Edge computing enables devices to predict the future and make smarter decisions without taxing the cloud. Edge AI has many applications. Recent technological advances include facial recognition, self-driving vehicles, wearable medical devices, and real-time traffic updates accessible through smartphones. Supradip B., Founder and CEO of Next Move Strategy Consulting, discusses the endless possibilities of cutting-edge AI.
Companies see the interplay of edge, cloud and AI (artificial intelligence) as a possible solution to post-pandemic labor shortages, inflation, uncertainty and logistical challenges . AI is typically deployed in the cloud, where it processes a huge amount of data that is untimed and consumes massive computing resources. However, it does not only exist in the cloud. On the contrary, artificial intelligence at the edge provides data processing and facilitates decision-making locally, on devices such as smartphones, laptops, wearables, IoT, vehicles, etc. – reliably, faster and with greater security. This technology is the obvious choice for businesses in geographies with little or no internet connectivity. A recent report examines the key trade-offs and explains why the EdgeAI trend is here to stay.
It’s not just a matter of latency
There are over 20 billion phones and billions of other IoT devices, smart TVs, vehicles, computers, cameras and all connected devices that collect and process huge amounts of data. While these support numbers promise compelling benefits, they also expose new vulnerabilities. AI at the edge can process data from a device, passing a much smaller volume of data to the cloud for computation. Additionally, since the data is created and processed locally, it provides better security and privacy, keeping hackers at bay.
Real-time analytics, another important benefit facilitated by edge computing, is evident in many uses.case and is the main driver for increasing the adoption rate in many companies. This is possible because the data is processed, analyzed and stored locally on nearby hardware or server rather than being sent to a remote cloud. An edge gateway also reduces bandwidth. Since edge devices only transmit the amount of data relevant to the computation, cloud bandwidth is not overloaded.
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Improving the experience by bringing data and IT closer together
Although Edge AI is a relatively new technology, it is gaining traction in various industries. Industry 4.0, which has received a lot of attention lately, is transforming operations by using AI and analytics at different stages of production chains. Mounting intelligence at the edge will allow machines to make intelligent decisions, monitor component failures, and spot anomalies in the manufacturing process.
Edge computing is increasingly used in the healthcare sector. It enables autonomous monitoring of hospital rooms and patient conditions using computer vision and information from other sensors. Healthcare professionals can leverage artificial intelligence to detect cardiovascular abnormalities in imaging tests and spot bone dislocation, tissue damage, and fractures to make treatment choices or perform surgery.
This technology has proven to be a boon to the automotive industry. Today, automakers use massive amounts of data collected by all types of vehicles to identify and detect objects, improving passenger safety and comfort. It helps to avoid collisions with pedestrians or other vehicles and to detect roadblocks, which requires real-time data processing.
Technology innovation is driving new business outcomes in various industries, including smart forecasting in energy, future forecasting in manufacturing, and virtual assistant in retail. Autonomous shopping systems such as smart cart and smart payment system have enabled retailers to harness the power of integrated vision, improving the consumer experience. Additionally, major market players are being presented with lucrative opportunities owing to the growing adoption rate of video analytics solutions in the building and construction industries.
Software and hardware continue to Power Edge Computing
IoT and connected device companies are betting heavily on the potential of edge computing. To answer which is more important for powering Edge devices – software or hardware – the simple answer 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 allow users to access real-time data without having to connect to other systems or the Internet. AI algorithms are collected and processed locally, either on the device or on 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 ideal choice for use on advanced devices such as drones, surveillance cameras, robots and more.
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Huge investments continue to accelerate growth
Recent advanced computing patent applications demonstrate rapid innovation in Chinese industries. The rapid 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 cutting-edge AI hardware market.
Axelera AI BV, a Netherlands-based chip company, caught the eye when it revealed it had raised $27 million in a first round of funding. The 2021-founded company is developing a chip designed to run AI applications outside of data centers or at the network edge. Another company, Spot AI, recently made headlines for raising $40 million to build smarter surveillance camera technology.
Big companies such as Google, IBM and Amazon are investing heavily in the development of their Edge devices, so the only way to keep pace with the competition is to take the initiative and invest in technology.
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 computing, and the commercial maturity of neural networks. This enables companies to take advantage of the huge opportunity presented by integrating AI into their operations and act on real-time data, while improving security and privacy, reducing latency and reducing bandwidth and costs. Even though artificial intelligence is still in its infancy, its evolution and potential uses seem limitless.
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