Smart devices connected to the Internet of Things (IoT) – in combination with 5G network technology – are now everywhere. But next generation applications, which use artificial intelligence (AI), are starting to arrive on connected objects. This will further reduce latency and improve the higher data transfer speeds of 5G and IoT.
Take the example of a VR or AR headset that not only offers a 3D view of the inside of an airplane engine, but also on-board intelligence to show you problem areas or information about anomalies of this engine, which are immediately and automatically recognized and corrected.
Chipmakers are already developing powerful, energy-efficient processors – or “systems on chip” (SoC) – capable of processing artificial intelligence in a small device. For example, Qualcomm just announced AI-enabled Snapdragon chips that work in smartphones and PCs.
The number of connected IoT devices is expected to exceed 29 billion by 2027
Also on the horizon is a generation of NeuRRAM chips, developed at the University of California San Diego, capable of running large AI algorithms on small devices.
Overall, the number of connected IoT devices is expected to exceed 29 billion by 2027 worldwide. That number is 16.7 billion today, according to a recent analysis by zScaler. “Consumer connected devices are now common, but business process-oriented IoT is driving more transactions,” the report authors point out.
And the growth of this sector will also be achieved with AI. Because 5G and IoT technologies open new doors for innovation in the field of AI – and vice versa. AI “will be most effective when it is equipped with local decision-making capabilities and near real-time data,” says Arun Santhanam, VP at Capgemini Americas. “Low-latency 5G will be key to getting real-time data from relatively inexpensive IoT solutions.”
Most viable use cases for edge computing and AI are in healthcare and manufacturing
Most viable use cases for edge computing and AI are within industries such as healthcare and manufacturing, says Haifa El Ashkar, chief strategy officer at CSG.
In healthcare, for example, “there are now AI-enabled medical devices, such as laparoscopes, that allow surgeons to use real-time information to make life-saving decisions about anomalies that could have gone unnoticed,” explains El Ashkar. “Without 5G, these industries would be unable to exploit edge computing.”
The proliferation of AI-based applications and services also amplifies the power of 5G applications, continues El Ashkar. “When you combine the low latency of 5G networks and the AI capabilities on edge computing, businesses can access real-time decision-making,” he says. “It takes less time for data to travel back and forth between devices and data centers. And AI algorithms that run on connected devices at the edge of the network now offer insights and actions in real time.”
How AI Improves Connectivity
AI also improves connectivity because it “can have a huge impact on the reliability and efficiency of wireless networks and enable new ways to stay connected,” says Milind Kulkarni, head of InterDigital’s Wireless Lab. “For example, the combination of 5G, cloud and edge computing is crucial to enabling immersive experiences in the metaverse.”
While more centralized environments – clouds and data centers – can provide the computing power needed for immersive experiences, “they may be too far away from where the low-latency resources are,” says Kulkarni.
“To take advantage of the ultra-low latency that is one of the key benefits of 5G, edge computing plays a critical role in delivering smaller amounts of storage and computing, much closer to the device using it. needs. Additionally, edge computing can be customized to support specific use cases such as storing content for streaming video on demand or running artificial intelligence algorithms for rapid decision-making on incoming data.”
Source: “ZDNet.com”