Since 3G, the Third Generation Partnership Project (3GPP) has standardized features and specifications for wireless cellular networks around the world. "5G-Advanced enhancements will unleash a diversity of new capabilities for fixed/mobile broadband as well as vertical industries powered by artificial intelligence, machine learning, and full duplex technologies based on a single platform. Chris Pearson, President of 5G Americas said, "The work done by 3GPP in Release 17 improved 5G New Radio (NR) by adding support for new services, reduced-capability user equipment, non-terrestrial networks, frequency bands beyond 52GHz, and the multicast and broadcast service (MBS). He further added, "The next few years will see initial work on Release18, the 5G-Advanced standard, which will offer a boost to network performance and create even more opportunities." This 5G Americas paper provides a detailed background on 3GPP's work leading into 2025, focusing on the organization's processes, a review of major enhancements and new vertical applications, an assessment of global market trends and use cases, and a developmental timeline for currently active 3GPP releases. It also covers radio access technologies, highlighting 22 new and enhanced RAN features and capabilities.

A collaborative robot, also known as a cobot, is a robot that is capable of learning multiple tasks so it can assist human beings. In contrast, autonomous robots are hard-coded to repeatedly perform one task, work independently and remain stationary. Today, advances in mobile technology, machine vision, cognitive computing and touch technology (including collision avoidance) are making it possible for small, lower-power robots to be aware of their surroundings and perform multiple types of tasks safely in close proximity to human workers. A cobot, when working side by side a human, can quickly learn tasks through demonstration and reinforcement learning. As of this writing, the majority of industrial robots are still autonomous.

Artificial Intelligence is changing the landscape of all industries. AI is touted as the next big frontier, and the logical next step after big data, with the promise of automatic data analysis and cognitive abilities. Providers must leverage AI to create new product lines or improve efficiencies in existing processes that will take them to the next step of innovation. We discuss what you need to know about solutions employing AI tools and on what you should focus your efforts.

Microsoft AI Research Vice President Peter Lee shed some like on how Microsoft will meet those challenges when we met at this week's MIT Technology Review EmTech Digital conference. Lee began with the same explanation that he gave to Microsoft CEO Satya Nadella. Nadella, a former research engineer, does not need to have technology spoon fed to him, but he does need to understand the framework used to manage the future of the vertical industries transferred to the stewardship of Microsoft's AI group. It is a big bet, amounting to 6,000 employees or one-fourth of the company and includes leading industry businesses such as healthcare, education, automotive, finance and retail. Tier 0: The customer can use one of Microsoft's pretrained models, perhaps adding domain-specific categories.

"Tell me, what would you do if you had 1,000 times more data?" I still remember reading this profound question on December 24, 2007 in BusinessWeek's cover article on Google and cloud computing like it was yesterday. It was an interview question, posed by then senior Google engineer Christophe Bisciglia (later founder of red-hot cloud software company, Cloudera and more recently WibiData) to job applicants. For the first time, I began to think through the practical implications of what the advent of the information explosion (now commonly referred to as "Big Data") really meant to both individuals and corporations. How would this onslaught of information effect decision-making across a range of industries, what were the practical implications to businesses and executives, and what were the resulting investment opportunities?

Data mining using machine learning enables businesses and organizations to discover fresh insights previously hidden within their data. Whether exploring oil reserves, improving the safety of automobiles, or mapping genomes, machine-learning algorithms are at the heart of these studies. At Intel, we are quickly moving machine learning from an academic pursuit to a driver of innovation an...d competitive advantage for our business. To that end, Intel IT developed a machine-learning tool that helps Intel's sales and marketing organization identify which resellers will best connect with customers in specific vertical industries. The machine-learning algorithm helps us learn more about our resellers by classifying them and then supplementing this information with another algorithm that mines resellers' website content.