The Future of Technology in Telecom

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Dashcam monitoring for police officers. Under normal circumstances, these cameras transmit a video stream of average quality. However, when an officer stops a potential violator, the AI ​​system allows the resolution to be automatically increased. This allows critical small details to be captured that could potentially help law enforcement in their investigations.

Technically, such projects can be implemented by placing inference engines near the cameras. This is the name of the AI ​​components that are responsible for making decisions based on input data. This will ensure minimal latency.

In the case of national parks, the AI ​​components are distributed across Europe and North America. Inference engines are pre-trained to recognize emergency situations. As soon as the AI ​​“sees” signs venezuela telegram of a fire in the video stream, a signal is sent to the management plane in the main data center in Europe.

After this, the parameters are reconfigured at the control level to ensure excellent transmission quality. The duty personnel also receive automatic notification.

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AI has great potential in the telecom industry. The implementation process is slowed down by the fact that not all operators see its benefits. Another problem is collecting data for training and implementing AI. Mobile networks generate huge amounts of information, but it is often poorly structured and distributed across different legacy systems.

Finally, telecom operators often lack competencies, which, incidentally, also applies to the implementation of 5G in general. Traditionally, they rely on one or two suppliers that provide vertically integrated turnkey solutions and service them themselves. The new paradigm requires broader competencies on the ground: from classic operator tasks to programming and DevOps.

The winners will be those operators who learn to manage data wisely, close skill gaps, and figure out how to monetize promisi