> TFA literally and unironically includes such phrases as "AI is awesome". It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels".

This does not contradict what I said.

> In what sense is the article dismissive? What, exactly, is it dismissive of?

Consider the following direct quotes:

> It’s like having the world’s most educated parrot: it has heard everything, and now it can mimic a convincing answer.

or

> they generate responses using the same principle: predicting likely answers from huge amounts of training text. They don’t understand the request like a human would; they just know statistically which words tend to follow which. The result can be very useful and surprisingly coherent, but it’s coming from calculation, not comprehension

I believe these examples are self-evidently dismissive, but to further put it into words, the article - ironically - rides on the idea that there's more to understanding then just pattern recognition at a large scale, something mystical and magical, something beyond the frameworks of mathematics and computing, and thus these models are no true scotsmans. I wholeheartedly disagree with this idea; I find the sheer capability of higher level semantic information extraction and manipulation to be already a clear and undeniable evidence of an understanding. This is one thing the article is dismissive of (in my view).

They even put it into words:

> As impressive as the output is, there’s no mystical intelligence at play – just a lot of number crunching and clever programming.

Implying that real intelligence is mystical, not even just in the epistemological but in the ontological sense, too.

> But here at Zero Fluff, we don’t do magic – we do reality.

Please.

It also blatantly contradicts very easily accessible information on how a typical modern LLM works; no, they are not just spouting off a likely series of words (or tokens) in order, as if they were reciting from somewhere. This is also a common lie that this article just propagates further. If that's really how they worked, they'd be even less useful than they presently are. This is another thing the article is dismissive of (in my view).

I honestly find this kinda stuff more terrifying than the models themselves.

They can, but won't necessarily. If you use a managed service, they likely will, due to batched inference. Otherwise, it's simply a matter of configuring the seed to a fixed value and the temperature to 0.

At least that's what I did, and then as long as the prompts were exactly the same, the responses remained exactly the same too. Tested with a quantized gemma3 using ollama, I'd say that's modern enough (barely a month or so old). Maybe lowering the temp is not even necessary as long as you keep the seed stickied, didn't test that.

Not true. How the higher level thought is occurring continues to be a mystery.

This is an emergent behavior that wasn’t predicted prior to the first breakthroughs which were intended for translation, not for this type of higher level reasoning.

Put it this way, if we truly understood how LLMs think perfectly we could predict the maximum number of parameters that would achieve peak intelligence and go straight to that number.

Just as we now know exactly the boundaries of mass density that yield a black hole, etc.

The fact that we don’t know when scaling will cease to yield new levels of reasoning means we don’t have a precise understanding of how the parameters are yielding higher levels of intelligence.

We’re just building larger and seeing what happens.

The engineers who built these things in actuality don’t understand how it works. Literally. In fact you can ask them and they say this readily. I believe the CEO of anthropic is quoted as saying this.

If they did understand LLMs why do they have so much trouble explaining why an LLM produced certain output? Why can’t they fully control an LLM?

These are algorithms running on computers which are deterministic machines that in theory we have total and absolute control over. The fact that we can’t control something running on this type of machine points to the sheer complexity and lack of understanding of the thing we are trying to run.

> But these technologies, by and large, are understood by the engineers that created them.

Simply incorrect. Look into the field of AI interpretability. The learned weights are black boxes, we don't know what goes on inside them.

Models are grown, not built. The ruleset is engineered, the training framework built, but the model itself that grows through training is incredibly dense complexity.

Put it this way Carlson. If you were building LLMs if you understood machine learning if you were one of these engineers who work at open ai, you would agree with me.

The fact that you don’t agree indicates you literally don’t get it. It also indicates you aren’t in any way an engineer who works on AI, because what I am talking about here is an unequivocal and universally held viewpoint held by literally the people who build these things.

Yeah, the brain obviously has types of circuits and networks that are doing things llms don't do, they have timing, and rhythm, and extremely complex feedback loops, there's no justification to call llms sentient. But all the people trying to say they're categorically different are wrong. Brains process sequential electrical signals from the senses, and send sequential signals to the muscles. That's it. The fact that modern neural networks can trivially shift modes from audio, language, image, video, 3d or any other symbolic representation is obviously a significant development and something significant has happened in our understanding of intelligence.