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Edge AI Opens Endlessness Possibilities for Next-Gen AI | – Spiceworks News and Insights

Edge computing enables devices to predict the future and make wiser decisions without taxing the cloud. Edge AI has numerous applications. Recent technological advancements include facial recognition, autonomous vehicles, wearable medical devices, and real-time traffic updates that can be accessed via smartphones. Supradip B., founder and CEO at Next Move Strategy Consulting, discusses the endless possibilities that come with edge AI.

Businesses are considering the interplay of edge, cloud, and AI (Artificial Intelligence) as a possible solution to the post-pandemic workforce shortages, inflation, uncertainty, and logistical problems. AI is typically deployed in the cloud, where it processes a huge amount of data that’s not time driven and consumes massive computing resources. However, it does not exist solely in the cloud. On the contrary, AI at the Edge provisions data crunching and facilitates decisions to be made locally, on devices like smartphones, laptops, wearables, IoT, vehicles etc. – reliably, faster and with greater security. This technology is the clear choice of companies having a presence in geographies with little to zero internet connectivity. A recent report examines the prominent tradeoffs and explains why the EdgeAI trend is here to stay.

It’s Not Only About Latency 

There are over 20 billion phones and billions more IoT devices, smart TVs, vehicles, computers, cameras and all connected devices collecting and processing massive amounts of data. While these bolstering numbers promise irrefutable advantages, it also exposes fresh vulnerabilities. AI on the edge can process data from a device, transmitting a much smaller volume of data to the cloud for computation. In addition, as the data is created and processed locally, it provides better security and privacy, keeping hackers at bay. 

Real-time analytics, another significant benefit facilitated by edge computing, is evident in many usencases and is the primary driver of the rising adoption rate among many businesses. This is possible because the data is processed, analyzed, and stored locally on hardware or a nearby server rather than being sent to a remote cloud. An edge gateway also reduces the bandwidth. As the edge devices transmit only the relevant volume of data for computing, the cloud’s bandwidth is not overburdened. 

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Enhancing Experience by Bringing Data and Computing Closer

Though Edge AI is relatively a new technology, its gaining traction in various business verticals. Industry 4.0, which has received a lot of attention lately, is transforming operations by utilizing AI and analytics at various stages in production lines. Mounting intelligence on the edge will equip machines to make smart decisions, monitor component failure, and spot anomalies in the manufacturing process. 

Edge computing is becoming more widely used in the healthcare sector. It enables autonomous monitoring of hospital rooms and patients’ conditions by using computer vision and information from other sensors. Healthcare professionals can leverage artificial intelligence to detect cardiovascular abnormalities in imaging tests and spot dislocation of bones, tissue damage and fractures to make treatment choices or perform surgery. 

This technology has proved a boon for the automotive industry. Today, automakers are using massive amounts of data gathered by all types of vehicles to identify and detect objects, enhancing passenger safety and comfort. It aids in avoiding collision with pedestrians or other vehicles and detecting roadblocks, which requires real-time data processing.

Technology innovation is driving new business outcomes in various sectors, including smart forecasting in energy, future prediction in manufacturing and virtual assistant in retail. Autonomous shopping systems such as the smart trolley and smart checkout system have enabled retailers to tap the power of embedded vision, thereby improving the consumer experience. Furthermore, key market players are being presented with lucrative opportunities as a result of the increasing adoption rate of video analysis solutions in the building and construction industries.

Software and Hardware Continue to Power Edge Computing 

IoT and connected device companies are banking heavily on the potential of Edge computing. To answer what is more important for powering Edge devices – software or hardware – the simple response is both. Edge AI software refers to Edge AI applications or algorithms that run on hardware devices such as robots, sensors, smart speakers, processors, wearables and others.

These algorithms make it possible for users to access real-time data without requiring them to connect to other systems or the internet. AI algorithms are collected and processed locally, either on the device or the server, thus equipping the device to make decisions, correct problems and make predictions without human intervention. A type of specialized artificial intelligence hardware called an AI accelerator is designed to accelerate data-intensive deep learning inference, making it the perfect choice for use on edge devices such as drones, surveillance cameras, robots and more. 

See More: How to Build Validated Patterns for Continuous AI Deployment at the Edge

Huge Investments Keep Accelerating Growth 

Recent edge computing patent applications demonstrate rapid innovation in China’s industries. The quick adoption of 5G and the pursuit of smart grids have propelled this innovation in the region. Many Chinese AI processor startups are raising capital to enter the edge AI hardware market.

Axelera AI B.V., a chip company, based in the Netherlands, grabbed attention when it revealed that it had raised $27 million in an early-stage funding round. The 2021-founded business is developing a chip that is designed to run AI applications outside of data centers or at the network’s edge. Another company, Spot AI, recently made headlines for raising $40 M to build a smarter surveillance camera tech. 

Large companies such as Google, IBM and Amazon are investing generously in the development of their Edge devices, so the only way to ace the competitive pace is to take the initiative and invest in technologies.     

This Is Just the Beginning 

A strong infrastructure for machine learning has been influenced by favorable factors such as the expansion of IoT devices, 5G, improvements in parallel computation, and the commercial maturity of neural networks. This is enabling businesses to take advantage of the enormous opportunity presented by integrating AI into their operations and acting on real-time data, all while enhancing security and privacy, reducing latency and lowering bandwidth and costs. Even though edge AI is still in its infancy, its evolving and the potential uses seem limitless.

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