictive maintenance and robotics. Software AG has already employed Adaptive Decision and Predictive Analytics (ADAPA) from Zementis into its Digital Business Platform to offer its clients with comprehensive insights for real time business analytics.
Nvidia CEO Jen-Hsun Huang introducing the Nvidia Spot, a USD 49.95 microphone and speaker that will let owners use Google Assistant anywhere in a home, at the company's CES 2017 keynote (Photo by Ethan Miller/Getty Images) Nvidia continued to see demand for its graphics processors in the emerging world of artificial intelligence in its fourth quarter earnings reported Thursday. In its fourth quarter earnings release, the Santa Clara, Calif.-based company reported revenue of $2.17 billion, up 55% year over year, on earnings per share of $1.13, up 117% a year ago. Wall Street analysts estimated $2.11 billion in revenue on EPS of 83 cents. Traditionally, the company's processors have been mostly used to power the latest gaming graphics, but the chips have become popular to run AI software in the data center and autonomous vehicles. A specific branch of AI, called deep learning, is where Nvidia's processors particularly shine.
IBM (NYSE: IBM) today revealed a series of new servers designed to help propel cognitive workloads and to drive greater data center efficiency. Featuring a new chip, the Linux-based lineup incorporates innovations from the OpenPOWER community that deliver higher levels of performance and greater computing efficiency than available on any x86-based server. Collaboratively developed with some of the world's leading technology companies, the new Power Systems are uniquely designed to propel artificial intelligence, deep learning, high performance data analytics and other compute-heavy workloads, which can help businesses and cloud service providers save money on data center costs. The three new systems are an expansion of IBM's Linux server portfolio comprised of IBM's specialized line of servers co-developed with fellow members of the OpenPOWER Foundation. The new servers join the Power Systems LC lineup that is designed to outperform x86-based servers on a variety of data-intensive workloads.
Researchers from MIT's Computer Science and Artificial Laboratory (CSAIL) alongside machine learning-startup PatternEx have created a new cybersecurity defense system that makes use of both unsupervised and supervised learning methods. Human analysts are then presented with the data and given an opportunity to identify actual attacks, which are then fed back into the machine. The system learns and refines its accuracy over time. CSAIL research scientist Kalyan Veeramachaneni, one of AI,2's co-creators, described it this way: "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions. That human-machine interaction creates a beautiful, cascading effect."