Pundit ClaimsAI claims by Gary Marcus and Ed Zitron, with the evidence

Checked claims by Gary Marcus, page 21 of 21

Deadlines are judged as of 2026-11-11. About Gary Marcus and the sources covered.

Checked claims by Gary Marcus (2,045)

Claims we checked against evidence.

Claims 2,001 to 2,045 of 2,045, newest first. Page 21 of 21.

  1. Assessment: SupportedSaid

    “I would be very surprised if that fundamental lack of reliability did not also hold true for GPT-4. Maybe it will take three paragraphs in a prompt rather than one to break it, but a big Transformer alone does not a a theory of the world create.”

    What supported it: OpenAI's launch-era report explicitly acknowledges both predicted failure classes and advises safeguards where reliability matters. It also reports substantial factuality improvement, which is compatible with Marcus's allowance for fewer errors. (OpenAI / arXiv, )

  2. Assessment: UnresolvedSaid

    “Large language models are (unless hybridized with symbolic systems, a la Jurassic X) are, on their own, inherently weak at understanding the physical and psychological world.”

  3. Assessment: UnresolvedSaid

    “Interestingly, as Ernie has just discovered, across the full set of our Technology Review questions the difference from GPT-3’s earlier version to the current version (InstructGPT) is far more modest than one might have expected: fewer outright wacky answers, some old answers now correct, but some backsliding, too.”

  4. Assessment: UnresolvedSaid

    “The core claims that Ernie and I made in Technology Review still all, so far as I can tell, hold true, nearly two years and many even larger models later. Yes, some of the specific examples we pointed out have been remedied, but the general problems have not.”

  5. Assessment: UnresolvedSaid

    “But they still don’t convey genuine comprehension, and so they are still very easy for Ernie and me (or anyone else who cares to try) to break.”

  6. Assessment: UnresolvedSaid

    “I am willing to bet SlateStarCodex now (terms to be negotiated) that if OpenAI gives us unrestricted access to GPT-4, whenever that is released, and assuming that is basically the same architecture but with more data, that within a day of playing around with it, Ernie and I will still be able find lots of examples of failures in physical reasoning, temporal reasoning, causal reasoning, and so forth.”

  7. Assessment: UnresolvedSaid

    “Now it is true that GPT-3 is genuinely better than GPT-2, and maybe (but maybe not, see footnote 1) true that InstructGPT is genuinely better than GPT-3. I do think that for any given example, the probability of a correct answer has gone up.”

  8. Assessment: UnresolvedSaid

    “The improvements, such as they are, come, primarily because the newer models have larger and larger sets of data about how human beings use word sequences, and bigger word sequences are certainly helpful for pattern matching machines.”

  9. Assessment: UnresolvedSaid

    “The fourth thing to realize is that we, and by this I mean humanity as a whole, still don’t really have an adequate methodology for building complex cognitive systems.”

  10. Assessment: UnresolvedSaid

    “In 2029, AI will not be able to reliably construct bug-free code of more than 10,000 lines from natural language specification or by interactions with a non-expert user.”

  11. Assessment: SupportedSaid

    “You said, for instance in 2015, that (truly) self-driving cars were two years away; you’ve pretty much said the same thing every year since. It still hasn’t happened.”

    What supported it: The original interview establishes the attributed prediction. Musk separately allowed additional time for regulatory approval, so the prediction concerned technical capability first. (Fortune, )

  12. Assessment: UnresolvedSaid

    “I don’t know what you have planned for Optimus, but I can promise you that the AGI that you would need for a general-purpose domestic robot (where every home is different, and each poses its own safety risks) is way beyond what you would need for a car that drives on roads that are more or less engineered the same way from one town to the next.”

  13. Assessment: UnresolvedSaid

    “In 2029, AI will not be able to read a novel and reliably answer questions about plot, character, conflicts, motivations, etc.”

  14. Assessment: UnresolvedSaid

    “We still don’t have anything like stable or trustworthy solutions for common sense, reasoning, language, or analogy. Truth is, I have been using this same pie chart for about 5 years, and the situation has scarcely changed.”

  15. Assessment: UnresolvedSaid

    “Deep learning is pretty decent at, for example, recognizing objects, but not nearly as good at planning, or reading, or language comprehension.”

  16. Assessment: UnresolvedSaid

    “In 2029, AI will not be able to take arbitrary proofs from the mathematical literature written in natural language and convert them into a symbolic form suitable for symbolic verification.”

  17. Assessment: SupportedSaid

    “Even within perception 3D perception remains a challenge, and scene understanding is by no means solved.”

    What supported it: Testing fifteen common corruptions exposed substantial robustness weaknesses in leading 3D recognition models. The authors also report mitigation methods, so the result establishes remaining challenges rather than an absence of progress. (arXiv; Jiachen Sun and colleagues, )

  18. Assessment: SupportedSaid

    “But for the kinds of AI techniques that are currently popular, and which rely more on big data than on reasoning, it’s a very serious issue.”

    What supported it: The contemporary synthesis documents poor learning of rare classes and substantial limitations in conventional deep-learning methods. It also reviews mitigation methods, supporting a serious practical problem without implying that every method fails. (International Journal of Computer Vision; author manuscript rendered by ar5iv, )

  19. Assessment: UnresolvedSaid

    “In 2029, AI will not be able to work as a competent cook in an arbitrary kitchen (extending Steve Wozniak’s cup of coffee benchmark).”

  20. Assessment: MixedSaid

    “Debugging with deep learning is wicked hard, because nobody (a) really understands how it works, and (b) knows how to fix problems other to collect more data and add more layers and so forth.”

    What supported part of it: The survey supports the narrower causal point that opacity makes locating and repairing problems harder. Its review of explanation-based debugging also shows that researchers were already developing remedies. (arXiv; Piyawat Lertvittayakumjorn and Francesca Toni, )

    What contradicted part of it: The authors used causal interventions to investigate factual recall and evaluated targeted factual edits on CounterFact. This supplies a concrete pre-post example of useful mechanistic understanding and repair without adding data or layers. (arXiv; Kevin Meng, David Bau, Alex Andonian and Yonatan Belinkov, )

  21. Assessment: UnresolvedSaid

    “The thing is, it’s easy to convince yourself that AI problems are much easier than they are actually are, because of the long tail problem.”

  22. Assessment: SupportedSaid

    “And it’s why I absolutely guarantee you won’t be shipping level 5 self-driving cars this year or next, no matter what you might have told Chris Anderson at TED.”

    What supported it: The annual filing describes Tesla's offerings as driver assistance and says the driver remains responsible for controlling the vehicle. That capability does not meet Level 5. (Tesla, Inc., mirrored by AnnualReports.com, )

  23. Assessment: UnresolvedSaid

    “In 2029, AI will not be able to watch a movie and tell you accurately what is going on (what I called the comprehension challenge in The New Yorker, in 2014).”

  24. Assessment: SupportedSaid

    “But you and I both know that they continue to be a major problem that as yet has no known robust solution.”

    What supported it: The survey identifies unresolved obstacles involving graph structure, differing tasks, and different kinds of distribution shift. It also describes progress, supporting the distinction between useful remedies and a general solution. (arXiv; Haoyang Li and colleagues, )

  25. Assessment: SupportedSaid

    “That’s where the deep understanding of language should live—but, despite the PR, it isn’t there.”

    What supported it: The original authors identify a category-level alignment weakness in the figure containing the horse-riding-an-astronaut prompt. This supports the contextual reading that reliable compositional instruction understanding was missing. (Google Research, Brain Team; copy hosted by 3DVAR, )

  26. Assessment: UnresolvedSaid

    “As Ernie Davis, Scott Aaronson, and I showed, a week or so after the release, it’s easily fooled, especially with complex sentences and words like “similar” that would require a deep understanding of language to be interpreted correctly”

  27. Assessment: UnresolvedSaid

    “Since we know that Imagen can draw images of horses riding astronauts (if the instructions are specific enough) we know that the failure to draw a horse riding an astronaut given the prompt “a horse riding an astronaut” can only have one explanation: the system doesn’t understand what the prompt “a horse riding an astronaut” means.”

  28. Assessment: UnresolvedSaid

    “And, no, it wasn’t about common sense at all.”

  29. Assessment: UnresolvedSaid

    “The problem was not on the image generation side, it was in the connection between language and images.”

  30. Assessment: UnresolvedSaid

    “(In reality, Imagen has no idea what weight, mass, or density are.)”

  31. Assessment: UnresolvedSaid

    “What one really needs, and no one yet knows how to build, is a system that can derive semantics of wholes from their parts as a function of their syntax.”

  32. Assessment: UnresolvedSaid

    “Furthermore, there is no step in the process to try to derive such structures; so it is just wishful thinking to expect them to be fully sensitive to semantics as it relates to syntactic structure.”

  33. Assessment: ContradictedSaid

    “They do not gravitate towards human language, and they never develop a cohesive undestanding of the world, no matter how much experience they have.”

    What contradicted it: Kallini and colleagues compared GPT-2 learning across English and systematically altered languages, including different training stages. The observed preference conflicts with the language-learning component under the stated family-level interpretation. (arXiv, )

  34. Assessment: UnresolvedSaid

    “There is no intention to create misinformation—but also no capacity to avoid it, because fundamentally, GPT-3 is a model of how words relate to one another, not a model of how language might relate to the perceived world.”

  35. Assessment: UnresolvedSaid

    “Virtually none of the critical problems for AI that cognitive scientists such as Steve Pinker, Judea Pearl, the late Jerry Fodor, and myself have been pointing to for decades is actually solved yet.”

  36. Assessment: UnresolvedSaid

    “The bottom line is this, something that AI once cherished but has now forgotten: If we are to build AGI, we are going to need to learn something from humans, how they reason and understand the physical world, and how they represent and acquire language and complex concepts.”

  37. Assessment: UnresolvedSaid

    “Systems like DALL-E 2, GPT-3, Flamingo, and Gato appear to be mind-blowing, but nobody who has looked at them carefully would confuse them for human intelligence.”

  38. Assessment: SupportedSaid

    “Success on a few problems with a particular strategy in no way guarantees that we can solve all problems in a similar way. It is sheer inductive folly to imagine otherwise, particular when the failure modes (unreliability, bizarre errors, failures in compositionality and discomprehension) have not changed since Fodor and Pinker pointed them out (in separate articles) in 1988.”

    What supported it: Tests involving SCAN and RobustFill found continuing difficulty combining learned components in new ways, even with modified Transformers. This supports persistence of compositional generalization problems while explicitly acknowledging progress. (arXiv; Kensen Shi and colleagues, )

  39. Assessment: UnresolvedSaid

    “Virtually all of the world’s software, from web browsers to spreadsheets to word processors, still depends on knowledge engineering, and Sutton sells that short.”

  40. Assessment: UnresolvedSaid

    “So far, in most domains (not all, to be sure) Big Data has triumphed (for now) over careful knowledge engineering.”

  41. Assessment: UnresolvedSaid

    “As I have written, I am fine with thinking of Gato as an “Alt Intelligence”— an interesting exploration in alternative ways to build intelligence—but we need to take it in context: it doesn’t work like the brain, it doesn’t learn like a child, it doesn’t understand language, it doesn’t align with human values, and it can’t be trusted with mission-critical tasks.”

  42. Assessment: SupportedSaid

    “DeepMind’s newest star, just unveiled, Gato, is capable of cross-modal feats never seen before in AI, but still, when you look in the fine print, remains stuck in the same land of unreliability, moments of brilliance coupled with absolute discomprehension:”

    What supported it: The launch paper reports one model spanning dialogue, captions, games and real robot control, including human-level or better performance on 23 Atari games, while acknowledging frequent poor dialogue responses. Its prior-work comparison supports novelty in that combined coverage, rather than novelty of multimodality itself. (arXiv; DeepMind, )

  43. Assessment: UnresolvedSaid

    “Right now, the predominant strand of work within Alt Intelligence is the idea of scaling.”

  44. Assessment: SupportedSaid

    “But it’s also pretty prone to surprising stumbles, like this uncomprehending reply to “a red cube on top of a blue cube”.”

    What supported it: The authors discuss the exact red-cube/blue-cube prompt in Figure 14 and report recurring object–attribute mix-ups in reconstructions in Figure 15. This supports a recurring compositional weakness in the original model, without establishing a population-wide error rate. (arXiv; OpenAI, )

  45. Assessment: SupportedSaid

    “The history missed is the the fact that many neural net enthusiasts, have argued against symbols for decades, and the reality that is missed is the above-documented fact that symbolic descriptions like “red cube on blue cube” still elude the state-of-the-art in 2022.)”

    What supported it: This dated scholarly account documents an explicit anti-symbolic strand well before the post. It is historical synthesis rather than a primary statement by an advocate. (Stanford Encyclopedia of Philosophy, )