It's like a photo. For me at least, it's kind of ghostly and transparent and appears briefly overlaid on what I can see with my physical eyes.
When I was younger the images were more solid and stable. Teachers often complained that I was spaced out because I was paying attention to some inner images.
That faculty greatly deteriorated and I became hyperverbal around the same time. (Coinciding, curiously with my shift from introversion to extroversion. Spurred, presumably, by my then-new interest in women!)
These days I can still access that previous level of visual fluency if I am in a state of very deep relaxation. (So it's conceivable that cortisol inhibits the circuits required to have such experiences. But I have no idea!)
Okay, comment version 2.0 since someone instantly flagged me for replying with a dictionary entry.
The reason my comment is fundamentally a dictionary entry is because I was asked "what is it?" in response to a two sentence comment where I already said it was "conceptual". I think there's a very wide gap in understanding here, and restating and defining the word "concept" is the best way to bridge that gap. All I can really do is make my point clearer (via dictionary entry), I can't say anything else useful until I understand what wasn't clear about my previous comment.
If the person that flagged me still has a problem with my comment, please explain it.
Reply:
> So what is it, then? Some mystical thing?
concept/ˈkänˌsept/A concept is a broad mental plan, general idea, or core principle used to understand and group things together. It acts as a basic building block for human thought, language, and design.
Also, why are you asking me? Didn't you just accept it not being text and then argue it's also not an image?
I have noted the employment of a language technique that as per the wikipedia article "signs of AI writing" is commonly used by AI, you can attack facts all you like and that will indeed stop me from talking!
I think the distinction is that when an AI generates the phrase it chooses to use the negation and then fills in the terms. If it only has one thing to communicate at that point then it is essentially placing a redundant rephrasing on onr side of the negation.
My use of it took the form of
A statement on how a guillotine kills people when they are powerless.
The negation then, in the first part compares that to the connotation of the previous post of an underdog battling those in power, and rejects it as inconsistent and the negation provides an alternative framing that is consistent with the action of killing a person who can no longer harm you.
Both sides of the negation make a point.
This is obviously much more common in a dialogue than a monologue, because the first part is used to reference words of the other party. In a monologue, that other party is the past self of the speaker, it suggests a change in position. I think models end up using as a short hand for realisation.
Are there any good collections of negation usage in AI with the surrounding paragraphs? I'd be interested to see what percentage of them are an expression of self realisation.
language models are trained to maximise engagement and maximally convey meaning. binary contrast happens to achieve these goals by optimally translating the manifold. if I come across further research it would be interesting to continue our language related discussion.
>language models are trained to maximise engagement
I have seen no research to that effect.
>and maximally convey meaning
Nobody knows how to measure that
They are trained to complete sequences, then they are trained to engage in conversations, produce what people prefer (with a fairly crude measure of preference, hence the sycophancy). Reasoning models are trained to produce what an observer model think will give you the right answer, the observer model simultaneously learns the likelihood of producing the right answer.
All these things train to improve a property that can be measured at training time. You could build a model to estimate engagement, but I have not seen any evidence that shipping LLMs have used a measure like this.
I can't even imagine how you could tell if a measure of meaning was accurate or not, let alone how you could produce such a measure. You could try to measure information density, but that would also include noise.
It is the overuse of such tells. Please, let's not ban uses of not X, but Y completely. It's a rhetoric technique that is completely fine, with moderation. But real human will use them with moderation.
Also, there is the issue of human language taking some 'tells' because they are exposed to LLM output a lot.
I'm still grumpy that LLMs made it nearly impossible to use the words 'delve' or em-dashes without getting called out as an LLM.
That view is incredibly reductionist. It really is an efficient encoding of how nature behaves. It might be a human construct, but given how best it allows to understand nature (through principles of physics), it is uncanny to be any different from the language of nature.
Reminds me of Wigner's Unreasonable effectiveness of mathematics in natural sciences [0].
Nature has no language. Language is a human tool to imperfectly describe nature. Putting language before nature is magical thinking (also known as Platonism).
Yes, of course mathematics is a tool to model nature as accurately as we could. That still doesn't mean mathematics is as trivial or frivolous as manipulating symbols.
At a very high level mathematics is basically 100% text/symbolic rewriting. You start from some set of postulate assumed true and you do your thing to get a new different set of equivalent assertions in a form that is more useful.
I don’t know if LLMs will kill the working-mathematicians but at least seem like that it doesn’t seem absurd to imagine LLMs will be good at math…
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