[Lingtyp] On AI, Language, and (In)Human Thinking

Thu Nov 13 16:46:42 UTC 2025

Dear all,

Several colleagues wrote to me privately in response to the task, and I would like to thank you all for your engagement. Quite a few suggested crossword or rebus solving/constructing as a possible answer. It seems that crosswords / rebuses resonate intuitively with linguists when thinking about form–meaning relations.

One of the private messages made me particularly happy: it included an additional step, namely further checking my solution against a real-life situation — something a mathematically trained person would do, too. I will not go into detail here, but I appreciate that colleague’s thinking very much; it showed the kind of independent reasoning I had hoped the task would inspire. Overall, I was surprised by the colleagues who wrote to me privately: famous scholars and shy!

Now, to make the math-based approach I revealed to you more directly applicable to linguistic analysis, let us try something new, namely evaluating linguistic terminology. In other words, using parallels from real-world activities, we will now examine linguistic terms. I suggest backformation and borrowed morphology as an easy (and fun) starting point. Note that ChatGPT does not have both.

Let us give ourselves three days this time, i.e. the time limit ends on November 16 (Sunday) at the exact time when this message is sent. But unlike last time, I would like to reward quick thinkers — just like at a serious math event — so feel free to post your solutions the moment they occur to you. I am looking forward to seeing what comes first, and I hope this round will be livelier publicly!

Best,

Stela

> On 11.11.2025, at 22:01, Stela MANOVA <manova.stela at gmail.com> wrote:
> 
> Hello everyone,
> 
> The time limit for problem solving is over. I hope you enjoyed the process of looking for a solution. As promised, here is my answer.
> 
> An activity in the real world that clearly separates meaning and form is film recording. In filmmaking, picture and sound are recorded on separate tracks and can exist independently. The picture track corresponds to meaning (think of a silent film), while the sound or written script corresponds to form (think of radio broadcasting or a play script, respectively). Picture and sound/script can be stored on their own data carriers and later synchronized. Thus, the goal here is not to deny the interconnection of form and meaning, but to show that their (temporary) separation and later synchronization are familiar operations for the human brain.
> 
> Thanks to the mediation of the human brain, even before synchronization, we can produce sound or text based on the picture, and conversely, we can reconstruct the picture based on sound or text. However, the two operations are not the same, and reconstruction based on form appears more stable and precise, while reconstruction based on meaning is less so. I think this explains why listening to the radio and reading a text are normal activities, whereas silent films are no longer produced. In other words, with language generation based on form, the ChatGPT creators made the perfect choice. 
> 
> At this point, a math-gifted person will start looking for similar phenomena in other scientific fields to ensure that this is indeed the right analogy to the separation of meaning and form. Analogous examples do exist — I could find parallels in mathematics, physics, and biology. Intriguingly, each scientific field has its own version of the phenomenon. But I am not sure whether it is a good idea to mention all the examples here; the amount of information could feel overwhelming for untrained brains (recall that my brain was trained for ten years, and I am math-gifted, which further facilitates analogizing and problem solving for me).
> 
> Thus, let me stop here and see your answers now. I am very excited! Please introduce your findings in the way I have done above and explain why you believe that your examples show a separation of meaning and form and allow for reconstruction of each aspect from the other. Maybe we should wait for a few posts and then start the discussion.
> 
> Looking forward to your examples and explanations.
> 
> Best,
> 
> Stela
> 
> 
> 
> 
>> On 10.11.2025, at 03:14, Anna Alexandrova <anna.alexandrova at uniroma1.it> wrote:
>> 
>> Dear Stela,
>> 
>> Thank you for this Big Yus Ѫ energy moment (is енергия на голям юс the correct wording in Bulgarian?). I suspect many of us are a bit exhausted for a challenge at the moment, though.
>> 
>> We did enjoy our math and coding classes back in school, even if we might remember them less intensely than you do.
>> 
>> Best regards,
>> 
>> A.
>> 
>> 
>> Il giorno dom 9 nov 2025 alle ore 22:18 Stela MANOVA via Lingtyp <lingtyp at listserv.linguistlist.org <mailto:lingtyp at listserv.linguistlist.org>> ha scritto:
>>> Dear colleagues,
>>> 
>>> It turned out that the topic of LLMs is exactly like the topic of language — everybody feels competent, irrespective of their qualifications. In what follows, I would like to address some misinformation that appeared in relation to AI in recent messages on this list: that LLMs are designed to generate costs and therefore end virtually every answer with a question, and that they are “dangerous” because they operate in an entirely inhuman way, based only on form.
>>> 
>>> As many of you know, my work focuses on ChatGPT, so I will use it in my examples:
>>> 
>>> ChatGPT knows many things but cannot start a conversation. It needs prompts — i.e., contextual anchors — to select the next token. This is why it often ends answers with a question: not because it “thinks of money,” but because it seeks additional input. The larger the input, the better the answer. Note that the fact that AI is reactive, not proactive, places the human in control of the machine.
>>> 
>>> LLMs are not mere text collections but a triumph of human intelligence. You do not believe that if you have the whole Internet in text format, this huge amount of text will start speaking like a human by itself. Behind LLMs lies immense conceptual and mathematical work — all done by mathematically gifted humans. I describe the mindset of such people in my paper https://ling.auf.net/lingbuzz/008998 <https://ling.auf.net/lingbuzz/008998>, including how the creators of ChatGPT arrived at the idea of representing language as a linear sequence of tokens. The idea to put all languages in a shared representational space is equally remarkable: that way they get everything (grammar, semantics, typology, etc.) for free, so to speak; data self-classify, and they can work even with pieces of data and in many languages simultaneously. Compare this with the linguistic approach: each language is described separately; we compare data only when (complete) language descriptions are ready, and the transfer of classificational features from language to language is not always obvious or smooth.
>>> 
>>> As those of you who have read the paper mentioned above know, I was educated as a math-gifted student for ten years. What I do not mention in the paper is that my brain was trained for mathematical thinking at least five hours a day, every day except Sundays — yes, for ten years — to form the necessary neural connections. And yes, I learned university mathematics in my teens. A mathematically gifted child often hears three things:
>>> 
>>> i) All problems are already solved in the real world; you only need to find the right analogy.
>>> 
>>> ii) It is the belly that knows, not the head. Listen to your belly!
>>> 
>>> iii) All problems have more than one solution.
>>> 
>>> Allow me now to propose a short experiment related to the alleged “inhuman” way LLMs treat language — namely, by separating meaning and form. (By “separation” I mean only that the two aspects can be processed or represented independently for a while. By the mediation of the human brain, they both then appear meaningful.) The goal of the experiment is to make you experience the way of thinking of a math-gifted person and to demonstrate that there is an analogy to the ChatGPT approach to language in the real world.
>>> 
>>> So, the task: Can you find an activity in the real world where form and meaning are separated, yet each can be reconstructed from the other? If you can, the separation of meaning and form is not alien to the human brain.
>>> 
>>> Following the spirit of mathematical training — where there is always a time limit, including a period for full concentration (with a “no restroom” rule) — I propose the following:
>>> 
>>> It is now November 9, 22:00 CET, and I am giving you two days (until November 11, 22:00 CET) to find the activity described in the task (it could be from any sphere of human life). During this period, I kindly ask that no messages be posted in this thread, so that we can all focus on the task (the “no restroom” rule does not apply).
>>> 
>>> After the time limit elapses, we will collect our findings and discuss them. I have already solved the task (and mentioned the solution in an exchange with a well-known neurolinguist — I hope this does not spoil the experiment). On the third day, I will share my answer and look forward to hearing yours.
>>> 
>>> I hope this demonstration will bring you closer to the thinking behind ChatGPT and show that there is nothing “dangerous” in the model — only a new, fascinating way of representing language grounded in the real world. (Think of the shared representational space as the Earth where all humans live.)
>>> 
>>> Best wishes,
>>> 
>>> Stela / Gauss:AI Global
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
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>> 
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