#ai-uplink-demand

#ai-uplink-demand

Williams Companies will break ground on the Northeast Supply Enhancement pipeline, the first new pipeline in New York in over a decade, expanding its Transco natural gas network.

In the fintech industry we can link latency directly to profit and money. If I have lower latency than the competition, I can get to the better deals, I can make the better deals.

The opportunity is to have more devices that are smart, adopting AI and [highlighting] the evolution of the original cloud AI with complementary support happening at the edge. We are able to grow the performance of platforms. Two things are converging: from one side, AI models are working with better performance [with a] reduced number of parameters, [and] at the edge, you can see more and more inference happening.

Edge computing is a type of IT infrastructure in which data is collected, stored, and processed near the "edge" or on the device itself instead of being transmitted to a centralized processor. Edge computing systems usually involve a network of devices, sensors, or machinery capable of data processing and interconnection. A main benefit of edge computing is its low latency. Since each endpoint processes information near the source, it can be easier to process data, respond to requests, and produce detailed analytics.

Ookla said the growing use of ChatGPT and other AI tools places much more demand on mobile networks than the typical activities of browsing social media and the web, watching videos, texting, and making the occasional phone call. As a result, more speed and expanded capabilities will be necessary. The report said advanced AI capabilities like AI-enabled glasses will put a particular strain on upload connections in the future.

AI and ML are critical for enabling autonomous, self-optimizing Wi-Fi networks capable of managing dense deployments and real-time performance demands. AI/ML reduces operational costs, improves reliability and security and delivers a more consistent quality of experience. Proprietary approaches, inconsistent data quality, and closed interfaces slow innovation and increase integration costs. Interoperable frameworks - not algorithms - will be key to success. Interoperability must include data models, telemetry, APIs, and model lifecycle management.

These AI tools drive profitability because they reduce outages, save energy, and obviate the need for manual intervention said Chetan Sharma, CEO of Chetan Sharma Consulting, who contributed to the report. The focus on AI-native networks and autonomous operations has overtaken customer service optimization as the leading use case for investment, according to findings from the firm's fourth annual State of AI in Telecommunications survey. The report also showed that AI has become profitable for 90% of telco operators.