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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.

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eestileib@lemmy.blahaj.zone ⁨2⁩ ⁨weeks⁩ 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.

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- IHeartBadCode@fedia.io ⁨2⁩ ⁨weeks⁩ ago
  Then we are completely on the same page! I actually mentioned earlier in our thread that I hate the notion of calling these specific, highly useful operations 'AI' for exactly the reasons you just laid out.
  
  My previous replies were just looking at it through a strict, literal computer science taxonomy lens. But my original comment literally points out the very things you bring up.
  
  We're fighting an uphill battle with the vast marketing hype and it's good that you can understand the nuanced difference between all the things that have gotten rebranded AI. My point isn't to try and use past technology as a way to validate what the techbros are doing but to highlight how muddy the waters have become.
  
  I closed out my original comment with a hope that perhaps the more utility based work that's had solid proof of usefulness doesn't get tossed out with the bathwater so to say.
  
  Again, I'm not apologetic of what has come from that hard work, more lamenting that everything has become the fuzzy mess it is.
  
  By all means, head back to my original comment and let me know if there's any part where I've come off indicating embracing this thing that I would say you and I agree with.
  
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