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.
“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.”
We are saying the same thing, just at different layers of the tech stack. Your description of an SGD-driven model running on an NPU is the precise low-level math and hardware that allows the Deep Reinforcement Learning (DRL) agent to function.
What's happening here is that you're falling into a trap many people now mistakenly believe that if software doesn't generate text or use a Transformer, it isn't "real AI."
But that's only because LLM have become the dominating conversation piece of AI.
Saying 'that's not AI, that's an algorithm' is a fundamental misunderstanding of computer science. All AI is built out of algorithms. Neural networks, stochastic gradient descent, and transformers are all algorithms.
The line between 'traditional programming' and 'AI' is machine learning the ability of an algorithm to optimize its own internal weights based on environmental feedback rather than relying on hard-coded rules written by a human.
Would you say when Google's AlphaGo beat champions at Go that it wasn't AI? Because it didn't use language transformers either. By definition, a DRL agent that uses a Neural Processing Unit (NPU) to continuously calculate optimal radio frequencies via Stochastic Gradient Descent is text-book Machine Learning.
But the thing is I don't blame you for the confusion. Marketing hype leads many to this same trap of prerequisites for particular transformation to qualify as quote/unquote AI. But technically that just isn't true. I do enjoy this conversation we're having as it does highlight common misconceptions.
I learned about simulated annealing, gradient descent, perceptron feedback methods in the 1990s, when those were still being called AI.
I was the pm for multiple teams that used ML classifiers at a major Internet company to get serious work done, for a decade. Nobody called it AI.
From where I’m sitting, the frenzied use of the term AI on a grand scale for fundraising coincided pretty much exactly with general purpose transformers applied to language models (and to a lesser extent diffusion models).
I feel like calling all machine learning AI is confusing, because it confuses actually well-designed systems that do real stuff with an emperor-has-no-clothes bullshit mania.
It feels like maybe you’re trying to extend the AI halo to non-llm, non-gpt algorithms because you think it will improve the esteem in which the latter type of system is held.
I think the AI branding is a stain, I think there is going to be justified and ferocious backlash, and I would want to keep a perceptual moat between “AI” and whatever I’m building, even if at some point I do want to write code for an npu.
IHeartBadCode@fedia.io 1 month ago
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.
eestileib@lemmy.blahaj.zone 1 month ago
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.”
IHeartBadCode@fedia.io 1 month ago
We are saying the same thing, just at different layers of the tech stack. Your description of an SGD-driven model running on an NPU is the precise low-level math and hardware that allows the Deep Reinforcement Learning (DRL) agent to function.
What's happening here is that you're falling into a trap many people now mistakenly believe that if software doesn't generate text or use a Transformer, it isn't "real AI."
But that's only because LLM have become the dominating conversation piece of AI.
Saying 'that's not AI, that's an algorithm' is a fundamental misunderstanding of computer science. All AI is built out of algorithms. Neural networks, stochastic gradient descent, and transformers are all algorithms.
The line between 'traditional programming' and 'AI' is machine learning the ability of an algorithm to optimize its own internal weights based on environmental feedback rather than relying on hard-coded rules written by a human.
Would you say when Google's AlphaGo beat champions at Go that it wasn't AI? Because it didn't use language transformers either. By definition, a DRL agent that uses a Neural Processing Unit (NPU) to continuously calculate optimal radio frequencies via Stochastic Gradient Descent is text-book Machine Learning.
But the thing is I don't blame you for the confusion. Marketing hype leads many to this same trap of prerequisites for particular transformation to qualify as quote/unquote AI. But technically that just isn't true. I do enjoy this conversation we're having as it does highlight common misconceptions.
eestileib@lemmy.blahaj.zone 1 month ago
I learned about simulated annealing, gradient descent, perceptron feedback methods in the 1990s, when those were still being called AI.
I was the pm for multiple teams that used ML classifiers at a major Internet company to get serious work done, for a decade. Nobody called it AI.
From where I’m sitting, the frenzied use of the term AI on a grand scale for fundraising coincided pretty much exactly with general purpose transformers applied to language models (and to a lesser extent diffusion models).
I feel like calling all machine learning AI is confusing, because it confuses actually well-designed systems that do real stuff with an emperor-has-no-clothes bullshit mania.
It feels like maybe you’re trying to extend the AI halo to non-llm, non-gpt algorithms because you think it will improve the esteem in which the latter type of system is held.
I think the AI branding is a stain, I think there is going to be justified and ferocious backlash, and I would want to keep a perceptual moat between “AI” and whatever I’m building, even if at some point I do want to write code for an npu.