dandi8

I hold a MSc from what is arguably the most prestigious University in Europe

Good for you. Have a cookie, I guess?

LLM do not simply regurgitate existing content, and are in fact capable of creating wholly new content not seen before.

Citation needed.

Hallucinations occur when their context buffer is too small, and as time goes on, it will largely be a thing of the past.

A whole book of citations needed. That claim is wildly inconsistent with the consensus about AI hallucinations.

Magic Eight Balls, as I’m sure you’re aware, have a limited, predetermined number of responses.

You mean like how LLMs keep hallucinating the same passwords and nonexistent dependencies to the point that bad actors are using that fact to compromise vibe coded systems via techniques like slopsquatting?

I would disagree with you, and would suspect you are basing your assessment of their abilities on dated usage.

In fact, I keep experimenting with frontier models (including Fable when it was available) just so that the “but we’ve made so much progress in the past few months” argument can’t be used against me. You’re wildly overselling their capabilities.

Except LLM output is largely gibberish. Just confident gibberish. There’s a reason we call it “AI slop”.

LLM responses are only ever “sound” when they’re regurgitating existing information they were trained on. Beyond some simple transformations, they are unable to create original ideas. They very frequently break down on somewhat unique tasks, as evidenced by the ever-prevalent code-slop which is eroding our software.

They don’t have a memory of previous conversations (unless you literally copy-paste it into the prompt), they don’t learn (Claude “memories” is literally just copy-pasting a summary into the prompt, only automatically). They don’t have any “thoughts” of their own between prompts (OpenClaw just keeps prompting them to pretend they are autonomous).

The underlying implementation of “reasoning” in LLMs is literally “hallucinate some more text which vaguely looks like thoughts and hope that influences the answer”. LLMs are probabilistic models which we figured out how to make so they produce somewhat correct-looking answers at a rate a little higher than chance.

Magic 8-balls sometimes give sound responses. Do they think? Where do we draw the line with this interpretation of “thinking”?

No, humans are not word predictors, and my claim is absolutely not an oversimplification.

LLMs are word predictors. No amount of attention heads and backpropagation is going to change that. Scientific researchers agree.

The human brain works in a completely different way to how LLMs do and to conflate the two like you did is disingenuous.

Word predictors don’t think any more than a magic 8-ball does.

That’s exactly the kind of language I would expect it to use.

Sure, I could be wrong.

At the same time, people on the internet don’t seem to be talking this way. Every AI-generated response I have ever gotten looks exactly like that, though.

I’m not basing my assumption on how I talk so the psychology lesson doesn’t quite apply.

Why are we sharing this? Why is this important? CEO of Oreo says you should eat 10 oreos a day to stay healthy. Shocker.

“it’s not x, it’s y”, forcing the rule of 3 into every paragraph, flowery language that doesn’t add anything or make much sense (e.g.,“it’s the silence in your chest where the certainty used to be”) and other tells that this was AI generated. Do you not notice that this is not how humans speak?

Why would you use AI to generate this text, tho?

The leading cause will be population decline leading to the inability to defend your own boarders.

That sounds awfully like right wing xenophoba.

And who made your local models?

The author seems to still be encouraging layoffs if they truly let the company save money.

Bleh.

Check their batteries, just in case. They might have turned into pillows.

Unit testing with LLMs is just asking an AI to hallucinate requirements.

Tests are what documents expected behavior and are therefore the worst candidate for code gen.