On the Edge - How Edge AI is reshaping the future

As computers have become more sophisticated over the years, we have passed through several ‘eras’ of computing. From the ‘personal computing era’, in which we kept customized sets of apps and data locally on our devices, to that of ‘cloud computing’, which is typified by people making use of centralized online services hosted on ‘the cloud’. Now we are moving into the world of ‘edge computing’, in which data is processed close to its source, cutting out the need for it to be sent to the cloud. But computing isn’t the only thing taking place on ‘the edge’ – now, AI is being brought to the source of the data as well, allowing ‘Edge AI’ to bring about new standards of speed and intelligence. So, what is Edge AI, what kinds of benefits will it offer, and how will it empower solutions going forward? What is ‘Edge AI’? Currently, the heavy computing capacity required to run deep learning models necessitates that the majority of AI processes be carried out in the cloud. However, running AI in the cloud has its disadvantages, including the fact that it requires an internet connection, and that performance can be impacted by bandwidth and latency limitations. Edge AI, also known as ‘on-device AI’, is a distributed computing paradigm that allows for AI algorithms to be run locally on a device, using the data produced by that device. Running AI at the ‘edge’ of the local network removes the requirement for the device to be connected to the internet or centralized servers like the cloud. The Benefits of Edge AI Edge AI offers significant improvements as far as response speeds and data security. Executing AI close to the data source allows for processes like data creation and decision-making to take place in milliseconds, making Edge AI ideal for applications where near-instantaneous responses are essential. Although cloud computing has its advantages, uploading personal data to a centralized forum naturally comes with a host of privacy concerns. Edge AI addresses this problem by processing data locally and offers additional reassurances to AI-driven industries in which data integrity is imperative. Utilization of Edge AI also contributes to improved user experience and cost reductions. Edge AI drastically reduces latency concerns so that users can engage in real-time by doing things like using wearables to make payments and having smart speakers track exercise and sleep patterns. As far as cost reductions, Edge AI lowers the amount of money users must spend on bandwidth, and removes cloud processing costs. How Edge AI will be Applied The list of applications for Edge AI is a long one. Current examples include face recognition and live traffic updates on smartphones, as well as semi-autonomous vehicles and smart refrigerators. Other Edge AI-enabled devices include smart speakers, robots, drones, security cameras and wearable health monitoring devices. Some more areas where Edge AI is expected to be utilized further are outlined below: • Self-driving vehicles: In few areas are near-instantaneous response times more important than in that of autonomous driving. For self-driving vehicles to be able to respond to developments on the road in real-time while reacting swiftly and appropriately to traffic signs, pedestrians and other vehicles, the ability to process data very rapidly is imperative. • Surveillance and monitoring: In the past, security cameras would transmit unaltered video signals directly to the cloud, resulting in heavy bandwidth use and overburdened servers. Now, machine learning built into cameras can monitor activity, and only transmit events of note to the cloud. • Industry IoT: In industries that utilize automation, Edge AI will improve safety and reduce costs. Locally performed AI will monitor machinery for potential defects and respond to them in real-time, with local deep learning able to contribute to data collection too. • Image and audio analytics: The image and audio analytics-related applications of Edge AI are vast. From real-time image and scene recognition to devices responding to audio triggers, there is a wide range of latency-critical applications that Edge AI can be applied to. Where Will Edge AI Take Us? A 2018 study estimated that the edge computing industry would be worth approximately USD 3.24 billion by 2025. While Edge AI is expected to boost demand for IoT devices, and help facilitate the inception of 5G networks, its inception will additionally see a whole new standard of real-time, smart performance put into users’ hands. As industries increasingly make their systems smarter and more efficient, ‘The Edge’ could see businesses improve and diversify in never-before-seen ways too. AI on the edge is primed to improve standards across the board. Be those standards about privacy, speed or accessibility, bringing AI to the data source is expected to open a whole new world of intelligence and convenience for users and businesses alike.