On February 9, 2017, two technology market leaders made announcements: SAP unveiled its next-generation intelligent ERP system, and Nvidia announced that demand for artificial intelligence (AI) applications was driving demand for its graphics platform. Simply speaking, cognitive computing refers to self-learning systems that mimic the way the human brain works. In the SAP announcement, the "intelligent" piece of "intelligent ERP" comes from a digital assistant (SAP CoPilot) of which users can ask questions and to which they can give commands via voice, text, or gestures, just as they would to a human assistant. We might do the same on our path to build artificial intelligence brains."
CHICAGO, April 20, 2017 –SAP SE (NYSE: SAP) today announced SAP Fieldglass Live Insights, a new machine learning-powered data-driven insights service that makes it possible for executives to benchmark, plan, predict and simulate external talent scenarios. The new SAP Fieldglass service is the result of partnering with SAP Data Network to create this capability that is powered by SAP HANA . With SAP Fieldglass Live Insights, organizations can benchmark market rates, hiring cycle times and supplier performance as part of a seamless process that culminates with decisions about external talent and their engagement, using SAP Fieldglass data and trusted, supplemental third-party data that has been aggregated, anonymized and normalized. Organizations also can upload their own employee data into SAP Fieldglass Live Insights to gain visibility of their talent across channels in real time. Rob Brimm, president, SAP Fieldglass, said: "With access to the most robust data via SAP Fieldglass Live Insights, our customers can analyze and make decisions at speed about their external talent – arguably one of their most valuable assets – as they race to become more competitive in every aspect of their business.
Following their success in data handling and processing to fuel the fire of Machine Learning, SAP has now jumped head long into the direct integration of Machine Learning in their tool through the launch of SAP CLEA. SAP CLEA is a recently launched Machine Learning Solution of SAP providing embedded intelligent systems on the SAP cloud platform with various applications involved. The focus is to drive business applications with smart algorithms and models for maneuvering unprecedented insights, be able to make better predictions and decisions and also automate the various routine redundant tasks while focusing on higher-value work. SAP CLEA is the first front of the future of SAP's Machine Learning systems which are to include several applications, tools and services, all developed in collaboration with and feedback from the various co-innovation customers across domains and regions.
The term artificial intelligence (AI) is thrown around in many contexts, especially in the tech industry. However, many people (including those in IT) don't actually understand what AI is, let alone the challenges and opportunities it presents. This is the first in a series of "AI for Dummies" blogs where I'll share the basics of all things AI. As computer systems become ever more capable of performing the tasks that traditionally are staffed by humans, this evolution will affect nearly every industry. In the short term, there will be positions that are replaced by machines that lead to job loss.
We all see the term artificial intelligence (AI) thrown around in many contexts. AI is a buzzword in the tech industry in particular. However, many people (including those in IT) don't actually understand what AI is nor the challenges and opportunities it presents. Today, I'm beginning a series of blogs to share the basics of all things AI. As computer systems become ever more capable of performing the tasks that traditionally are staffed by human employees, the evolution will affect nearly every industry.
SAP Labs in India is the second largest R&D centre for the company after its centre in Walldorf, Germany and among the three hubs in the SAP Labs network of 19 Labs across 16 countries. Dilipkumar Khandelwal, MD for SAP Labs in India, has a dual role as he is also the EVP and Global Head of Enterprise Cloud Services for SAP. In an exclusive interview with Ayushman Baruah, Khandelwal talks about their India focus, latest technologies, and their emphasis on innovation. Excerpts: What is the SAP Lab's focus here? Over a period of 19 years, SAP Labs India has evolved to become an integral part of SAP's global strategy.
On February 9, 2017, two technology market leaders made announcements: SAP unveiled its next-generation intelligent ERP system, and Nvidia announced that demand for artificial intelligence (AI) applications was driving demand for its graphics platform. On the face of it, these announcements were business as usual – routine sound bites that proliferate in the tech news landscape. Look a bit deeper, though, and you realize that this day marked a profound shift in both the way businesses use technology and the implications for the rest of us. For decades, developing a computer that could think has been the Holy Grail of technology. And while we have made tremendous progress in our ability to process vast amount of data, the "thinking" part has remained mostly elusive.
SAP has added some new capabilities to SAP Vora, its in-memory distributed computing system based on Apache Spark and Hadoop. Version 1.3 of Vora includes a number of new distributed, in-memory data-processing engines, including ones for time-series data, graph data and schema-less JSON data, that accelerate complex processing. Common uses for the graph engine might be analyzing social graphs or supply chain graphs, said Ken Tsai, SAP's head of product marketing for database and data management. One application that would benefit from the new time-series engine is looking for patterns of electricity consumption in smart metering data. "You can certainly do it without the time-series engine, but it's not as efficient," Tsai said.
Machine Learning involves algorithms that learn from and make predictions on data and, generally speaking, more data means better predictions. Combine that with the vast amounts of data that most organizations are now generating, and the transformational potential of Machine Learning is nothing short of amazing. It's no surprise that Predictive Analytics and Machine Learning are two of the hottest areas in analytics today, as organizations see their potential to help with Digital Transformation. Enterprises are investing heavily with the hope of reaping big business benefits from smarter business processes & better decisions that improve their Return on Investment. Let's be honest though, many of us don't understand how the complex algorithms that make Machine Learning work translate into measureable business results, and there is so much hype that it's difficult to separate great marketing from great products.
Spurred by the rise of chatbots in consumer apps, companies are giving voice (literally) to virtual assistants in the workplace. Gartner research predicts that by 2020, we'll conduct 30 percent of our web browsing sessions without a screen. While these analysts envision the impact on consumers as they drive, cook or exercise, this newest generation of voice-first applications promises even more for employees in the workplace. Gartner predicts we'll conduct 30 percent of our web browsing sessions without a screen by 2020 . Technology that speeds up daily tasks with simple voice commands will have a tremendous effect.