Checked claims by Gary Marcus, page 20 of 20
Deadlines are judged as of 2026-10-31. About Gary Marcus and the sources covered.
Checked claims by Gary Marcus (1,931)
Claims we checked against evidence.
Claims 1,901 to 1,931 of 1,931, newest first. Page 20 of 20.
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“Even within perception 3D perception remains a challenge, and scene understanding is by no means solved.”
What happened: 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, )
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 happened: 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, )
Assessment: UnresolvedSaid
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 held: 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 did not: 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, )
Assessment: UnresolvedSaid
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 happened: 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, )
Assessment: UnresolvedSaid
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 happened: 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, )
Assessment: SupportedSaid
“That’s where the deep understanding of language should live—but, despite the PR, it isn’t there.”
What happened: 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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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 happened: 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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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 happened: 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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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 happened: 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, )
Assessment: UnresolvedSaid
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 happened: 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, )
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 happened: 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, )