They’re made out of weights

Not quite exactly.

    Teapots are not compelling. | | God is compelling t billions of people.

My error was not pointing out those sentences. With those in mind you see that I take each of your claims and makes a parallel to the Teapot argument.

> It's metonymy. "X" stands in for "people who argue for X".

I needed to be sure. I have enough experience with people replying that they didn’t mean that.

Sometimes people say the opposite thing because they accidentally negated a sentence.

> If it looks like a duck, if it quacks like a duck, you should adjust your priors to assign a higher likelihood that it is a duck.

Then someone points out that it has no anus and doesn’t need to breathe. But bodily functions? Harumph, why would a true duck need that?

Of course people who argue for AI Consciousness already know that. The actual case under discussion is more absurd than a duck trying to mate with a robot duck.

> All measurements contain error; you can't ever observe, "that is a duck," only "that looks like a duck". All knowledge is founded on a sufficiently deep stack of "looking like a duck" that we may assert it with confidence.

All perfectly generic philosophical arguments. Which doesn’t hold any more water for AI Consciousness than AI Toaster... except for the fact that humans find it compelling.

Like how a light bug could find artificial light compelling.

> To the extent we have objective measures (like conducting a Turing test on blind participants), it can meet them too. You can't say the same of a toaster.

The Turing Test is bunk with regards to objective reality, like whether some thing has consciousness.

> "It is a statistical model, ergo it is not conscious" is also an argument from incredulity. I don't know if that's your view or not but it's the one my remarks have been addressing in general.

Who has said ergo? Don’t just assert that someone has asserted a logical implication unless that is the case.

Bullshit arguments don’t get the privilege of symmetry. That AI might be conscious because it is compelling to humans is a bullshit argument, and just supported by but I don’t see why not. People are free to dismiss the claim by pointing at one of a hundred factors, like how it is “just a statistical model”. Just pull something out of the hat.

Yes, it is very unfair. The Church of AI can do the hard work of throwing out incredulous claims while people who don’t quite buy those statement don’t read up on five different philosophical and scientific subjects in order to get an informed opinion about how a primate might be different from a blob of numbers.

https://news.ycombinator.com/item?id=44098845

I don't want to waste time going over weak uncompelling critique like, if human ~~sound production~~ intelligence is analogue, then ~~digital speakers~~ artificial intelligence is unlikely to be possible (to paraphrase the critique of the Biological Assumption); but I am genuinely interested in the increasing evidence that shows that the foundation of modern AI research is flawed.

You can then proceed with Metaphors we live by, by Lackoff and Johnson. The book shows how and why our understanding of the world is based on the fact that we are embodied beings.

Then there is Being and Time, by Martin Heidegger. It shows how our understanding of the world is, again, based on the fact that we are embodied beings.

Now, these are not newly edited books, and no, there is no real reason to think that because they are all +30 years old or even more, they are outdated. They are not. If you only look at the publication date, then Ramon y Cajal works would be totally crap (by the way, still one of the most cited works in neuroscience). It is from early 1900s.

To complete the picture a bit, you could read:

On the mode of existence of technical object, by Gilbert Simondon Technics and Time, by Bernard Stiegler Meditation on the technique, by Jose Ortega y Gasset The question concerning technology, by Martin Heidegger

These works will give an understanding of how technique in general (technology in particular) is completely anthropomorphisized, which is what ultimately leads to the assumptions present in the AI research programme.

Also, A history of philosophy, by Frederick Copleston. Although extensive, reading volume I (greek and roman philosophy) is essential.

More citations (again, if you really measure the quality or relevance of a philosophical/scientific work by its publication date, you are missing the picture):

Arbib, M. A. (2025).* Artificial intelligence meets brain theory (again). Biological Cybernetics, 119, 16. https://doi.org/10.1007/s00422-025-01013-5

Farkaš, I., Vavrečka, M., & Wermter, S. (2025).* Will multimodal large language models ever achieve deep understanding of the world? Frontiers in Systems Neuroscience, 19, 1683133. https://doi.org/10.3389/fnsys.2025.1683133

Lin, Z. (2025).* Six fallacies in substituting large language models for human participants. Advances in Methods and Practices in Psychological Science, 8(3), 25152459251357566. https://doi.org/10.1177/25152459251357566

Seth, A. K. (2025).* Conscious artificial intelligence and biological naturalism. Behavioral and Brain Sciences, 1–42. https://doi.org/10.1017/S0140525X25000032

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024).* Dissociating language and thought in large language models. Trends in Cognitive Sciences, 28(6), 517–540. https://doi.org/10.1016/j.tics.2024.01.011

Mitchell, M., & Krakauer, D. C. (2023).* The debate over understanding in AI's large language models. Proceedings of the National Academy of Sciences, 120(13), e2215907120. https://doi.org/10.1073/pnas.2215907120

Bowers, J. S. (2025).* The successes and failures of artificial neural networks (ANNs) highlight the importance of innate linguistic priors for human language acquisition. Psychological Review. Advance online publication. https://doi.org/10.1037/rev0000595

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024).* Dissociating language and thought in large language models. Trends in Cognitive Sciences, 28(6), 517–540. https://doi.org/10.1016/j.tics.2024.01.011

Bolhuis, J. J., Crain, S., Fong, S., & Moro, A. (2024).* Three reasons why AI doesn't model human language. Nature, 627(8004), 489. https://doi.org/10.1038/d41586-024-00824-z

Bender, E. M., & Koller, A. (2020).* Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185–5198. https://doi.org/10.18653/v1/2020.acl-main.463

Everaert, M. B. H., Huybregts, M. A. C., Chomsky, N., Berwick, R. C., & Bolhuis, J. J. (2015).* Structures, not strings: Linguistics as part of the cognitive sciences. Trends in Cognitive Sciences, 19(12), 729–743. https://doi.org/10.1016/j.tics.2015.09.008

Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002).* The faculty of language: What is it, who has it, and how did it evolve? Science, 298(5598), 1569–1579. https://doi.org/10.1126/science.298.5598.1569

Johnson, M., & Lakoff, G. (2002). Why cognitive linguistics requires embodied realism. Cognitive Linguistics, 13(3), 245–263. https://doi.org/10.1515/cogl.2002.016

Lakoff, G. (2012). Explaining embodied cognition results. Topics in Cognitive Science, 4(4), 773–785. https://doi.org/10.1111/j.1756-8765.2012.01222.x

Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1–3), 335–346. https://doi.org/10.1016/0167-2789(90)90087-6

Placani, A. (2024). Anthropomorphism in AI: Hype and fallacy. AI and Ethics, 4, 691–698. https://doi.org/10.1007/s43681-024-00419-4

Salles, A., Evers, K., & Farisco, M. (2020). Anthropomorphism in AI. AJOB Neuroscience, 11(2), 88–95. https://doi.org/10.1080/21507740.2020.1740350

Floridi, L. (2025). AI as agency without intelligence: On artificial intelligence as a new form of artificial agency and the multiple realisability of agency thesis. Philosophy & Technology, 38(1), 1–27. https://doi.org/10.1007/s13347-025-00858-9 Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Philosophical Psychology, 20(2), 247–268. https://doi.org/10.1080/09515080701239510

Bengio, Y., & Elmoznino, E. (2025). Illusions of AI consciousness. Science, 389(6765), 1090–1091. https://doi.org/10.1126/science.adn4935

Dotov, D. G., Nie, L., & Chemero, A. (2010). A demonstration of the transition from ready-to-hand to unready-to-hand. PLOS ONE, 5(3), e9433. https://doi.org/10.1371/journal.pone.0009433

Mitchell, M. (2021). Why AI is harder than we think. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021). https://doi.org/10.1145/3449639.3465421

I haven't read all of them yet. Feel free to discuss.

Now, there are two problems I see in the community regarding the critique of AI. One is the problem of the increasing capability of models. The other is the idea that GOFAI and ANN-based systems (like LLMs) are fundamentally different. Let me explain.

1) The increasing capability of models: it is difficult to engage in any meaningful discussion if the metrics are the capability of models. One should look at how models structurally encode information and what the learning process looks like from an epistemic point of view. As far as I know, and correct me if I'm wrong, these two issues have not changed and are likely not going to change.

2) The idea that GOFAI and ANN-based systems are fundamentally different: this, I recognize, is a controversial claim. But one should not look at how GOFAI and ANN-based systems encode knowledge (explicitly curated and written rules vs statistical learning), but at how the learning material is selected, curated and presented to the system, and the problem of 'closure' and self-reference in datasets. In this regard (which we could call epistemic) there should be no difference between these two technologies. Again, we should not look at how they are implemented, but at how we relate to them from an epistemic point of view.

But going back to my initial comment, this whole thread feels like proving my point. For those not wanting to get involved in philosophy despite willing to engage in AI research discussions, keep in mind that philosophy has always been a guiding light for science.

As a final note: the whole discussion about AI is on whether computational theories of mind are actually solid or not. But it is really difficult to engage in this conversation without at least some background in philosophy, but preferably a strong background in it.

I'm getting a bit tired of coming back to this thread. Reach me out if you want to discuss more. Glad to help and glad to learn.

@federico_ricca https://www.linkedin.com/in/federico-ricca