[f_klem]

The books that I referenced at my first comment already contain an extensive explanation of why those assumptions are flawed, false, or ungrounded.

I will not paste here parts of books.

Nonetheless, I've been compiling references on different AI research assumptions and problems. I'll paste them here later on.

[LudwigNagasena]

If I take a text written on a tangential topic from a generation or two ago and try to imagine how it applies to the current state of AI that would be me putting words in your mouth and speculating on your interpretation.

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.

[ozgung]

After watching a lecture of Dreyfus on Heidegger and skimming his books I think I begin to see what's going on. Hubert Dreyfus teaches a specific set of philosophers beginning with Heidegger. His brother Stuart is an industrial engineer working on programming very early computers (50s) for Operations Research. They both worked at RAND corporation and involved in early "AI" research projects for military. Those AI projects of course were logic based problem solvers of 50s and 60s. But Hubert sees a problem with this work. What they do is incompatible with the philosophical tradition he belongs:

"I began reading NSS's landmark papers with a mixture of excitement and fear. Perhaps Hobbes, Kant, and Husserl were right after all, and the human mind was an analytic engine. But then what about the seemingly plausible arguments of Merleau-Ponty, Heidegger, and Wittgenstein, which I had come to accept? As I read the RAND papers my excitement and fear turned to disappointment and relief."

I think this was more like a cognitive dissonance than an actual contradiction. It was about choosing sides between Heidegger and maybe Descartes for him. That's why his objection sounds personal and dogmatic.

So what was the big idea of Heidegger? Elephant in the room is the concept of "Dasein" (being-there), which Dreyfus think Heidegger is a genius for being the first philosopher noticing that. Dasein is an entity, with special mode of being. Only *human-beings* can be Dasein. So it's not an "object" like a table, that has properties (like in object oriented programming). It's not an equipment like a hammer (objects having methods). Human-beings, and only human-beings (definitely not LLMs, not dogs) can have this special way of being, or Existence. This idea of course has some roots in Christian Theology, as Heidegger himself.

I think this is the reason of the strong opinions. A bit dogmatic. A belief that humans are fundamentally different beings. So an Equipment trying to mimic "Dasein" is categorically wrong (even impossible) in this belief system. The problem doesn't have to be Dasein itself, but you get the idea. It's either-or. If modern AI research is not flawed, their philosophy must be flawed. Since it can't be flawed, AI research must be flawed. Since research is trial and error, failures are the part of the process. But for them, each failure is an "increasing evidence".

[f_klem]

You can start by reading What Computers still can't do, by Hubert Dreyfus. Sorry to repeat myself: this books explains the assumptions the AI research programme is based on, and why they are problematic. It also references evidence. It also references claims from AI researchers (among others, Minsky), that were unfounded. Is the book still relevant today? yes, it is. Why? because the assumptions work at a fundamental level.

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 <<< I am not convinced by his position, but it is nonetheless relevant since it splits the debate in two: intention and consciousness

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

[LudwigNagasena]

> But going back to my initial comment, this whole thread feels like proving my point.

Ok, but look at this thread from the POV of someone like me, who reads lots of philosophy daily and who asks you to simply elaborate on your point, and you just continuously keep deflecting from providing an actual argument.

Even in this comment instead of providing an actual philosophically grounded argument based on all those literally great thinkers many of whom I’ve read with great passion you waste your energy on name dropping things that don’t directly support your initial thesis in any way and then you do some meta-commentary on the impossibility of discussing those issues that you can’t even clearly articulate for several days.