How telecom providers are embracing cognitive app development


As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.

Q&A: Analytics-Driven Embedded Systems


Analytics-driven embedded systems bring analytics to embedded applications, moving many of the functions found in cloud-based, big-data analytics to the source of data. Pilotte: The ability to create analytics that process massive amounts of business and engineering data is enabling designers in many industries to develop intelligent products and services. Using engineering data from thermometers, pressure sensors, and other HVAC sensors, as well as business data from weather forecasts and real-time energy prices, the analytics running in BuildingIQ's cloud service tune the building's HVAC embedded systems. That includes combining an analytics workflow with an embedded design workflow that includes the system simulation, verification, and hardware targeting that we know are essential.

Building the Foundation of the Cognitive Computing Era


When most people think about artificial intelligence and cognitive computing, they think of futuristic technological landscapes overrun with evil robots. Scott Crowder is currently Chief Technical Officer and Vice President, Technical Strategy and Transformation for IBM Systems. Previously, Scott was Vice President, Technical Strategy within IBM Corporate Strategy. In this role, he helped define the cross-IBM technical strategy for cloud infrastructure, workload optimized systems, Big Data and Analytics, composable services, software-defined infrastructure, and cognitive solutions.