Today, we live in a world where technology is redefining the way we work, live and play. Significantly, our future is quickly being reshaped by digital transformation technologies like Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, Internet of Things (IoT), 5G and Edge Computing. Indeed, data management is going through profound changes, opening up countless opportunities. More so, as businesses are looking for comprehensive services and process thousands of scenarios, spanning considerations hinge on everything from central storage to frontier-edge computing. As we step into 2020, here's my take on the top five technology trends that will impact businesses, society, and the world.
With the advancements in deep learning, the recent years have seen a humongous growth of artificial intelligence (AI) applications and services, traversing from personal assistant to recommendation systems to video/audio surveillance. All the more as of late, with the expansion of mobile computing and Internet of Things (IoT), billions of mobile and IoT gadgets are connected with the Internet, creating zillions of bytes of information at the network edge. Driven by this pattern, there is a pressing need to push the AI frontiers to the network edge in order to completely release the potential of the edge big data. To satisfy this need, edge computing, an emerging paradigm that pushes computing undertakings and services from the network core to the network edge, has been generally perceived as a promising arrangement. The resulting new interdiscipline, edge AI or edge intelligence (EI), is starting to get an enormous amount of interest.
Phillip Marangella, CMO for EdgeConneX explores how edge data centers can help us rearchitect the internet in a way that will support the flood of data and massive traffic flows generated by emerging technologies like AI, cloud gaming, VR/AR and multi-cloud deployments, and more. The internet was not constructed to handle the traffic flows of today, and it's only going to get more congested in the coming months and years. Traditionally, traffic flows on the internet have largely been download-centric and networks have been built out to support those flows. However, the gravity of data and compute has shifted from the core to the edge as a result of technologies like the Internet of Everything (IoE), artificial intelligence and machine learning, cloud gaming and HD streaming and virtual reality. There is much more content and data that is now being created, stored and processed at the edge.
Hyperautomation is the combination of multiple machine-learning (ML), packaged-software and automation tools to deliver work. Hyperautomation refers not only to the breadth of tools available, but also to all the steps of automation itself (discover, analyze, design, automate, measure, monitor and reassess), Cearly said. Understanding the range of automation mechanisms, how they relate to one another and how they can be combined and coordinated is a major focus for hyperautomation. Hyperautomation requires a combination of tools to help support replicating pieces of where the human is involved in a task. Through 2028, the user experience will undergo a significant shift in how users perceive the digital world and how they interact with it.
Just a few years ago, many expected all the Internet of Things (IoT) to move to the cloud--and much of the consumer-connected IoT indeed lives there--but one of the key basics of designing and building enterprise-scale IoT solutions is to make a balanced use of edge and cloud computing.1 Most IoT solutions now require a mix of cloud and edge computing. Compared to cloud-only solutions, blended solutions that incorporate edge can alleviate latency, increase scalability, and enhance access to information so that better, faster decisions can be made, and enterprises can become more agile as a result. That being said, complexity introduced by edge computing should justify the objectives at hand, which include scale, speed, and resiliency. A choice that goes too far in one direction typically introduces substantial operational complexities and expenses.