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

Claims checked against evidence by Gary Marcus, page 12 of 19

Deadlines are judged as of 2026-10-29. About Gary Marcus and the sources covered.

Claims checked against evidence by Gary Marcus (1870)

Each of these claims was checked against dated evidence.

Page 12 of 19: claims 1101 to 1200 of 1870, ordered by the date first said, newest first. To find a claim by its words, use search.

  1. Assessment: SupportedSaid

    “OpenAI has trained on a massive amount of copyrighted material, without consent, and in many instances without compensation.”

    What happened: The section documents web and book corpora, including roughly 400 billion tokens of filtered Common Crawl data. It establishes training scale, but does not itself establish the copyright or licensing status of every work. (OpenAI researchers, published on arXiv, )

  2. Assessment: MixedSaid

    “OpenAI proceeded to make a Scarlett Johansson-like voice for GPT-4o, even after she specifically told them not to, behaving in an entitled fashion that has angered many—highlighting their overall dismissive attitude towards artist consent.”

    What held: The report supports recognizable resemblance in the public demonstration. It does not establish cloning or deliberate imitation. (NPR, carried by Iowa Public Radio, )

    What did not: OpenAI dates recording to June and July 2023 and Johansson’s first approach and refusal to September. This contradicts creation of the original Sky voice after her refusal, although deployment followed it. (OpenAI, )

  3. Assessment: UnresolvedSaid

    “By clustering piles of similar things together in a giant n-dimensional space, they pretty much automatically become synonym and pastische machines, regurgitating a lot of words with slight paraphrases while adding conceptually little, and understanding even less.”

  4. Assessment: SupportedSaid

    “What the LLM does is more akin to what some high school students do when they plagiarize: change a few words here or there, while still sticking close to the original.”

    What happened: The article dates the shared screenshot to May 23 and transcribes the generated sentence. Compared with the reproduced source in e8, it retains the quantity, glue-in-sauce recommendation, and adhesive purpose with changed wording. (Hindustan Times, )

  5. Assessment: SupportedSaid

    “Many people are reaching essential the same perceptions.”

    What happened: Merchant's own commentary criticizes Altman's conduct as unusually entitled and overbearing. It is direct evidence of his perception, not independent verification of the allegations he discusses. (Blood in the Machine — Brian Merchant, )

  6. Assessment: SupportedSaid

    “In November, the narrative was about how everybody loved Sam. Things have changed. A lot.”

    What happened: WIRED reported that roughly 95% of employees had signed the letter supporting his return. This documents the prominent November narrative of overwhelming workplace support, although it does not measure public affection. (WIRED, )

  7. Assessment: UnresolvedSaid

    “It’s a pattern.”

  8. Assessment: UnresolvedSaid

    “I probably don’t have to tell you, but that’s complete bullshit. And stupid, obviously refuted bullshit at that.”

  9. Assessment: UnresolvedSaid

    “They said it wasn’t intentional, but of course it was.”

  10. Assessment: UnresolvedSaid

    “I can’t tell you what happened to Sam’s caps lock, but obviously the claim that the resemblance to ScarJo was a “coincidence” was a lie. Sam knew perfectly well what the character sounded like.”

  11. Assessment: SupportedSaid

    “I shared some of my own thoughts about which Pandora’s Boxes had (LLMs) and had not (AGI) already been opened.”

    What happened: Meta's release announcement offered downloadable Llama 3 models and described access through its public assistant. This establishes public availability before the post. (Meta, )

  12. Assessment: UnresolvedSaid

    “Lots of quirky errors are already being reported, same as ever, like this reasoning error from Jane Rosenzweig:”

  13. Assessment: UnresolvedSaid

    “They don’t have GPT-5 after 14 months of trying.”

  14. Assessment: UnresolvedSaid

    “It has a right to about 49% of a for-profit subsidiary of OpenAI’s profits, up until a very complex point that may require litigation to resolve, but the for-profit hasn’t made any profits, and the for-profit is owned by a non-profit.”

  15. Assessment: SupportedSaid

    “You might think that if OpenAI said, hey, we don’t want our software used for military purposes, Microsoft would respect that and say, ok cool, but they haven’t.”

    What happened: The report places this Microsoft presentation at an October 2023 Defense Department seminar, when OpenAI's policy prohibited military use. Microsoft acknowledged the proposed application but said it had not begun implementing it. (The Intercept, )

  16. Assessment: SupportedSaid

    “Playing the field with Mistral wasn’t enough; now he is building a really big model, big enough to compete directly with GPT-4, internally:”

    What happened: The original article identifies MAI-1 as Microsoft's internal project and describes its intended competitive class. Its underlying source is The Information, rather than an independent technical evaluation. (The Decoder, )

  17. Assessment: SupportedSaid

    “You might think that after Satya offered to rescue Sam after Sam got fired, that Sam wouldn’t want to compete directly with Microsoft, but they are.”

    What happened: The reported pitches present OpenAI's enterprise offering as an alternative purchase for existing Microsoft customers. That satisfies the ordinary commercial meaning of direct competition. (Reuters, republished by Yahoo Finance, )

  18. Assessment: UnresolvedSaid

    “Usership for ChatGPT rose like a rocket until May 2023 but has more or less stabilized since then without significant further growth.”

  19. Assessment: UnresolvedSaid

    “He seems like such a nice guy, yet he implies, unrealistically, that the solution to AGI is within his grasp; he presents no evidence that is so, and rarely considers the many critiques of current approaches that have been raised.”

  20. Assessment: UnresolvedSaid

    “So far, there have been no net profits, and as noted above if there are any, for a long time roughly half must be shared with Microsoft, by contractual agreement.”

  21. Assessment: SupportedSaid

    “People who buy Altman’s narrative will continue to invest.”

    What happened: SoftBank explicitly endorses OpenAI's role in achieving advanced general intelligence. This establishes the belief condition under the stated public-endorsement interpretation. (SoftBank Group, )

  22. Assessment: UnresolvedSaid

    “... but in reality mostly just sells chatbots. Charitable work is at best a small part of the overall operation.”

  23. Assessment: MixedSaid

    “In the last year, his company has released no major update to GPT-4, and many competitors have caught up or are close. Competitors are eroding the initial gap in quality and starting to offer similar products, sometimes at significantly lower prices.”

    What held: The release documents overlapping multimodal chat and API products and reports strong Opus benchmark performance. Sonnet's published input and output token charges were below GPT-4 Turbo's corresponding charges in e1, although Opus was more expensive and Sonnet was a lower capability tier. (Anthropic, )

    What did not: The announcement documents an accessible GPT-4 Turbo preview with 128K context, improved instruction following, JSON mode and image-input support in the API. These constitute a substantial update under the stated reading of major. (OpenAI, )

  24. Assessment: UnresolvedSaid

    “But wait, if he really believes in basic income for all, why is he fleecing artists, writers, and other creators, not paying a nickel (to most) for training materials? A lot of them are losing gigs.”

  25. Assessment: SupportedSaid

    “At the same talk at Stanford, Altman grandoisely asserted, with no qualification, that “we’re making AGI”, without touching on the possibility that current approaches might not get us there — despite (a) the enormous known problems with reliability, reasoning, planning and hallucinations and (b) the fact that the entire field, despite being massively well-capitalized, has struggled for over year (and in fact for decades) to solve any of these problems.”

    What happened: OpenAI explicitly acknowledged three named limitations more than a year before Marcus's post. The same section also reports reduced hallucination and improved factuality, so it supports unresolved problems rather than an absence of progress. (OpenAI, )

  26. Assessment: UnresolvedSaid

    “LLMs confined to X tweets will be even less grounded in reality.”

  27. Assessment: UnresolvedSaid

    “I grew more and more frustrated that when it came to AI, we just weren’t going to get the legislation we needed.”

  28. Assessment: ContradictedSaid

    “Over a year ago in The Atlantic, I urged that we come up with laws and new technological responses to blunt the impact of AI-generated misinformation. So far almost nothing has been done.”

    What happened: The ruling applied existing consent and disclosure protections to AI-generated voice calls and became effective on release. It was a concrete federal response relevant to deceptive impersonation calls, subject to statutory exemptions. (Federal Communications Commission, )

  29. Assessment: SupportedSaid

    “The very process that LLMs use to generalize also creates hallucinations.”

    What happened: Hallucination follows from statistical prediction itself, even with ideal data. That matches the claim that the generalizing process creates hallucinations. (arXiv / STOC 2024 (Kalai and Vempala), )

  30. Assessment: MixedSaid

    “Instead, everything that LLMs say -- true or false -- comes from the same process of statistically reconstructing what words are likely in some context. They NEVER fact-check what they say.”

    What held: This supports the first part. The leading LLM generates text by predicting likely next tokens. The report also notes post-training, which shapes but does not replace that process. (OpenAI (arXiv), )

    What did not: From September 2023, Google's deployed LLM product, at the user's request, checked its own answers against web content. This contradicts 'NEVER fact-check' as applied to LLM products. (Google, )

  31. Assessment: MixedSaid

    “Instead, LLM "hallucinations" arise, regularly, because (a) they literally don't know the difference between truth and falsehood, (b) they don't have reliably reasoning processes to guarantee that their inferences are correct and (c) they are incapable of fact-checking their own work.”

    What held: In the reported intrinsic-correction experiments, models had to judge their answers without ground-truth feedback, and accuracy fell. This supports unreliable reasoning and self-correction in those settings, not universal incapacity or the full explanation of factual hallucinations. (arXiv; Huang et al., )

    What did not: The model generated verification questions, answered them using the model itself without external tools, and revised its response. The reported improvements contradict categorical inability under the stated behavioral reading, while leaving substantial errors. (arXiv; Dhuliawala et al., )

  32. Assessment: UnresolvedSaid

    “That means, of course, that OpenAI’s business model is threatened; same for Anthropic’s, Mistral’s, and so on.”

  33. Assessment: UnresolvedSaid

    “Whatever tiny moat there was between the companies will further diminish.”

  34. Assessment: SupportedSaid

    “LLMs are well on their way to becoming a commodity; price wars will be intense.”

    What happened: Reuters reports Alibaba's steep cuts, Baidu making two models free hours later, and ByteDance's earlier aggressive pricing. This directly establishes competitive price cutting rather than merely cheaper computation; reputable reporting is used for the response sequence. (Reuters, republished by Yahoo Finance, )

  35. Assessment: UnresolvedSaid

    “A billion robots with a superficial understanding of the physical world would be a recipe for disaster.”

  36. Assessment: UnresolvedSaid

    “Khosla is a venture capitalist, and I think venture capitalists will have a huge role in whether or not we get to the promised land.”

  37. Assessment: UnresolvedSaid

    “Microsoft’s Mustafa Suleyman promised that hallucinations would be cured “soon”, yet my X feed is still filled with examples like these from Princeton Professor Aleksandra Korolova:”

  38. Assessment: UnresolvedSaid

    “Policies around transparency, auditing and interpretability might not be as sexy as alien intelligences, but without it, we will see more and more public resistance to AI.”

  39. Assessment: UnresolvedSaid

    “We won’t get to a billion personal robots if they are as dodgy as driverless cars, frequently working, yet stymied often enough by outliers that we can’t fully count on them.”

  40. Assessment: SupportedSaid

    “Adding visual data to LLMs hasn’t solved hallucinations.”

    What happened: The developer of GPT-4V, the main model that added vision to an LLM, reports that it was still prone to hallucinations. (OpenAI, )

  41. Assessment: SupportedSaid

    “I also urged the world to focus on global AI governance, and am pleased to see some genuine progress in that direction, especially led by the UN and the Council of Europe.”

    What happened: The UN's own account records adoption without a vote of an AI resolution addressing human rights and governance frameworks. Its broad international backing establishes a concrete multilateral milestone. (United Nations in Türkiye, )

  42. Assessment: SupportedSaid

    “Yejin Choi worried that LLMs were not reliable. They still aren’t.”

    What happened: The March evaluation covers thirteen models from the Gemini, GPT, Claude and PaLM-2 families and documents continuing factuality limitations. It also finds better performance among larger models, so it supports continued unreliability without supporting stagnation. (arXiv; Jerry Wei and colleagues, )

  43. Assessment: UnresolvedSaid

    “I think it’s fair to say that the multimodal stuff still is very buggy, having troubles with basics like the English alphabet.”

  44. Assessment: SupportedSaid

    “The Humane AI pin/virtual assistant just doesn’t work well, and a lot of the problem is the underlying AI, which I believe is powered by OpenAI.”

    What happened: The launch review documents a false answer about a California food ban and a mistaken temple identification. These failures directly impair the question-answering function, although the review also identifies missing features and hardware problems. (WIRED, )

  45. Assessment: UnresolvedSaid

    “As I put it to the Times, no company yet had A.I. technology that was sophisticated enough to make a virtual assistant answer questions reliably,”

  46. Assessment: UnresolvedSaid

    “I warned that LLMs would be used by bad actors, and they increasingly are.”

  47. Assessment: SupportedSaid

    “Eliezer Yudkowsky warned that AI was (inevitably!) going to kill us all. I am happy to report that so far it hasn’t.”

    What happened: The governing body's report documents living humans competing in the Boston Marathon on April 15. Their existence logically establishes that AI had not killed all humanity by then. (World Athletics, )

  48. Assessment: UnresolvedSaid

    “I am just not seeing that as OpenAI’s mission, anymore, especially when they have continued to fail to compensate most of the creators whose work they are drawing on, largely without consent.”

  49. Assessment: MixedSaid

    “The study Mollick linked to doesn’t actually show what he claims. If you read it carefully, it says literally nothing about capabilities improving.”

    What held: The study measures efficiency at a given performance level. Its compute-halving estimate does not establish that capabilities themselves doubled at the same rate. (Anson Ho and colleagues, arXiv, )

    What did not: The authors explicitly describe an analysis of improving capabilities. Their summary also explains the study's decomposition of performance gains into compute scaling and algorithmic contributions, contradicting the assertion that it addresses only efficiency. (Epoch AI, study authors, )

  50. Assessment: SupportedSaid

    “What it actually shows is some improvements, mostly modest, over the last few months for a bunch of different measures.”

    What happened: In the assistant configuration, April-minus-January changes are 4.5 DROP F1 points and 7.9, 8.9, 4.5, 1.3, and 1.6 percentage points on GPQA, MATH, MGSM, MMLU, and HumanEval. The ChatGPT configuration likewise has small changes on most measures, with larger GPQA and MATH gains and a slight HumanEval decline. (OpenAI, )

  51. Assessment: UnresolvedSaid

    “Another way to think about this (see third graph above) is that there about 5 - 7 recent models are on a par with GPT-4, but none is clearly and decisively ahead.”

  52. Assessment: SupportedSaid

    “I think it’s fair to say that they have not. GPT-Turbo released this week still produces its share of groaners, like this interchange Phil Libin just sent:”

    What happened: The tests used the newly released snapshot on 133 Python exercises, allowing two attempts. Unsuccessful tasks demonstrate that coding errors persisted; this is not a measurement of factual hallucinations. (Aider, )

  53. Assessment: UnresolvedSaid

    “One more way to look at this, is this graph I just saw: enormous convergence on GPT-4 level performance, in multiple models released since, yet nothing decisively ahead.”

  54. Assessment: UnresolvedSaid

    “If enthusiasm for GenAI dwindles and market valuations plummet, AI won’t disappear, and LLMs won’t disappear; they will still have their place as tools for statistical approximation.”

  55. Assessment: SupportedSaid

    “Already in recent weeks, Inflection AI largely closed shop, Stability AI is struggling, and LLM-based autonomous vehicle company called Ghost closed shop, and a software engineer on YouTube raised quite serious questions about the wildly-hyped AI coding system Devin.”

    What happened: The regulator found that almost all of the team transferred, including its core expertise. This supports substantial dismantling of the original organization, under the stated interpretation. (UK Competition and Markets Authority, )

  56. Assessment: SupportedSaid

    “Huge jump from GPT-2 to GPT-3. Huge jump from GPT-3 to GPT-4 … and not so much for GPT-4 (13 months ago) to GPT-4 Turbo (just released). It’s hard not to see this plot as tentative evidence for the hypothesis of diminishing returns.”

    What happened: Table 1 reports 32.4% for fine-tuned GPT-2 and 43.9% for GPT-3 X-Large evaluated few-shot, a reported increase of 11.5 percentage points. The different evaluation methods prevent treating this as a controlled comparison of unchanged model preparation. (Dan Hendrycks and coauthors, arXiv, )

  57. Assessment: UnresolvedSaid

    “I couldn’t agree more. And parallel arguments could be made in many domains.”

  58. Assessment: SupportedSaid

    “Skeptical of this, I offered Musk a $1 million bet that AGI superior to the smartest individual humans would not arrive by the end of 2025, reprinted here for those who missed it.”

    What happened: The retrospective report says every ARC-AGI-2 task was solved by at least two independent non-expert human testers. Its analysis identifies continuing generalization limitations in 2025 AI systems. (ARC Prize Foundation, via arXiv, )

  59. Assessment: SupportedSaid

    “Tesla FSD is getting better. But is getting enough better?”

    What happened: Crowdsourced tracker data as of early April 2024 showed v12.3 with far more miles between critical disengagements than v11 or v10, a measurable improvement just before the post. (NextBigFuture, )

  60. Assessment: SupportedSaid

    “current models aren’t literally look up tables; they can generalize to some degree, but not enough.”

    What happened: The study evaluates 25 LLMs and four prompting techniques. It reports resilience to some numerical and wording changes alongside substantial failures under other mathematical perturbations, supporting the contextual claim of partial but unreliable generalization. (arXiv; Li and colleagues, )

  61. Assessment: ContradictedSaid

    “This is why driverless cars are still just demos, and why LLMs will never be reliable.”

    What happened: The filing describes daily public driverless rides for work, school, errands and airport travel, and identifies Waymo One as a deployed commercial service. Those operations contradict the demonstration-only premise. (Waymo LLC; filed with the California Public Utilities Commission, )

  62. Assessment: UnresolvedSaid

    “It also drives their greediness with data, and even the ethical choices their developers have been making.”

  63. Assessment: SupportedSaid

    “Neural networks (at least in the configurations that have been dominant over the last three decades) have trouble generalizing beyond the multidimensional space that surrounds their training examples.”

    What happened: WILDS reports performance losses across ten datasets involving natural distribution shifts. This supports recurring generalization difficulty across applications, without establishing a universal inability. (arXiv; Koh and colleagues, )

  64. Assessment: UnresolvedSaid

    “In short, diminishing returns are what we can expect.”

  65. Assessment: UnresolvedSaid

    “Sooner or later, the exponential greed for data will exceed what is available.”

  66. Assessment: UnresolvedSaid

    “an intellectual monoculture — with no alternative approach that is nearly as well-funded.”

  67. Assessment: SupportedSaid

    “The good news is that word is finally getting out more broadly.”

    What happened: A major mainstream body said AI-driven misinformation dominated its 2024 risk outlook, three months before the post. That shows the concern had reached the mainstream. (World Economic Forum, )

  68. Assessment: SupportedSaid

    “Meanwhile X itself is promoting nonsense.”

    What happened: Mashable directly observed the false story and documented its placement in Explore and the homepage sidebar. This establishes amplification by X's own features during the target period. (Mashable, )

  69. Assessment: SupportedSaid

    “Ironic that his own chatbot, Grok, is making the problem worse.”

    What happened: Grok wrote a false headline that X promoted to users, a direct addition to information pollution. (Mashable, )

  70. Assessment: SupportedSaid

    “The bad news, which outweighs the good, is that the problem is quickly getting worse.”

    What happened: A more than tenfold rise in AI content-farm sites in about seven months is fast growth in AI information pollution. (The Guardian (Nigeria), reporting NewsGuard data, )

  71. Assessment: UnresolvedSaid

    “At the same time, more optimistic predictions like these [from Reid Hoffman September 7, 2023] have not thus far borne out.”

  72. Assessment: SupportedSaid

    “The trouble is that the bigger the model, the higher the internal costs. And current-sized models, already expensive, aren’t reliable enough for many jobs.”

    What happened: The contemporaneous engineering assessment supports the general size-resource relationship and describes substantial deployment demands. It also explains that sparse architectures can weaken the relationship between total parameters and computation. (arXiv, )

  73. Assessment: UnresolvedSaid

    “So far most uses of LLMs have been for testing, not production-level systems.”

  74. Assessment: UnresolvedSaid

    “They are inherent to how the system makes predictions, and to the fact that LLMs themselves don’t have the wherewithal to look things up and reason over them.”

  75. Assessment: UnresolvedSaid

    “Meanwhile hype itself is becoming a kind of risk factor. The more hype, the bigger the fall, if expectations aren’t met.”

  76. Assessment: UnresolvedSaid

    “Thus far, the closest thing to a killer app is probably coding assistace, but the income there isn’t massive enough to cover costs of the chips, legal etc.”

  77. Assessment: UnresolvedSaid

    “Core problems like hallucinations aren’t going way.”

  78. Assessment: UnresolvedSaid

    “(Not easy. With everyone building similar models, there is no moat, hence lots of competition on price).”

  79. Assessment: UnresolvedSaid

    “$50B in, $3B out. That’s obviously not sustainable.”

  80. Assessment: UnresolvedSaid

    “Most of those who invested in driverless cars lost money, unless they invested early and cashed out, before patience began to run out.”

  81. Assessment: UnresolvedSaid

    “There may be a payoff someday, but thus far there has not been a lot.”

  82. Assessment: SupportedSaid

    “As noted here before, one study indicates that code quality is going down.”

    What happened: The study reports declining maintainability indicators in code authored through December 2023. This directly supports the limited assertion that one study indicated declining quality. (GitClear, )

  83. Assessment: SupportedSaid

    “On the other hand, the use cases on the negative side of the ledger are proliferating fast, literally faster than I can report them here, from fake books (undermining authors) to rings of fake websites designed to sell ads”

    What happened: The report records 713 identified AI-generated sites, compared with fewer than 50 in May and around 600 in December 2023, and describes advertising incentives. These are discovery counts, not a controlled census of site creation. (The Guardian Nigeria, )

  84. Assessment: UnresolvedSaid

    “(OpenAI even brazenly asked the British goverment to exempt them from copyright laws, wanting all the gain at none of the cost.)”

  85. Assessment: SupportedSaid

    “Some core problems have lingered, particularly hallucinations, instability and occasional nonsensical errors.”

    What happened: The experiments demonstrate false supporting assertions that GPT-4 can recognize as wrong when asked separately. They establish hallucination and elementary-error failure modes, not a population-wide error rate. (Zhang and colleagues, arXiv, )

  86. Assessment: SupportedSaid

    “We can expect price wars.”

    What happened: Reuters documents Alibaba's deep cuts followed hours later by Baidu making previously paid Ernie Speed and Ernie Lite services free. It explicitly identifies the competitive episode as extending into LLM pricing. (Reuters, republished by Yahoo Finance, )

  87. Assessment: SupportedSaid

    “There has however been lots of progress both in discovering potential applications and in putting GPT-4 class models into practice.”

    What happened: The announcement identifies GPT-4-powered conversation practice and answer explanations, available immediately through Duolingo Max in selected iOS markets. This documents distinct educational applications implemented in a released product. (Duolingo, )

  88. Assessment: UnresolvedSaid

    “But putting LLMs into production is hard; most work to date has been preliminary.”

  89. Assessment: UnresolvedSaid

    “Translation : We will never, ever give you advanced access to red-team any of our software. [Narrator: and they never have.]”

  90. Assessment: UnresolvedSaid

    “Translation: We are happy to let the public believe that we are open, but it’s really just a recruiting promise that we don’t intend to live up to (Ilya, January 2016) – but we shouldn’t disabuse people of that notion (Sam, May 2016).”

  91. Assessment: SupportedSaid

    “Translation: Using a well-known algorithm that we didn’t invent to decide which faces of the cube to turn, we taught a robot to do motor control on a specially instrumented Rubik’s cube with bluetooth sensors inside.”

    What happened: The announcement explicitly separates Kociemba's move sequence from the neural networks' learned manipulation. Its approach section states that training occurred in simulation. (OpenAI, )

  92. Assessment: UnresolvedSaid

    “Translation: But I do have equity in YCombinator and YCombinator has equity in OpenAI. It’s probably worth a lot, but you wouldn’t like me as much if I acknowledged that, so I won’t. Also, did I mention I own the OpenAI venture fund? Nope, guess not, sorry.”

  93. Assessment: SupportedSaid

    “Translation: But (revealed a few weeks later) behind the scenes we will try hard to water down your regulations.”

    What happened: The surrounding exchange supports public cooperation on regulation. Elsewhere in the same testimony, Altman advocates licensing and safety requirements. (U.S. Government Publishing Office, )

  94. Assessment: UnresolvedSaid

    “Things are clearly moving fast in a bad direction.”

  95. Assessment: UnresolvedSaid

    “Literally the only other solution I can envision is to force LLM manufacturers to pay a significant tax to subsidize the journal review system that they are about to destroy.”

  96. Assessment: UnresolvedSaid

    “And there is no way reviewers and journals are going to be able to keep up.”

  97. Assessment: UnresolvedSaid

    “Two clearest examples so far this year, are the now retracted paper with the above-mentioned rat and a new lithium battery paper, with an opening sentence clearly written by an LLM.”

  98. Assessment: SupportedSaid

    “GenAI is messing with science.”

    What happened: The original paper expressly identifies Midjourney as the source of its images. It is evidence of AI provenance, not evidence that the paper's scientific assertions are reliable. (Frontiers in Cell and Developmental Biology; Xinyu Guo, Liang Dong and Dingjun Hao, )

  99. Assessment: SupportedSaid

    ““Current deep-learning systems frequently succumb to stupid errors.” Still true.”

    What happened: Experts evaluated 26 ordinary election questions across five leading models; errors included incorrect contact details and fabricated information. GPT-4 performed substantially better, with 19% of its responses rated inaccurate, and the authors explicitly warn that this small, nonrandom sample is not representative of everyday usage. (Proof News and the Institute for Advanced Study, AI Democracy Projects, )

  100. Assessment: UnresolvedSaid

    “I wrote “Fast forward to 2022, and not a single radiologist has been replaced”. Still true”