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Shaping the future with artificial intelligence

The next generation of networks will be able to sense, compute, learn, reason, and act on business intent almost autonomously, and to manage the ongoing explosion of data from an ever-increasing number of connected intelligent devices. Artificial intelligence (AI) plays a key role in enabling automation, managing complexity and scalability, and leveraging on data from distributed systems in real time.

Instead of waiting for next-generation 5G network deployments to invest in AI, the telcos are already deploying AI on 4G networks now, the infrastructure most of their customers still use. With AI-based network monitoring, they can detect network issues up to 80 percent faster and reduce incident costs by as much as 70 percent.

At the same time, the service providers want to use AI better to monitor their networks as a whole, and measure the quality of service they provide – rather than just monitoring network-related KPIs and minimizing network downtime.

With AI, apart from telecom network maintenance and optimizing network performance, security is a big concern in the telecom industry. Telecom service providers are using new and unique techniques, and many of these techniques include AI at their base.

AI can be used to authenticate users and also provide security to towers. When users sign up for a new connection, the chances of fraud are the highest. Telecom companies are using AI to authenticate new users.

Towers can be secured by using preventive AI technologies. These models are trained to look for defects in the towers every now and then. Such towers can be found by constantly monitoring and reporting if even a slight change is found in the tower’s characteristics.

End-user data protection is important because today, hackers are more active than ever before. Moreover, hackers are targeting places like telephone company databases where they can get a lot of personally identifiable data easily.

Many telecom service providers are already using cujo.AI’s network security solutions. Companies like Verizon, AT&T, and Charter Communications in the US rely on AI services from to secure their networks.

When AI is leveraged, the need for better standards increases. Hence, many telecom service providers use end-to-end encryption and other newly created security protocols and encryption standards. With suitable security systems, the data is fully secure and free from any interference.

However, most CSPs lack the specific tools that integrate with AI, existing data, and tools to perform more effective, holistic network monitoring. Service providers are looking for solutions that deliver a short time to value with easy integration and open APIs. They are also seeking solutions that are easy for teams to use and support without requiring significant investment in data science talent and professional services.

The combination of billions of connected devices, petascale computing, and advanced communication capabilities that enable real-time interactions is leading to the creation of systems on a scale and level of complexity that is beyond the ability of humans to fully comprehend and control. The management and operation of these systems require an extremely high degree of intelligent automation.

Machine learning, machine reasoning, and other technologies from the field of AI offer the best opportunity to achieve the high levels of automation necessary to manage the complexity and optimization of system performance. Some of this work has already begun as we can see with initiatives to support AI in standards-development organizations, such as the 3rd Generation Partnership Project (3GPP) and the Open Radio Access Network (O-RAN) Alliance.

A mobile network is a highly distributed and decentralized system. As shown in the figure, AI capabilities are added in the network architecture to enable data processing for various purposes, both locally (close to where data is created) and centrally (where data can be consolidated). Local learning and decision-making take place at distributed sites while data and knowledge are blended across sites for a comprehensive global understanding of networks, services, and functions.

However, to fully capitalize on the potential of AI, Ericsson has identified five technical challenges that must be addressed. The technical challenges involve systems that can make decisions autonomously, distributed and decentralized intelligence, trustworthy AI, human-machine collaboration, and bringing games and simulations to industrial scale.

These challenges are best addressed through approaches based on strong domain knowledge, including a deep understanding of the underlying connectivity/communication aspects. The telecom equipment manufacturers and the larger research and development community will need to address these challenges and finding solutions.

The European Union is the first global institution to outline draft rules on regulating AI. It is expected that it will establish the right balance between providing safety and encouraging innovation within the EU. The AI regulation will test the relationship between the law and AI. This raises questions as to whether the law will be able to move fast enough to effectively regulate constantly evolving AI. Either way, businesses will need to remain abreast of the changing legal landscape to avoid infringing the law and incurring significant fines.

AI will be a clear differentiator in the year ahead. The sudden lockdown and shifting nearly four million staff in a work-at-home mannequin was by no means dreamt of in any enterprise continuity plan, whereas making certain world prospects and their staff might handle an identical transition.

The shift from RFPs to consultative promoting, in-person conferences to digital gross sales, from engaged on outlined tasks to defining the scope of what is potential – it was all about reimagination and staying forward. It additionally dropped at the fore that the distant work mannequin presents immense alternatives for India to faucet numerous expertise swimming pools.

Whereas massive firms managed this transition properly, it was equally heartening that startups and MSMEs have been fast to pivot their choices, construct extra-resilient operations, and are poised to revert to a pre-COVID stage in 2021.

And as adoption of these technologies continues apace, enterprises will be drawing on lessons learned over the past-year-and-a-half that will guide their efforts well into the decade ahead. Business leaders understand firsthand the power and potential of analytics and AI on their businesses. CEOs are discovering that AI is instrumental in alleviating skills shortages, boosting productivity, delivering new products and services, accentuating corporate values, addressing supply chain issues, and fueling startups, while helping companies manage disruption.

The coming boom in business growth and innovation will be a data-driven one. As the world eventually emerges from the other side of the COVID crisis, there will be opportunities for entrepreneurs, business leaders, and innovators to build value and launch new ventures that can be rapidly re-configured and re-aligned as the customer needs change. Next-generation technologies – artificial intelligence and analytics – will play a key role in boosting business innovation and advancement in this environment, as well as spur new business models.

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