Are we “at the edge” or “going over the edge” with computing technology? Or not even close to the edge? It all depends, as they say, on what you mean by “the edge.” Gartner’s Tom Bittman thinks enterprises aren’t doing “edge computing,” as the phrase is accepted, but “they’re doing automation, and agility, and speed, and customer experience, and productivity, and predictive maintenance, and office health, and public safety, and quality control, and on and on.”
According to Gartner, digital transformation at the edge means that people and things at the edge can interact digitally. They can also interact with the cloud, leverage cloud services, be managed from the cloud—but from the edge point-of-view, this is about controlling equipment with a voice, or automating systems through digital rules, or leveraging machine learning to create smart systems.
We may classify computing that’s extending from the cloud to the edge as “edge computing” but use cases, and topologies, and technologies, and technology stacks are wildly diverse. Bittman notes context matters:
- When edge computing is responsible for reacting to events within a millisecond;
- Or filtering through lots of noisy data to find the useful bits to keep;
- Or keeping an automated store running even when disconnected from the data center or cloud.
The COVID-19 pandemic had an impact on the demand for edge computing solutions as many businesses started operating remotely, which increased the demand for broadband connectivity. The increased demand for edge computing to increase speed, bandwidth, and security concerns and provide accurate and realtime insights, influenced the market during the pandemic.
As a result, the global edge computing market size is expected to reach $140 billion by 2030, according to a study by Polaris Market Research. The adoption of IoT (Internet of Things) and connected devices are significantly influencing the market as companies relying on cloud computing are shifting toward edge computing due to its lower latency and cost feasibility. Moreover, businesses are adopting these solutions as they bring data processing near the source, improving decision-making ability.
And edge computing isn’t the endgame, it’s a building block in the foundation for the next step: AI (artificial intelligence) at the edge. Artificial intelligence has been in development for decades but in 2022, it was “discovered” by many people when OpenAI’s ChatGPT became news. AI models such as GPT—generative pre-trained transformer—are autoregressive language models that use deep learning to produce human-like text. These models represent a change in the field of AI as they offer unique benefits, such as massive reductions in the cost and time needed to create a domain-specific model, but they also pose risks and ethical concerns.
Edge AI is being used more and more frequently, driven by technological advances. In January 2023, Dell Technologies and NVIDIA, both players in edge AI, launched a suite of solutions leveraging on Dell’s PowerEdge servers accelerated by the full NVIDIA AI stack, including NVIDIA’s AI Enterprise software suite. This partnership aims to help businesses accelerate automation by building an AI-first system, leveraging years of expertise from the two companies.
The ability to process realtime data gives edge computing an edge in autonomous vehicle technology, too. Autonomous vehicles, such as Tesla, Google’s Waymo, and Nuro, an autonomous delivery robot, rely on AI algorithms deployed at the edge to provide a complete and multi-layered view of the surrounding environment.
The demand for greater cybersecurity and data residency regulations will also fuel the growth of edge AI. Businesses can regulate the flow of data and reduce exposure to cyberattacks by ensuring that data are kept and processed locally at the source without the need to be transported to a centralized location, the cloud. Edge AI can assist in ensuring compliance with tight data residency laws, with transparency in understanding exactly when, where, and how the data are processed and kept.
There have been new advancements and innovations that helped improve the speed and efficiency of edge AI processing. Some include energy-efficient chips, optimized for AI workloads with faster processing times, enabling edge AI devices to perform realtime tasks. Energy-efficient chips also produce less heat, reducing the risk of thermal heat issues that might impact processing performance.
Pre-built hardware and software AI toolkits and platforms make it easier to develop, deploy, and manage AI solutions on the edge. An example of this would be NVIDIA’s Jetson AGX Xavier Series, enabling AI capabilities on edge devices, specifically for autonomous machines such as delivery and logistics robots.
According to ABI Research, the worldwide shipments for on-premises and edge/cloud AI servers are expected to grow by a CAGR (compounded annual growth rate) of 56% from 2023 to 2028, while the installed base is expected to grow by a CAGR of 63% during the same period. Meanwhile, Mind Commerce sees 85% of all chipsets AI-equipped as they currently ship and predicts more than 63% of all electronics will have some form of embedded intelligence by 2026.
The combination of AI and the IoT has the potential to dramatically accelerate the benefits of digital transformation for consumer, enterprise, industrial, and government market segments. Mind Commerce sees AIoT (artificial intelligence of things) as transformational for both technologies as AI adds value to IoT through machine learning and decision making and IoT adds value to AI through connectivity and data exchange. The AIoT market includes solutions, applications, and services involving AI in IoT systems and IoT support of AI solutions.
With AIoT, AI is embedded into infrastructure components, such as programs, chipsets, and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level, and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.
As IoT networks proliferate throughout every major industry, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product lifecycle management.
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