IBM (NYSE: IBM) unveiled "Project DataWorks," a Watson initiative that is a cloud-based data and analytics platform to integrate all types of data and enable AI-powered decision-making. Project DataWorks is designed to make it simple for business leaders and data professionals to collect, organize, govern and secure data, so they can gain the insights needed to become a cognitive business. Businesses today understand the competitive advantage of gaining insights from data. However, obtaining those insights can be increasingly complex, and most of this work is done by highly skilled data professionals who work in silos with disconnected tools and data services that may be difficult to manage, integrate, and govern. Also, because data is never static, businesses must continually iterate their data models and products--often manually--to benefit from the most relevant, up-to-date insights.

Cognitive systems learn from vast amounts of complex, ambiguous information and help us do amazing things, such as treat disease, manage finances, and transform commerce. Underneath these systems, the core fields of science & technology --from artificial intelligence to brain science to computer architecture to cognitive science-- are advancing rapidly and achieving breakthroughs not envisioned even a few years ago. IBM Research and its network of scientific partners are pursuing some of the hardest technical problems while creating practical solutions that make a difference to the world. Accompanied by special guests, Guruduth Banavar will highlight IBM's commitment to advancing the science and technology behind cognitive computing.

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Frankfurt, Germany October 17, 2016 – Aries Systems Corporation today announces the integration of Meta Bibliometric Intelligence into Editorial Manager, Aries' industry-leading manuscript and peer-review tracking system for scholarly publications. The new technology, created by Meta, applies artificial intelligence toward the identification of high-impact manuscripts at the moment of first submission, allowing editors to triage and rank incoming manuscripts. "By incorporating Meta Bibliometric Intelligence into Editorial Manager, we're the first workflow system that helps publishers explore the potential of big data analysis during peer review," said Richard Wynne, Vice President of Sales and Marketing at Aries Systems. Bibliometric Intelligence uses sophisticated machine learning algorithms that were trained using Meta's corpus of millions of full-text articles – a collection that now comprises the largest scholarly text-mining collection on Earth. As newly-submitted manuscripts are processed, hundreds of unique features are pulled from the papers and fed through the algorithms.

In the next five years, every important decision, whether it's business or personal, will be made with the assistance of IBM Watson. Watson, the company's artificial intelligence-fueled system, is working in fields like health care, finance, entertainment and retail, connecting businesses more easily with their customers, making sense of big data and helping doctors find treatments for cancer patients. The Watson system is set to transform how businesses function and how people live their lives. "Our goal is augmenting intelligence," Rometty said. "It is man and machine. This is all about extending your expertise. It doesn't matter what you do. IBM's conference this week, which the company said drew 17,000 attendees, explored how companies, including retailers, educators, human resources departments and financial institutions, amon others, can use Watson. "The challenge IBM has right now is to define the marketplace," said Jeff Kagan, an independent industry analyst, who attended the conference. "Ten years from now, will IBM be the leader?

A London-based startup has combined some of today's most disruptive technologies in a bid to change the way we'll build the future. By retrofitting industrial robots with 3D printing guns and artificial intelligence algorithms, Ai Build has constructed machines that can see, create, and even learn from their mistakes. When CEO and founder Daghan Cam was studying architecture, he noticed a disconnect between small-scale manufacturing and large-scale construction. "On one side we have a fully automated production pipeline," Cam explained at a recent conference in London. With the emergence of more efficient printing technologies, he thought there must be a better way.

Google parent Alphabet (Xetra: ABEA.DE - news) delivered higher profits for the third quarter, lifted by gains in mobile and video advertising as the tech giant narrowed losses on its "moon shots." Net (LSE: 0LN0.L - news) profit climbed 27 percent to $5.1 billion. Revenue rose to $22.5 billion from $18.7 billion in the same period a year earlier. Shares (Berlin: DI6.BE - news) rose nearly one percent in after-market trades that followed the release of the stronger-than-expected earnings figures. "We had a great third quarter," Alphabet chief financial officer Ruth Porat said in the earnings release.

Companies have been trying to drum up enthusiasm for them for years, with little success. Pepper, a humanoid machine carrying the hopes of SoftBank Group Corp.'s billionaire founder Masayoshi Son, was supposed to change that. Promoted as the first robot to be endowed with emotions, the company marketed Pepper aggressively after it was unveiled in 2014, promising the gadget was sophisticated enough for tasks usually handled by shop clerks, receptionists and translators. "It's not there to have a conversation," said Junichi Nishi, a municipal government official in Fujieda, a city of about 140,000 in central Japan. "We use it primarily as a tablet," he said, referring to the touch screen attached to the robot's chest.

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The goal is now in sight – if not yet in reach: a fully-automated operational production environment. The rise of DevOps shows the progress we've made in automating the provisioning and configuration of ops, as well as application deployment. IT Operations Management (ITOM), and in particular Application Performance Management (APM) are now well on their way to realizing this hands-off vision. In today's complex enterprise production environments, we still need people – but as the environments and applications become more difficult to manage, we must give our ops personnel smarter, more powerful tools. How to use an abacus – not the algorithms we're looking for First-generation monitoring tools simply took events and log entries and fed them to hapless ops personnel as alerts.