AI introduction for telecom

- [Instructor] What if your telecom network not just route traffic, but can predict failures, optimize itself, and can also protect you against cybersecurity threats before they even strike? Welcome to the course. Telecom AI refers to the AI technologies and solutions, which are developed to support the evolving needs of communication service providers. Let's take a look at the introduction. In telecom, AI is used to automate network management and much more. It helps telecom operators to manage complex networks efficiently and cost effectively. You know, the key benefit includes faster troubleshooting. Yes, that's right. AI can detect and fixes issues quickly. It can help in network performance, optimizes the bandwidth, and reduces congestion. It can also lower cost, like reducing the manual work and operational expenses. But why do we need AI in telecom? AI addresses some of the complex problems such as automating network operations by zero touch provisioning and self-healing networks. It also can enhance the predictive maintenance, improve customer experience, as well as strengthening the security and optimizing resources. Let's take a look at some of the key AI applications in telecom. It all starts from automating network operations. You can optimize the network operations, do self-healing. Yes, the network can detect the failure itself and also it can help in enhancing predictive maintenance, which can result in improving customer experience. Like you can have AI chatbots and can ask, "Hey, why my quality of service is down?" And it can give you the results as well and take actions on top of it, which can strengthen the security and optimize the resources for you. Let's take a look at some of the key AI technologies. For telecom. We have machine learning. It can help pattern recognition for automation and analytics. We also have deep learning like a neural networks for anomaly detection and protections. You also have natural language processing like chatbots and AI-driven tech support and intelligent automation. We have computer vision and gen AI, which can take intelligent decision and give real time insights with LLM, the large language models, and we can tie it up with RAG, which is retrieval augmented generation to give you better insights. Let's take a look at the impact of Nokia's AI deal with AT&T. There are two main benefits that the AT&T is getting. Firstly, the network capabilities is enhanced by the integration of AI and machine learning. We have also seen operational automation is increased where we can see operations to be streamlined, which has reduced manual intervention and has improved efficiency a lot. There is also a case study of T-Mobile's collaboration with OpenAI for AI-driven customer service. T-Mobile has partnered with OpenAI to develop an AI platform named IntentCX. This platform utilizes customer data to automate service, task, and analyze past interactions to resolve issues proactively, reducing customer churn. In the next session, I'll give you some more insights in which you can visualize how the AI is helping telecom industry.