We have to stop acting like these things "think"; it leads to really weird misinterpretations of the output as "meaning" things.

For example, they will occasionally replace "colour" with "color". Why? Because both occur in the training data in the "same role" but "color" is, apparently, more common[1]. You can also trick them into replacing things like "sardines" with "anchovies" (on pizza) and "head of lettuce" with "cabbage" in the context of rowboats.

They are lossy text compressing parrots and we are all suffering from a massive madness-of-crowds scale Eliza Effect.

[1] Yep. https://books.google.com/ngrams/graph?content=color%2C+colou...

▲ Alive-in-2025 a day ago | parent | next [-]

This feels very different because there is no powerful political force trying to squelch discussion of colour or sardine. But there are lots of powerful folks trying to avoid discussions about Gaza or Palestine and related things. It's to their advantage to have tools hide that word

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rglover a day ago | parent | next [-]

It feels different because it's a political matter, but this is just probability doing what it does. Considering "Ukraine" is likely far more common in the training data, this isn't a terribly surprising outcome.

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MarkusQ a day ago | parent | prev [-]

There are also an awful lot of people trying to push it/publicize it.

	▲	Alive-in-2025 a day ago \| parent [-]
		There's always two sides, but there's a power imbalance. One side includes two of the most powerful nation states (Israel and the US), plus the oligarch billionaires in the US on Israel's side. The people wanting to talk about Palestinians is much weaker nations or states or forces, plus us based people. I hesitate to say us popular opinion because we aren't all on the same page... But it feels like it is trending that way.

▲ stetrain a day ago | parent | prev | next [-]

When a company packages this tool up and makes it part of their product they are taking some of that responsibility. The end user isn't supposed to need to know what an LLM is or how it works, that's what they're paying Canva for.

▲ wonnage a day ago | parent | prev [-]

There are trillions of dollars riding on the fact that they in fact think, and a bunch of people here have their lottery tickets tied up in that, so good luck with that

▲ semiquaver a day ago | parent [-]

Don’t worry, goalpost shifting will ensure that no matter how useful LLMs get, there will always be a large contingent of people who insist that anything non-human is not thinking, just sparkling cognition.

▲ ozlikethewizard a day ago | parent [-]

LLMs are not/will never be thinking though, no matter how good they get? You could potentially argue that there is some level of cognition during the training phases (as long as that isn't being outsourced to humans anyways), but generation of output is stachostic selection of most common (/highly ranked if tuned) following patterns? They cannot learn things outside of training, nor do they actually "know" things. To use the parrot example from above, a parrot doesnt "know" what the words its been taught to mimic are, nor does an LLM "know" what the concept of love is, its just be trained to regurgitate the words that are used by humans to describe such a thing. This isn't a criticism of LLMs, that's what they're supposed to do, but its certainly not cognition.

▲ E-Reverance a day ago | parent | next [-]

They factorize the distribution in which they are trained on which is essentially generalization

https://arxiv.org/abs/2602.02385

▲ semiquaver a day ago | parent | prev [-]

You’re assuming that thinking requires learning, which I don’t necessarily agree with. Humans can have brain damage which inhibits the formation of long term memories, but such people can still function in the world. Would you say the thing such a person’s brain is doing is something other than thinking?

At any rate, just because the architecture of current LLMs doesn’t support learning at inference time does not constitute a fundamental limit that can never be changed, just a local maximum that has worked well to productize the approach.

And I’m quite certain that once systems that include post-training learning exist people like you will find a way to distinguish that from human learning, moving the goalposts again. You’re not arguing in good faith, you have an essentially religious opinion and you will stick to it as long as you are able.

  > but generation of output is stachostic selection of most common (/highly ranked if tuned) following patterns

This is not an accurate description of the transformer architecture. I’m not surprised that you are misinformed about this.

	▲	wonnage a day ago \| parent [-]
		Moving the goalposts describes the entire history of AI. it’s “AI” in the hype phase and turns into “OCR” once it actually works. At various points quadcopters, towel folding, image recognition, sentiment analysis, etc. were all “AI”