The most honest thing you can say about violence is that nobody wants it, but the conditions that produce it are being engineered with extraordinary efficiency by people who have apparently never opened a history book.
The most honest thing you can say about violence is that nobody wants it, but the conditions that produce it are being engineered with extraordinary efficiency by people who have apparently never opened a history book.
Modern dense networks face a ton of unpredictable interference and variable traffic patterns. Wifi is a victim of it’s own success. It’s literally everywhere and thus all of these sources clobber the airwaves around them. This makes the traditional methods for traffic management and resource allocation of the airwaves too complex to fully implement.
However, your usage of LLM isn’t correct here. Wifi 7 doesn’t use a large language model, it uses what is called a Deep Reinforcement Learning (DRL) model. Wifi 7 isn’t trying to be generative, it’s being administrative. It’s looking at the airwaves as they are, and attempting to find an optimization for the current situation it is in.
In most cases the wifi coverage is not such that the NPU needs to step in. Traditional methods for transmission can be used, but in cases where you’re walking in a mall or in an apartment complex. You have tons of APs vying for the same resource. AI is used here to listen to what’s going on out in the world and come up with a method to target the highest bandwidth that can be achieved.
That’s not AI, that’s an algorithm.
Technically, yes, it’s an algorithm but all AI software is built out of algorithms. The critical difference is that traditional algorithms are fixed, static instructions written step-by-step by human engineers. Deep Reinforcement Learning (DRL) is a self-learning algorithm. Instead of a developer programming exactly how to handle every single wireless interference scenario, the DRL model acts like an AI agent. It continuously learns, adapts, and teaches itself the absolute best optimization paths purely through real-world trial and error.
Is this statement true?
“The Wi-Fi allocation algorithm under discussion is a variation of stochastic gradient descent which in practice is typically executed on an npu. It does not make use of a language model or general purpose transformer.”