Metaphors We Live By

What Hacker News says

1. DotnaughtSep 2026
   See George Lakoff/Mark Johnson, Metaphors We Live By (1980):

   https://george-lakoff.com/books/metaphors-we-live-by/

   Took a course with Lakoff as an undergrad and it was compelling.

   thread
2. HarHarVeryFunnySep 2026
   I watched this Hofstader video last night, and have to say the content was very disappointing and a waste of time. It doesn't help that Hofstader is a very poor speaker. All we get in this video is a rambling set of example analogies.

   The 2006 date of this talk precedes his "Surfaces and Essences: Analogy as the Fuel and Fire of Thinking" book where he really makes the claim of analogies being the basis of cognition. I haven't read "Essences", but the major criticism of it is that it was also mostly just endless examples of analogies, not delivering on the claim of the title.

   Hofstader's most famous book by far was GEB, which in retrospect seems it achieved it's cult status more for it's sprawling scope and intellectual appeal rather than for any coherent exposition or explanation of its ambitious subject matter. It was a book that felt deep and clever, but really was never more than a bunch of interesting talking points, only loosely related.

   Still, having read GEB I had high hopes for his "Fluid Concepts and Creative Analogies" which came 15 years later in 1995, which one might have expected to be equally ambitious in scope and breadth of ideas, but it turned out to be his least popular book, and was more of a narrow discussion on some of his GOFAI programs such as "Copycat" trying to at least demonstrate the use of analogy as a computational mechanism, if not one related to the brain.

   I'm reminded now why I never bothered to read his final book "Surfaces and Essences", and reading the reviews of that confirms what anyone who had read "Fluid Concepts", or watched this video, might have suspected - that he was basically out of ideas.

   For anyone interested in analogies and their role in language and thought, a book that has better stood the test of time is George Lakoff's "Metaphors We Live By".

   Show morethread
3. 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

   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

   Show morethread
4. OhMeadhbhMar 2026
   I recently read one of the best descriptions of why middle of the road, non wealthy voters went for Trump in the book "The King in Orange," a book about the "magickal" aspects of the 2016 campaign by John Michael Greer, the former (?) head of the Ancient Order of Druids in America.

   I expect cogent commentary about ritual magick by a Druid, but was a little surprised to find well laid out political commentary. I guess that was a failure of my imagination. Worth a read, even if you consider the topic bollocks. Greer sticks mostly to psychology and musings about using metaphor to engineer the mass imagination. Much less woo-woo than you might expect.

   I mention it in support of the previous poster's commentary about the Dems messaging being irrelevant to most Americans. Seemed to me middle America doesn't love Trump as much as they weren't able to hear Harris address any issues they were concerned about.

   I can recommend The King in Orange, What's the Matter with Kansas and Metaphors We Live By for more musings about such things.

   Show morethread
5. kensSep 2025
   I highly recommend the book "Metaphors We Live By", which discusses how metaphors are not arbitrary, but are part of schemas. For instance, there are whole classes of orientational metaphors that fall into the schemas: "more is up, less is down", "good is up, bad is down", "virtue is up, depravity is down", "rational is up, emotional is down", "having control is up, being subject to control is down", and so on. (Yes, I'm sure you're clever enough to find counterexamples.) This is a thought-provoking book that changed how I view the world, so check it out.

   The book: https://archive.org/details/lakoff-george-metaphors-we-live-...

   Norvig's review discussing the book in the context of AI: https://norvig.com/mwlb.html

   Show morethread
6. willemlaurentzAug 2025
   As a metaphor aficionado (book tip: Metaphors we live by), I figured random images would be a lovely way to allow visitors of willem.com to discover stories. Before I knew it, I was in the rabbit hole. Released under AGPL (backstory: https://willem.com/en/2025-08-11_willemstagram/)
   thread