Couldn’t agree more. I feel this is somewhat what modern discourse is like. Nobody is concerned with concepts. Or what it even is they’re “against” or “for”. We rather run on emotionalism. Fight words or people instead. That’s way easier than tackle the real issue. It’s somewhat the same in politics.
I feel most online discussions are more about identity than anything else. People want me to subscribe to their group identity or I’ll get downvotes on social media. Nobody is really concerned about the war we’re currently talking about and the people living there. Or what AI factually is and what the issue is with those ridiculously large and predatory companies and their effect on people, the environment and basically everything… Which would be the real deal for me. I don’t really care if you think text to speech and a thermostat is the same as ChatGPT. It’s not. But that’s not really why someone would form an identity around being pro- or contra AI in my eyes?!

Machine Learning had been used for decades by cops to harm minorities. We need to oppose both being used when they could harm minorities

Machines should not be allowed to learn, it will only lead to evil.

I’m fine with machine learning, neutral networks, and even specialize tools for industries to use “AI”.

I’m just not fine with it replacing human culture (art, music, memes ect), and misinforming people on basic topics.

My dude, there’s this feature of language that is escaping you. That feature is: it changes over time.

Technically speaking any kind of hardware assist to human thought is “AI”. The term started getting used 1956 (McCarthy) and was explicitly a term created as, essentially, marketing: a fresh, ambitious label that would attract funding and academic attention.

Over the years the term “AI” has been applied to a bewildering array of technologies that aren’t called AI anymore because, get this, once a given AI technology has found a niche that it works in, they stop calling it AI. It’s just “software” at that point.

So the term “AI” is a moving target that basically slides on the leading edge of a string of kinda/sorta related technologies, while the parts of it that worked over the decades are given names like “handwriting recognition” or “photographic enhancement” or even “chess playing engine”.

Today “AI”, outside of very specific professional circumstances (jargon, in other words) means degenerative AI. You can rail against this if you like, but you’re just in “old man shakes fist at clouds” territory if you do. You’re basically in the same space as those who insist that people shouldn’t split infinitives; you’ve been bypassed by language.

Agreed it’s important to seperate. It is hard explaining to people though, they still just call it AI in general

One of the best AIs I have is in my thermostat for floor heating. Adaptive heating changes the time it should start the heating so the floor will be at the target temperature when a temperature schedule starts.

Cant believe someone would say this about Marxism Leninism /s

The AI problem includes recommendation models used by social media corporations, search engines and eCommerce sites. The social media ones have been very dangerous.

The surveillance models are also relevant.

I have a tragically nuanced opinion that the technology is neat it’s just being used by evil men for evil things. A small locally hosted llm trained with legally obtained data would be all right.

Don’t get me wrong sew Aultman into a canvas sack full of rats.

We should differentiate between Generative AI (LLM, Video and image Generation) and those old school, valid and useful “AI” systems.

A bit of clarification for people who are OotL (simplified from a few courses on ML, but maybe helpful):

The reason generative AI has exploded is because of the discovery of the transformer architecture in 2017 which itself was an innovation on deep learning. Transformers are special because they enable extreme parallelization of tasks that used to be heavily serialized (namely ones that needed recurrent neural networks (RNNs) or their more advanced variants like LSTMs).

We aren’t starting to mix this up with “classical machine learning”, because classical ML isn’t about the task; it’s about how you’re performing it. ICR, voice recognition, etc. in 2026 are largely done using transformer models. Even those ML tasks that aren’t using transformers (which do have their own downsides) are usually still using a deep learning model (for example, KDE’s Spectacle screenshot tool has OCR, and I’m pretty sure they went with an LSTM model). And it’s also why separating it into “GPU or no GPU” is a wild oversimplification (classical ML models, such as SVMs, can also take advantage of parallelization in training).

Classical ML (supervised and unsupervised) are techniques and architectures like SVM, k-means, decision trees, etc. Deep learning incorporates these classical ML concepts (for example, the most basic type of deep learning model, a deep neural net (DNN), is also a “multilayer perceptron” – where a single-layer perceptron is a bog-standard linear classifier and the granddaddy of ML models). But still, “classical ML” in the field is “machine learning that isn’t deep learning”.

If you want to learn how these models work and don’t want to be smacked in the face with textbook-style math, the YouTube channel StatQuest gets a lot of students through their ML courses. And if you want to try hands-on, there’s an extremely user-friendly Python library called scikit-learn that’ll be more beginner-friendly than e.g. PyTorch or TensorFlow.

I hate subjectivity without a subject. I hate the effect on the environment. I hate that AI is used to reinforce the current economic reality.

To expand on your post:

AI is an umbrella term for three different levels of intelligence:

Weak AI or ANI (Artificial Narrow Intelligence): This is where we currently are, and where we actually already have been for decades. It’s a system that has specialized knowledge in some narrow domain. That chess program you played against 20 years ago, your cars built in driving security systems, your assistant on your phone (even the old ones pre Gen-AI), and that school project you’ve coded that consists of 87 hand-rolled if-conditions (which you now know is called a rule engine), and every LLM or LLM-based product is an ANI. An ANI could compete with a human in its specialized domain, but it’s not able to transfer knowledge from one domain to another.

Strong AI or AGI (Artificial General Intelligence): An AGI does now have specialized knowledge in a single domain, but all human knowledge in all domains, and could perform comparably to a qualified human in anything you task it to do. This is science fiction, no matter how hard OpenAI wants to push its IPO. There isn’t even a definitive confirmation that we will ever achieve AGI, it’s “only” likely to happen at some point. This point in time would be the so-called singularity.

*ASI (Artificial super intelligence): Basically the same as an AGI, just that it’s better in everything. Even more science fiction, even less to say about it. Another purely theoretical construct, unlikely to be ever achieved.

What’s important to mention: Any LLM is by definition only an ANI, and it can never be an AGI. LLMs have a single domain: stochastic text proposals. They have no intelligence, emotion, knowledge or transfer capabilities. An LLM cannot be an AGI - ever. Whether it enables us to create it at some point - maybe. It seems intelligent and knowledgeable, because modern hardware, clouds and some algorithmic advances enabled a system which is (very simplified) based on the same algorithms like T9-typing on your old nokia, but has an absolutely unimaginable amount of data fed in. It seems emotional, because it’s based on data produced by humans, and we all are emotional beings, and also because it’s a system designed to be manipulative. You will only continue your LLM subscription when your “LLM-Buddy” is helpful, not only in providing good answers, but also packaging them in text which creates an emotional response in you when you read it.

I was playing Doom the other day and the enemy AI decided to attack me. I will not stand for that!

One thing to note is every useful ML application models when scaled will require huge amount of RAM. I remember around 5 years ago, I ran a simple STT model, and my 8GB computer was barely keeping up.