[runarberg]

> Categories of _what_, exactly?

Precisely. At least apples and oranges are both fruits, and it makes sense to compare e.g. the sugar contents of each. But an LLM model and the human brain are as different as the wind and the sunshine. You cannot measure the windspeed of the sun and you cannot measure the UV index of the wind.

Your choice of the words here was rather poor in my opinion. Statistical models do not have cognition any more than the wind has ultra-violet radiation. Cognition is a well studied phenomena, there is a whole field of science dedicated to cognition. And while cognition of animals are often modeled using statistics, statistical models in them selves do not have cognition.

A much better word here would by “abilities”. That is that these tests demonstrate the different abilities of LLM models compared to human abilities (or even the abilities of traditional [specialized] models which often do pass these kinds of tests).

Semantics often do matter, and what worries me is that these statistical models are being anthropomorphized way more then is healthy. People treat them like the crew of the Enterprise treated Data, when in fact they should be treated like the ship‘s computer. And I think this because of a deliberate (and malicious/consumer hostile) marketing campaign from the AI companies.

[Workaccount2]

It's easy to handwave away if you assign arbitrary analogies though.

If we stay on topic, it's much harder to do since we don't actually know how the brain works. Outside at least that it is a computer doing (almost certainly) analog computation.

Years ago I built a quasi mechanical calculator. The computation was done mechanically, and the interface was done electronically. From a calculators POV it was an abomination, but a few abstraction layers down, they were both doing the same thing, albeit my mecha-calc being dramatically worse at it.

I don't think the brain is an LLM, like my Mecha-calc was a (slow) calculator, but I also don't think we know enough about the brain to firmly put it many degrees away from an LLM. Both are infact electrical signal processors with heavy statistical computation. I doubt you believe the brain is a trans-physical magic soul box.

[runarberg]

But we do know how the brain works, we have extensively studied the brain, it is probably one of the most studied phenomena in our universe (well barring alien science) and we do know it is not a computer but a neural network[1].

I don’t believe the brain is a trans-physical magic soul box, nor do I think an LLM is doing anything similar to an LLM (apart from some superficial similarities; some [like the artificial neural network] are in an LLMs because it was inspire by the brain).

We use the term cognition to describe the intrinsic properties of the brain, and how it transforms stimulus to a response, and there are several fields of science dedicated to study this cognition.

Just to be clear, you can describe the brain as a computer (a biological computer; totally distinct from a digital, or even mechanical computers), but that will only be an analogy, or rather, you are describing the extrinsic properties of the brain which it happens to share some of which with some of our technology.

---

1: Note, not an artificial neural network, but an OG neural network. AI models were largely inspired by biological brains, and in some parts model brains.

[Benjammer]

Wind and sunshine are both types of weather, what are you talking about?

[runarberg]

They both affect the weather, but in a totally different way, and by completely different means. Similarly the mechanisms in which the human brain produces output is completely different from the mechanism in which an LLM produces output.

What I am trying to say is that the intrinsic properties of the brain and an LLM are completely different, even though the extrinsic properties might appear the same. This is also true of the wind and the sunshine. It is not unreasonable to (though I would disagree) that “cognition” is almost the definition of the sum of all intrinsic properties of the human mind (I would disagree only on the merit of animal and plant cognition existing and the former [probably] having similar intrinsic properties as human cognition).

[Kiro]

Artificial cognition has been an established term long before LLMs. You're conflating human cognition with cognition at large. Weather and cognition are both categories that contain many different things.

[runarberg]

Yeah, I looked it up yesterday and saw that artificial cognition is a thing, though I must say I am not a fan and I certainly hope this term does not catch. We are already knee deep in bad terminology because of artificial intelligence (“intelligence” already being extremely problematic even with out the “artificial” qualifier in psychology) and machine learning (the latter being infinitely better but still not without issues).

If you can‘t tell I find issues when terms are taken from psychology and applied to statistics. The terminology should flow in the other direction, from statistics and into psychology.

So my background is that I have done both undergraduate in both psychology and in statistics (though I dropped out of statistics after 2 years) and this is the first time I hear about artificial cognition, so I don‘t think this term is popular, and a short internet search seems to confirm that suspicion.

Out of context I would guess artificial cognition would mean something similar to cognition as artificial neural networks do to neural networks, that is, these are models that simulate the mechanisms of human cognition and recreate some stimulus → response loop. However my internet search revealed (thankfully) that this is not how researches are using this (IMO misguided) term.

https://psycnet.apa.org/record/2020-84784-001

https://arxiv.org/abs/1706.08606

What the researchers mean by the term (at least the ones I found in my short internet search) is not actual machine cognition, nor claims that machines have cognition, but rather an approach of research which takes experimental designs from cognitive psychology and applies them to learning models.