That's not really a valid argument for why, but yes the models which use training data to assemble statistical models are all bullshitting. TBH idk how people can convince themselves otherwise.

Through the years I've bounced between different engines. I gave Bing a decent go some years back, mostly because I was interested in gauging the performance and wanted to just pit something against Google. After that I've swapped between Qwant and Startpage a bunch. I'm a big fan of Startpage's "Anonymous view" function.

Since then I've landed on Kagi, which I've used for almost a year now. It's the first search engine I've used that you can make work for you. I use the lens feature to focus on specific tasks, and de-prioritise pages that annoy me, sometimes outright omitting results from sites I find useless or unserious. For example when I'm doing web stuff and need to reference the MDN, I don't really care for w3schools polluting my results.

I'm a big fan of using my own agency and making my own decisions, and the recent trend in making LLMs think for us is something I find rather worrying, it allows for a much subtler manipulation than what Google does with its rankings and sponsor inserts.

Perplexity openly talking about wanting to buy Chrome and harvesting basically all the private data is also terrifying, thus I wouldn't touch that service with a stick. That said, I appreciate their candour, somehow being open about being evil is a lot more palatable to me than all these companies pretending to be good.

It’s not doomerism it’s just realistic. Deluding yourself won’t change that.

This isn’t a thing.

I think because it's language.

There's a famous quote from Charles Babbage when he presented his difference engine (gear based calculator) and someone asking "if you put in the wrong figures, will the correct ones be output" and Babbage not understanding how someone can so thoroughly misunderstand that the machine is, just a machine.

People are people, the main thing that's changed since the Cuneiform copper customer complaint is our materials science and networking ability. Most things that people interact with every day, most people just assume work like it appears to on the surface.

And nothing other than a person can do math problems or talk back to you. So people assume that means intelligence.

I often feel like I'm surrounded by idiots, but even I can't begin to imagine what it must have felt like to be Charles Babbage explaining computers to people in 1840.

TBH idk how people can convince themselves otherwise.

They don’t convince themselves. They’re convinced by the multi billion dollar corporations pouring unholy amounts of money into not only the development of AI, but its marketing. Marketing designed to not only convince them that AI is something it’s not, but also that that anyone who says otherwise (like you) are just luddites who are going to be “left behind”.

Yesterday I asked an LLM "how much energy is stored in a grand piano?" It responded with saying there is no energy stored in a grad piano because it doesn't have a battery.

Any reasoning human would have understood that question to be referring to the tension in the strings.

Another example is asking "does lime cause kidney stones?". It didn't assume I mean lime the mineral and went with lime the citrus fruit instead.

Once again a reasoning human would assume the question is about the mineral.

Ask these questions again in a slightly different way and you might get a correct answer, but it won't be because the LLM was thinking.

What a dumb title. I proved it by asking a series of questions. It’s not AI, stop calling it AI, it’s a dumb af language model. Can you get a ton of help from it, as a tool? Yes! Can it reason? NO! It never could and for the foreseeable future, it will not.

It’s phenomenal at patterns, much much better than us meat peeps. That’s why they’re accurate as hell when it comes to analyzing medical scans.

But 90% of "reasoning humans" would answer just the same. Your questions are based on some non-trivial knowledge of physics, chemistry and medicine that most people do not possess.

You assume humans do the opposite? We literally institutionalize humans who not follow set patterns.

previous input goes in. Completely static, prebuilt model processes it and comes up with a probability distribution.

There is no "unlike markov chains". They are markov chains. Ones with a long context (a markov chain also kakes use of all the context provided to it, so I don't know what you're on about there). LLMs are just a (very) lossy compression scheme for the state transition table. Computed once, applied blindly to any context fed in.

LLMs are not Markov chains, even extended ones. A Markov model, by definition, relies on a fixed-order history and treats transitions as independent of deeper structure. LLMs use transformer attention mechanisms that dynamically weigh relationships between all tokens in the input—not just recent ones. This enables global context modeling, hierarchical structure, and even emergent behaviors like in-context learning. Markov models can't reweight context dynamically or condition on abstract token relationships.

The idea that LLMs are "computed once" and then applied blindly ignores the fact that LLMs adapt their behavior based on input. They don’t change weights during inference, true—but they do adapt responses through soft prompting, chain-of-thought reasoning, or even emulated state machines via tokens alone. That’s a powerful form of contextual plasticity, not blind table lookup.

Calling them “lossy compressors of state transition tables” misses the fact that the “table” they’re compressing is not fixed—it’s context-sensitive and computed in real time using self-attention over high-dimensional embeddings. That’s not how Markov chains work, even with large windows.

their input is the context window. Markov chains also use their whole context window. Llms are a novel implementation that can work with much longer contexts, but as soon as something slides out of its window, it's forgotten. just like any other markov chain. They don't adapt. You add their token to the context, slide the oldest one out and then you have a different context, on which you run the same thing again. A normal markov chain will also give you a different outuut if you give it a different context. Their biggest weakness is that they don't and can't adapt. You are confusing the encoding of the context with the model itself. Just to see how static the model is, try setting temperature to 0, and giving it the same context. i.e. only try to predict one token with the exact same context each time. As soon as you try to predict a 2nd token, you've just changed the input and ran the thing again. It's not adapting, you asked it something different, so it came up with a different answer

We also reward people who can memorize and regurgitate even if they don't understand what they are doing.

While both Markov models and LLMs forget information outside their window, that’s where the similarity ends. A Markov model relies on fixed transition probabilities and treats the past as a chain of discrete states. An LLM evaluates every token in relation to every other using learned, high-dimensional attention patterns that shift dynamically based on meaning, position, and structure.

Changing one word in the input can shift the model’s output dramatically by altering how attention layers interpret relationships across the entire sequence. It’s a fundamentally richer computation that captures syntax, semantics, and even task intent, which a Markov chain cannot model regardless of how much context it sees.

That depends on your assumption that the left would have anything relevant to gain by embracing AI (whatever that's actually supposed to mean).

Wow, I would deeply apologise on the behalf of all of us uneducated proles having opinions on stuff that we're bombarded with daily through the media.

Maybe you failed all your high school classes, but that ain't got none to do with me.

I think you're misunderstanding the argument. I haven't seen people here saying that the study was incorrect so far as it goes, or that AI is equal to human intelligence. But it does seem like it has a kind of intelligence. "Glorified auto complete" doesn't seem sufficient, because it has a completely different quality from any past tool. Supposing yes, on a technical level the software pieces together probability based on overtraining. Can we say with any precision how the human mind stores information and how it creates intelligence? Maybe we're stumbling down the right path but need further innovations.