7 thoughts on “Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search”

1. Artificial intelligence is the future for sure. There are so many trends that are being introduced in the AI industry. Thank you very much. This is going to be so helpful for everyone to know about it.

2. From ChatGPT:
   Generative AI, also known as generative modeling, refers to a subset of artificial intelligence (AI) techniques that involve generating new content, such as images, text, or even music, based on patterns and data it has learned from. Generative models are designed to learn the underlying distribution of the training data and then use that knowledge to generate new, similar examples.

   One popular approach to generative AI is generative adversarial networks (GANs). GANs consist of two components: a generator and a discriminator. The generator learns to create new samples, while the discriminator tries to distinguish between real and generated samples. Through an iterative process, the generator and discriminator improve their abilities, leading to the generation of increasingly realistic and high-quality content.

   Another approach is the use of autoencoders, which are neural networks designed to encode input data into a lower-dimensional representation and then decode it back to its original form. By training an autoencoder on a specific dataset, it can learn to generate new examples that resemble the input data.

   Generative AI has found applications in various domains, including image synthesis, text generation, music composition, and even video generation. It has both practical and creative implications, offering opportunities for generating new content, data augmentation, simulation, and more. However, it is important to note that generative AI models also raise ethical concerns, such as potential misuse and generating misleading or harmful content.

3. ChatGPT and Google Search serve different purposes and have different strengths.

   ChatGPT, like other AI language models, is designed to understand and generate human-like text based on the input it receives. It can provide information, answer questions, and engage in conversation. It relies on the data it has been trained on, which includes a wide range of sources, but its responses are not always guaranteed to be accurate or up-to-date.

   Google Search, on the other hand, is a search engine that aims to provide relevant and up-to-date information from across the web. It uses sophisticated algorithms to index and rank web pages, taking into account various factors such as popularity, relevance, and credibility.

   In terms of accuracy, Google Search generally provides a broader and more diverse range of information from multiple sources, while ChatGPT may not have access to the same breadth of real-time information. However, ChatGPT can still be useful for answering questions, providing explanations, or engaging in conversation based on its training data.

   It’s important to note that both ChatGPT and Google Search can sometimes provide incorrect or outdated information, and it’s always a good idea to verify information from multiple reliable sources when accuracy is critical.

4. This blog post explains artificial intelligence and its practical applications in simple terms. It’s an excellent primer for those who are unfamiliar with the subject. The author did an excellent job of distilling down complicated issues into simple terms. The use of real-world examples helps to demonstrate AI’s capability and promise. Congratulations on developing an interesting and easily accessible resource!

   ChatGPT Prompts

5. “Generative artificial intelligence” is set to add up to $4.4 trillion of value to the global economy annually, according to a report from McKinsey Global Institute, in what is one of the rosier predictions about the economic effects of the rapidly evolving technology.

   Generative A.I., which includes chatbots such as ChatGPT that can generate text in response to prompts, can potentially boost productivity by saving 60 to 70 percent of workers’ time through automation of their work, according to the 68-page report, which was published early Wednesday. Half of all work will be automated between 2030 and 2060, the report said.

   McKinsey had previously predicted that A.I. would automate half of all work between 2035 and 2075, but the power of generative A.I. tools — which exploded onto the tech scene late last year — accelerated the company’s forecast.

   “Generative A.I. has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities,” the report said.

   McKinsey’s report is one of the few so far to quantify the long-term impact of generative A.I. on the economy. The report arrives as Silicon Valley has been gripped by a fervor over generative A.I. tools like ChatGPT and Google’s Bard, with tech companies and venture capitalists investing billions of dollars in the technology.

   https://www.nytimes.com/2023/06/14/technology/generative-ai-global-economy.html

6. Generative artificial intelligence (AI) is sweeping across the tech landscape at an unprecedented rate, upending operational models despite most still not fully grasping what it is or what it can do. That uncertainty is especially challenging for the telecommunications space that has a history of reticence when it comes to change and is perhaps facing the most pressure to adopt generative AI-supported change.

   Consulting firm Bain & Co. highlighted that inertia in a recent report, where a trio of its partners stated operators need to act fast in order to jump on this opportunity.

   “Speedy action trumps perfect planning here,” Herbert Blum, Jeff Katzin and Velu Sinha wrote in the brief. “It’s more important for telcos to quickly launch an initial set of generative AI applications that fit the company’s strategy, and do so in a responsible way – or risk missing a window of opportunity in this fast-evolving sector.”

   McKinsey & Co. in a different report explained that AI has highlighted business leader priorities. The consulting firm cited organizations that have top executives championing an organization’s AI initiatives, including the need to fund those programs. This is counter to organizations that lack a clear directive on their AI plans, which results in wasted spending and stalled development.

   “Reaching this state of AI maturity is no easy task, but it is certainly within the reach of telcos,” the firm noted. “Indeed, with all the pressures they face, embracing large-scale deployment of AI and transitioning to being AI-native organizations could be key to driving growth and renewal. Telcos that are starting to recognize this is non-negotiable are scaling AI investments as the business impact generated by the technology materializes.”

   How telecom operators can embrace generative AI:

   Ishwar Parulkar, chief technologist for the telco industry at AWS, touted several areas that should be of generative AI interest to telecom operators.

   The first few were common ones tied to improving the customer experience. This includes building on machine learning (ML) to help improve that interaction and potentially reduce customer churn.

   “We have worked with some leading customers and implemented this in production where they can take customer voice calls, translate that to text, do sentiment analysis on it … and then feed that into reducing customer churn,” Parulkar said. “That goes up another notch with generative AI, where you can have chat bots and more interactive types of interfaces for customers as well as for customer care agent systems in a call. So that just goes up another notch of generative AI.”

   The next step is using generative AI to help operators bolster their business operations and systems. This is for things like revenue assurance and finding revenue leakage, items that Parulkar noted were in a “more established space in terms of what machine learning can do.”

   However, Parulkar said the bigger opportunity is around helping operators better design and manage network operations. This is an area that remains the most immature, but one that Parulkar is “most excited about.”

   This can begin from the planning and installation phase, with an example of helping technicians when they are installing physical equipment.

   “In installation of network equipment today, you have technicians who go through manuals and have procedures to install routers and base stations and connect links and fibers,” Parulkar said. “That all can be now made interactive [using] chat bot, natural language kind of framework. You can have a lot of this documentation, training data that can train foundational models that can create that type of an interface, improves productivity, makes it easier to target specific problems very quickly in terms of what you want to deploy.”

   This can also help with network configuration by using large datasets to help automatically generate configurations. This could include the ability to help configure routers, VPNs and MPLS circuits to support network performance.

   The final area of support could be in the running of those networks once they are deployed. Parulkar cited functions like troubleshooting failures that can be supported by a generative AI model.

   “There are recipes that operators go through to troubleshoot and triage failure,” Parulkar said “A lot of times it’s trial-and-error method that can be significantly improved in a more interactive, natural language, prompt-based system that guides you through troubleshooting and operating the network.”

   This model could be especially compelling for operators as they integrate more routers to support disaggregated 5G network models for mobile edge computing (MEC), private networks and the use of millimeter-wave (mmWave) spectrum bands.

   Federal Communications Commission (FCC) Chairwoman Jessica Rosenworcel this week also hinted at the ability for AI to help manage spectrum resources.

   “For decades we have licensed large slices of our airwaves and come up with unlicensed policies for joint use in others,” Rosenworcel said during a speech at this week’s FCC and National Science Foundation Joint Workshop. “But this scheme is not truly dynamic. And as demands on our airwaves grow – as we move from a world of mobile phones to billions of devices in the internet of things – we can take newfound cognitive abilities and teach our wireless devices to manage transmissions on their own. Smarter radios using AI can work with each other without a central authority dictating the best of use of spectrum in every environment. If that sounds far off, it’s not. Consider that a large wireless provider’s network can generate several million performance measurements every minute. And consider the insights that machine learning can provide to better understand network usage and support greater spectrum efficiency.”

   While generative AI does have potential, Parulkar also left open the door for what he termed “traditional AI” and which he described as “supervised and unsupervised learning.”

   “Those techniques still work for a lot of the parts in the network and we see a combination of these two,” Parulkar said. “For example, you might use anomaly detection for getting some insights into the things to look at and then followed by a generative AI system that will then give an output in a very interactive format and we see that in some of the use cases as well. I think this is a big area for telcos to explore and we’re having active conversations with multiple telcos and network vendors.”

   AWS has been busy with generative AI
   Parulkar’s comments come as AWS has been busy updating its generative AI platforms. One of the most recent was the launch of its $100 million Generative AI Innovation Center, which is targeted at helping guide businesses through the process of developing, building and deploying generative AI tools.

   “Generative AI is one of those technological shifts that we are in the early stages of that will impact all organizations across the globe in some form of fashion,” Sri Elaprolu, senior leader of generative AI at AWS, told SDxCentral. “We have the goal of helping as many customers as we can, and as we need to, in accelerating their journey with generative AI.”

   https://www.sdxcentral.com/articles/analysis/aws-sees-a-role-for-generative-ai-in-the-telecom-space/2023/07/

7. AI plays a vital role in optimizing our transport networks. From predicting disruptions in fiber-optic cables to optimizing spectrum allocation in wireless backhaul systems, AI enhances efficiency and resilience.

   Fiber-optic cables, critical for data transmission, benefit from AI’s predictive capabilities. By analyzing data patterns, AI can anticipate potential issues and reroute traffic, ensuring uninterrupted service and proactive maintenance.

   AI optimizes spectrum allocation and enhances edge intelligence. Through advanced algorithms, it allocates frequencies efficiently and predicts user behavior at the network edge, ensuring robust connectivity in all conditions.
   The collaboration between AI and open architecture is essential for seamless data flow and interoperability across the network. Open standards enable AI algorithms to access diverse data sources, fostering collaboration and innovation.

   As we move towards 5G and beyond, scalability and flexibility are crucial. AI-powered transport networks demonstrate adaptability, accommodating evolving demands and optimizing performance for a superior user experience.

   What are other technical aspects of AI integration in transport networks and advancing towards a more intelligent, resilient, and user-centric communication ecosystem we should consider?

Comments are closed.