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Why the Future of AI is on the Edge

Edge AI

Today’s societies are becoming ever more data-centric and automated. Autonomous systems are already hitting our roads, oceans, and air space. Millions of smart sensors are getting embedded into cars, smart cities, smart homes, and warehouses using intelligent system, promising to connect everything from people to machines and even robotic agents. This rapid growth in the number of intelligent applications is expected to drive the growth of the edge AI software market.

What’s driving edge AI?

Most AI processes are carried out using cloud-based data centers that need substantial compute capacity. These expenses can add up quickly. Also, when AI applications are run on cloud technologies, they experience latency problems, making it difficult to provide fast responses.

Edge AI, by contrast, requires little to no cloud infrastructure beyond the initial development phase. It moves computing resources to the network’s edge, allowing applications to operate with low latency and high bandwidth. A model might be trained in the cloud but deployed on an edge device, where it runs without (or mostly without) server infrastructure.

“The cloud has revolutionized everything from office productivity to entertainment-on-demand. Yet, when it comes to artificial intelligence, it has some serious limitations, especially for low-latency applications. Self-driving cars, for example, can’t afford to wait on remote servers for instructions that need split-second timing to avoid accidents,” according to Samsung NEXT Ventures managing director Brendon Kim.

The increasing growth of IoT is expected to drive the adoption of Edge AI software. Companies may use edge AI to deploy their machine learning models to run locally on edge devices, reducing performance and latency issues.

AI systems may provide real-time input to improve mission-critical applications and edge AI would most likely support industrial-heavy applications like manufacturing and supply chain in the short term. Edge AI may also be used to automate product testing and inspection, resulting in higher quality and lower costs.

Furthermore, edge AI software is becoming more common among businesses as it supports essential AI applications like autonomous vehicles and robotics. This in turn is expected to drive the growth of the Edge AI software market.

Another factor that is expected to boost the growth of the Edge AI software market is the usage of wearable devices, as they require computing on the go and cannot rely on remote cloud services.

Edge computing represents a powerful paradigm shift, but it has the potential to become even more useful when combined with AI. “Edge AI” describes architectures in which AI models are processed locally, on devices at the edge of the network. As edge AI setups typically only require a microprocessor and sensors, not an internet connection, they can process data and make predictions in real time (or close to it).

Kim said that with powerful new AI technologies designed to operate within all the phones, laptops, and appliances around us — all without having to connect to processors in the cloud – the business value of edge AI could be substantial.

According to Markets and Markets, the global edge AI software market is anticipated to grow from $590 million in 2020 to $1.83 billion by 2026. Deloitte estimates more than 750 million edge AI chips that perform tasks on-device have been sold to date, representing $2.6 billion in revenue.

Edge AI use cases

Edge AI can be used for surveillance and monitoring purposes, autonomous vehicles, smart speakers, and Industrial IoT. The pandemic has speeded up the adoption of edge computing. According to the Linux Foundation’s State of the Edge report, digital health care, manufacturing, and retail businesses are particularly likely to expand their use of edge computing by 2028. This is largely because of the technology’s ability to boost response times and save bandwidth while enabling less constrained data analysis.

For example, some factories use sensors mounted on motors and other equipment configured to stream signals — based on temperature, vibration, and current — to an edge AI platform. Instead of sending the data to the cloud, the AI analyzes the data continuously and locally to make predictions for when a particular motor is about to fail.

Another use case for edge AI and computer vision is automated optical inspection on manufacturing lines. In this case, assembled components are sent through an inspection station for automated visual analysis.

A computer vision model detects missing or misaligned parts and delivers results to a real-time dashboard showing inspection status. Because the data can flow back into the cloud for further analysis, the model can be regularly retrained to reduce false positives.

Establishing a virtuous cycle for retraining is an essential step in AI deployment. A clear majority of employees (87%) peg data quality issues as the reason their organizations failed to successfully implement AI and machine learning, according to a recent Alation report. And 34% of respondents to a 2021 Rackspace survey cited poor data quality as the reason for AI R&D failure.

Not without challenges

Edge AI offers advantages compared with cloud-based AI technologies, but it isn’t without challenges. Keeping data locally means more locations to protect, with increased physical access allowing for different kinds of cyberattacks.

Some experts argue the decentralized nature of edge computing leads to increased security.) Compute power is limited at the edge, which restricts the number of AI tasks that can be performed. And large complex models usually have to be simplified before they’re deployed to edge AI hardware, in some cases reducing their accuracy.

The future of edge AI is optimistic and the combined force is in fact inevitable. While only about 10% of enterprise-generated data is currently created and processed outside a traditional datacenter or cloud, that’s expected to increase to 75% by 2022, Gartner says. IoT devices alone are expected to create over 175 zettabytes of data in 2025. And according to Grand View Research, the global computing market is anticipated to be worth $61.14 billion by 2028. All this explains that despite several initial glitches, the future of AI is on the edge.

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Sohini Bagchi
Sohini Bagchi is Editor at CXOToday, a published author and a storyteller. She can be reached at sohini.bagchi@trivone.com