Name: What computers still can't do
ISBN: 9780262540674

What computers still can't do

When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field. At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.

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f_klemJun 2026

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edited books, and no, there is no real reason to think that because they are all +30 years old or even more, they are outdated.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

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

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max_Jan 2024

The best books I would recommend are;

— The Master Algorithm by Pedro Domingos

— The Book of Why by Judea Pearl

— What Computers Still Can't Do by Hubert L. Dreyfus

— Society of Mind & The Emotion Machine by Marvin Minsky

— The Feeling Of What Happens by Antonio Damasio

— Language & Mind by Noam Chomsky

— A great mooc is also, the "Learning From Data" Telecourse from Caltech https://work.caltech.edu/telecourse

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cratermoonOct 2021

The gold standard for thinking about the limits of AI is the work of Herbert Dreyfus, specifically the books What Computers Can't Do: The Limits of Artificial Intelligence published in 1972, and the revised What Computers Still Can't Do: A Critique of Artificial Reason, published in 1992, and Mind over Machine, from 1986.

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AndrewOMartinSep 2017

I've clearly not mentioned What Computers Still Can't Do in comments enough to make What Computers Still Can't Do appear on this list, which is a shame as I love What Computers Still Can't Do, and What Computers Still Can't Do is very relevant to this community of programmers with much technical skill, but little philosophical background.

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swyxSep 2017

that's amazing, tell me more about What Computers Still Can't Do!

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AndrewOMartinSep 2016

Yes.

The critiques of AI from Hubert Dreyfus have stood the test of time, those who want to understand or challenge them directly can read What Computers Can't Do (1972, 1979), or even better the updated reprint What Computers Still Can't Do (1992). He's a Heideggarian Philosopher but all you need to know is that modern AI is ignorant of vast swathes of 20th Century investigation into the human mind, and state of being.

Hence, I think the big idea you're talking about is in AI that takes Hubert Dreyfus's critiques seriously.

Luckily for anyone reading about this for the first time, that process has already started. Dreyfus wrote a 2007 paper on the successes and failures of the first few steps of what he called Heideggarian AI with the snappy name of "Why Heideggerian AI failed and how fixing it would require making it more Heideggerian".

The "fixing" refers to work of a Neuroanatomist, with a suitable philosophical background, called Walter Freeman III, and is broadly described in the paper, but properly investigated in Freeman's (also eminently readable) book How Brains Make Up Their Minds (2000).

A note of caution, you'll be introduced to concepts that blur the line between body and environment, subject and object, intention and influence, and eventually things like relinquising your belief in causality and an objective "out there" universe (or at least any value in such a belief), all whilst staying perfectly scientific and evidence based.

Finally, bear in mind that if we do create an intelligence worthy of the name, we have reason to believe it will take about 18 years of "raising" by two adult humans, after which it will want to do its own thing, and not your dumb image classification tasks.

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