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Theme: Accelerating Artificial Intelligence & Big Data Through Edge Computing
The global labour market is increasingly adopting new technology. New technology makes it easier for companies to automate routine tasks and could disrupt the balance between job responsibilities completed by humans and those completed by machines and algorithms.
Africa is rising and technology is at the forefront of our growth as a continent. We have seen the explosion of the mobile space in the continent and how it has allowed a number of services and solutions to become easier. Technologies like the Internet of Things (IoT), Robotics, Sensor Technology, 3D Printing, Blockchain and Artificial Intelligence have the potential to act as impact amplifiers and challenge traditional approaches to go about ‘age-old-problems’ in sectors such as agriculture, financial services, healthcare, education, water, and energy.
Institutions across several industries want to provide quality services and bring in more customers. The competition presented by each industry requires institutions to offer something unique, and what better way to do this than through the use of new technology. Application programming interface (API), blockchain, and artificial intelligence (AI) are changing the industry and unlocking the potential for tailored customer experiences, hyper-personalization, and back-end support for any business model.
Today, we’re witnessing the fastest pace of change the world has ever seen. The global economy is being transformed and this change can be daunting to all enterprises and industries.
Artificial Intelligence (AI) is the primary driver of the Fourth Industrial Revolution (4IR). And while it is not a new concept – for decades computers have been programmed by humans to make decisions on available facts. But what is different now, as technology advances every day, is an evolution in machine learning. Machines are currently developing what is known as “tactic knowledge”, which is essentially how the human mind works. And this will only improve with the explosion of data.
But the true potential of Artificial Intelligence and Big Data can fully be realized through Edge Computing. Thirty years ago, an organization’s intelligence would have been held in data centres. Today, we’re at a stage where some of that workload can be moved onto edge devices.
With processing carried out at, or very near to, the source of data (rather than in the cloud or in remote data centres), edge computing allows decisions to be made based on information generated by devices located in the places that matter most.
In the telecommunication sector, Mobile network operators use Edge Computing to bring processing power close to the network edge and reduce latency, which is especially important in enabling the speed and availability promised by 5G.
In the financial service sector, dealing with large volumes of data would normally be impossible to process and they would fail at improving their back-end office operations and customer experiences. As financial institutions transform their business models, there is an increased need to adopt distributed data models. Banks use edge computing as a way of deploying a more personalized customer experience.
For example, facial recognition technology or virtual tellers, that previously were impossible due to latency and speed issues, are now plausible developments. As a customer walks into a branch, an infrastructure that works close to the ‘edge’ could instantly provide relevant loan offers, recognising their face and delivering information to staff.
Before 2020, digital transformation in health care was frustratingly slow in the healthcare sector, even as providers dreamed of boosting efficiency, increasing flexibility, and reining in spiralling costs. In a risk-averse industry known for lagging behind technology trends, doctors still had not fully adopted electronic health records, for example. And the use of digital tools for diagnosis, tracking, and treatment was emerging but limited. Yet, healthcare data often need to be accessed by collaborative teams across institutions, sometimes around the globe. System downtime or slow app performance is not an option. Edge computing is reshaping health care by bringing big data processing and storage closer to the source, to support game-changing technologies such as the internet of things, artificial intelligence, and robotics. For example, The real-time feedback loop required for things like remote monitoring of a patient’s heart and respiratory metrics is only possible with something like Edge computing.
In the education sector, which has undergone extensive cyber transformations like using cloud computing and online classes, there is potential for greater efficiency with a fresh technological strand known as edge computing. The on-site or nearby data centre provides a specific focus to your school, allowing you to adapt network needs when necessary. For example, jammed network traffic is more likely to occur during school hours when students and educators are using the Internet facilities. Edge computing supports the expansion and contraction of your network requirements allowing for scalability, reliability, flexibility and security of a virtual classroom experience.
In the transportation sector, A driverless car needs countless sensors to operate and ensure the safety of passengers. With typical cloud computing, it takes too long for data to transfer between the source and the data centre.
Even a split second could result in an accident, making the speed and latency of data a critical element in the success of the technology. The ability to process data closer to the source is essential for the future of autonomous vehicles.
Learn more about Everything Big Data & Edge Computing here