Comment by nozzlegear
1 day ago
I don't think Machine Learning falls under what most people consider "AI" and "LLM" these days, even if they're technically intertwined.
1 day ago
I don't think Machine Learning falls under what most people consider "AI" and "LLM" these days, even if they're technically intertwined.
How is LLM (a particular area of machine learning) not machine learning? Have people already forgotten the basis for LLMs?
The majority of people who use LLMs today never even heard of ML though a non-trivial percentage have heard that modern AI is powered by LLM. You can’t forget what you never knew. Such is the evolution of language when a formerly niche technical concept crosses the chasm to mass awareness.
I'd argue there's a qualitative difference between using machine learning for specific data analysis tasks, and using a generic agentic AI system controlled by some corporate entity. The association of the term 'AI' with the latter is increasing.
Yes, but nozzlegear claims that even technically "intertwined" (presumably they mean "inclined") people don't know the connection between LLMs and the broader ML work that encompasses it. That's a pretty big claim, and would be rather shocking if true. ML and deep learning were heavily invested in and discussed through the 2010s (and earlier, but the hardware developments at the end of the 2000s enabled the ML boom of the 2010s), is our industry really so memory constrained (I know there's a shortage now, but still) that people don't know the connection between machine learning and LLMs?
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Machine learning used to be used as a buzzword alongside AI, though nowadays after the release of ChatGPT it seems they've settled on AI.
Machine learning used to be considered a subset of AI. AI encompassed any algorithms that exhibited "intelligence" (e.g. a chess engine), while machine learning was scoped to algorithms that required training (e.g. a neural network).
In this particular case it means the Nx ecosystem, which is a solid Numpy alternative.