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

Claims checked against evidence by Gary Marcus, page 18 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 18 of 19: claims 1701 to 1800 of 1870, ordered by the date first said, newest first. To find a claim by its words, use search.

  1. Assessment: SupportedSaid

    “In short, ChatGPT might sound like the Star Trek computer, but for now, you still can’t trust it.”

    What happened: The developer acknowledges that convincing presentation can accompany incorrect content. The need to verify substantive answers before relying on them is an evaluator inference from this disclosed limitation. (OpenAI, )

  2. Assessment: SupportedSaid

    “Some of its advice will be head-scratchingly bad.”

    What happened: Two infectious-disease physicians assessed GPT-4 management recommendations against clinical literature and guidelines and identified harmful antibiotic advice. The same evaluation found optimal antibiotic recommendations in other cases, so it supports some bad advice without establishing universal failure. (Clinical Infectious Diseases; abstract hosted by PubMed, )

  3. Assessment: SupportedSaid

    “When queried on medicine it will either resist answering (if there are aggressive guardrails) or occasionally spout plausible-sounding but dangerous nonsense.”

    What happened: Researchers submitted 44 bloodstream-infection cases for medical management advice. This establishes the medical-query condition and the apparent fluency of the responses. The availability date is the indexed print publication date. (Clinical Infectious Diseases, indexed by PubMed, )

  4. Assessment: SupportedSaid

    “Although ChatGPT can write about anything, it is also easily confused.”

    What happened: The dated transcript records a false answer to a simple arithmetic question, followed by reasoning inconsistent with that answer. Other exchanges show repeated mistakes, although some prompts deliberately test false premises and some answers are correct. (Vipul Naik / LessWrong, )

  5. Assessment: SupportedSaid

    “It will remain turbocharged pastiche generator, and a fine tool for brainstorming, and for first drafts, but not trustworthy general intelligence.”

    What happened: The institution reports improved performance with GPT-4 on consulting work covering creativity and writing. The underlying paper identifies product brainstorming, press-release drafts and persuasive memos among the tasks. (Harvard Business School AI Institute, )

  6. Assessment: SupportedSaid

    “GPT-4 will still, like its predecessors, be a bull in a china shop, reckless and hard to control.”

    What happened: The systematic evaluation documents repeated harmful outputs under jailbreaks; its main GPT data collection ran from April 26 through May 1, with a subsequent correction rerun. Material qualification within the same study is that unmodified requests rarely elicited harmful answers and some attacks were less effective against GPT-4 than GPT-3.5. (Alexander Wei, Nika Haghtalab, and Jacob Steinhardt / arXiv, )

  7. Assessment: SupportedSaid

    “Alas, the genie can no longer be stuffed back in the bottle.”

    What happened: Meta pulled only the demo. It said the models stayed available so others could reproduce the results, which fits his point that withdrawal did not contain the technology. (MIT Technology Review, )

  8. Assessment: SupportedSaid

    “Large language models lack mechanisms for verifying truth; we need to find new ways to integrate them with the tools of classical AI, such as databases, webs of knowledge, and reasoning.”

    What happened: The paper explicitly describes default generation as ungrounded and introduces additional training and evidence provision to address it. This supports the contextual reading about standalone models, rather than an assertion that no augmented system existed. (DeepMind researchers, via arXiv, )

  9. Assessment: SupportedSaid

    “For another, chatGPT, just released by OpenAI, is more or less just as capable of producing similar nonsense, such as instant essays on adding wood chips to breakfast cereal.”

    What happened: In its first week ChatGPT was reported to confidently produce plausible but false content, the same failure Galactica was pulled for. (The Verge, )

  10. Assessment: UnresolvedSaid

    “they cost almost nothing to operate, and so they are on a path to reducing the cost of generating disinformation to zero.”

  11. Assessment: UnresolvedSaid

    “they can easily be automated to generate misinformation at unprecedented scale.”

  12. Assessment: UnresolvedSaid

    “If the website is flooded with worthless code examples, programmers will no longer go there, its database of over 30 million questions and answers will become untrustworthy, and the 14 year old website will die.”

  13. Assessment: SupportedSaid

    “GPT doesn’t talk randomly, because it’s pastiching things actual people said.”

    What happened: The study documents training on web text, books and Wikipedia, alongside controlled assessments of generated articles. Together these support learned language patterns as the basis of structured output, without establishing literal cut-and-paste generation. (OpenAI researchers / arXiv, )

  14. Assessment: SupportedSaid

    “The impressive thing about GPT is that it spits out hundreds of perfectly fluent, often plausible prose at a regular clip, with no human filtering required.”

    What happened: The first four conversations contain repeated readable, generally grammatical responses across recipes, social media, mood, and AI. The transcript exceeds hundreds of words and is explicitly described as unedited; this is evidence of output quality, not validation of its advice. (Jeff Turner, )

  15. Assessment: UnresolvedSaid

    “GPT”s heavy use of a technique called embeddings makes it really good at substituting synonyms and more broadly related phrases, but the same tendency towards substitution often lead it astray.”

  16. Assessment: UnresolvedSaid

    “When GPT sounds plausible, it is because every paraphrased bit that it pastes together is grounded in something that actual humans said, and there is often some vague (but often irrelevant) relationship between..”

  17. Assessment: UnresolvedSaid

    “Almost everything it says is fluent and at least vaguely plausible.”

  18. Assessment: SupportedSaid

    “Chance is definitely part of what’s going on. But it’s not quite the right way to understand the juxtaposition of brilliance and stupidity that we see within GPT.”

    What happened: Human detection accuracy was about 86% for the degraded control and 52% for GPT-3, with outputs selected programmatically. This supports systematic competence beyond random text, although the control changes several factors together. (Brown et al., arXiv, )

  19. Assessment: SupportedSaid

    “It could not be immediately applied to the challenge of, say, customer service or guiding the actions of a domestic robot, or well, almost anything else, really.”

    What happened: Meta explicitly limits the released agent's capabilities to Diplomacy while describing other applications as prospects for its technology. This supports Marcus's distinction between immediate deployment and later adaptation. (Meta, )

  20. Assessment: SupportedSaid

    “Strikingly, and in opposition to much of the Zeitgeist, Cicero relies quite heavily on hand-crafting, both in the data sets, and in the architecture; in this sense it is in many ways more reminiscent of classical “Good Old Fashioned AI” than deep learning systems that tend to be less structured, and less customized to particular problems.”

    What happened: The supplement specifies rules for corrupting game entities, targeted examples for counting and negation errors, and a separately configured classifier ensemble. Together with its explicit filtering and strategic-planning procedures, these document substantial task-specific engineering in both data and architecture. (Science / AAAS; supplementary PDF hosted by Gwern, )

  21. Assessment: UnresolvedSaid

    “As it turns out, the architecture of Cicero differs profoundly from most of what’s been talked about in recent years in AI.”

  22. Assessment: SupportedSaid

    “Cicero makes extensive use of machine learning, but is hardly a poster child for simply making ever bigger models (so-called “scaling maximalism”), nor for the currently popular view of “end-to-end” machine learning of in which some single general learning algorithm applies across the board, with little internal structure and zero innate knowledge.”

    What happened: The announcement describes a pretrained and fine-tuned language model combined with a specialized planning engine and message filters. This supports the factual claim that the system's design goes beyond enlarging a single general model. (Meta AI, )

  23. Assessment: UnresolvedSaid

    “See also my essay on Google’s Palm-SayCan, for what might happen if this tech were embedded in robots, which would be even more risky.”

  24. Assessment: UnresolvedSaid

    “if Alexa could converse freely, user expectations would go through the roof, and mostly be unmeetable.”

  25. Assessment: SupportedSaid

    “Bottom line: From the outset Large Language Models like GPT-3 have great at generating surrealist prose, and they can beat a lot of benchmarks, but they are not (and may never be) great tech for reliably inferring user intent from what users say.”

    What happened: The study tests implied meaning rather than fluent text production and finds substantial shortcomings even in adapted models. It supports the ordinary reading that dependable contextual understanding remained a weakness, while acknowledging meaningful improvements. (Ruis et al., arXiv, )

  26. Assessment: SupportedSaid

    “LLMs are unruly beasts; nobody knows how to make them refrain 100% of time from insulting users, giving bad advice, or just plain making stuff up.”

    What happened: OpenAI described reliable refusal of unsafe instructions as unsolved despite improvements from human-feedback training. Its limitations section also reports remaining toxic outputs and fabricated facts. (OpenAI, )

  27. Assessment: ContradictedSaid

    “It's worth asking since (a) Amazon is apparently greatly reducing Alexa's staff, (b) hasn't added significant features in years, and (c) seems clumsy and limited compared to GPT-3.”

    What happened: The dated launch account describes multiparty interaction and speech directed at Alexa without repeatedly saying the wake word. This is a substantive interaction feature within the claimed period, although it does not establish GPT-3-like open conversation. (Amazon Science, )

  28. Assessment: UnresolvedSaid

    “Turning LLMs into a product that controls your home and talks to you in a way that would be reliable enough to use at scale in millions of homes is still a long, long way away.”

  29. Assessment: UnresolvedSaid

    “Just like every other large language model I have seen.”

  30. Assessment: SupportedSaid

    “Pitch perfect and utterly bogus imitations of science and math, presented as the real thing.”

    What happened: The preserved response presents a purported paradox with formal sections and a solution, treating the sum of two 0.5 terms as 0.4 to reach this conclusion. Direct arithmetic gives 1, so the mathematical exposition is invalid despite its factual presentation. (Ernest Davis and Andrew Sundstrom, NYU-hosted experiment record, )

  31. Assessment: UnresolvedSaid

    “High school students will love it, and use it to fool and intimidate (some of) their teachers.”

  32. Assessment: UnresolvedSaid

    “The problem is about to get much, much worse.”

  33. Assessment: UnresolvedSaid

    “As the pace of machine-generated misinformation picks up, Twitter’s existing effort, Community Notes (aka Birdwatch), which is mostly done manually, by humans, is going to get left in the dust.”

  34. Assessment: UnresolvedSaid

    “Large language models are without equal at generating misinformation, but they suck at detecting it.”

  35. Assessment: UnresolvedSaid

    “Scaling won’t solve this.”

  36. Assessment: SupportedSaid

    “Knockoffs of GPT-3 are getting cheaper and cheaper, which means that the cost of generating misinformation is going to zero, and the quantity of misinformation is going to rise—probably exponentially.”

    What happened: The release compares optimized and baseline implementations of open text generators, including BLOOM and OPT, using dollars per million generated tokens. Its cost-sensitive benchmarks report substantial reductions, directly supporting cheaper attainable inference before the post. (Microsoft DeepSpeed, )

  37. Assessment: SupportedSaid

    “Knockoffs of GPT-3 are getting cheaper and cheaper, which means that the cost of generating misinformation is going to zero, and the quantity of misinformation is going to rise—probably exponentially.”

    What happened: The experiments establish that alternatives can produce convincing election-disinformation content. For ten articles daily over a month, the cost section estimates $0.30 using Gemini or $9 using hosted Llama 3 70B, compared with $4,500 for the cited human-writing service; these are generation-stage estimates, not total campaign budgets. (PLOS ONE, )

  38. Assessment: SupportedSaid

    “Scaling here was largely AWOL; even the best model was only at 80.6%, and for most models, scaling had at best a neglible effect.”

    What happened: The best overall mean is 80.6% with 30 examples; Section 4.1 reports near-zero or decreasing slopes for many families, with only Cohere, T0 and InstructGPT-3 performing best at their largest tested size. The same source qualifies the headline: structured prompts average 81.7%, and Table 58 includes an individual-template score of 82.8%. (arXiv; Laura Ruis and colleagues, )

  39. Assessment: SupportedSaid

    “Scaling is an empirical observation, not a guaranteed, continued law of nature.”

    What happened: The foundational paper presents empirical relationships and explicitly qualifies their theoretical status. Appendix C and section 6.3 discuss limits to trusting or extending those relationships, supporting the absence of an indefinite guarantee. (Jared Kaplan and colleagues, arXiv, )

  40. Assessment: UnresolvedSaid

    “Scaling maximalism is an interesting hypothesis, but I stand by my prediction that it won’t get us to AGI.”

  41. Assessment: UnresolvedSaid

    “Any one of them would mean we need a real paradigm shift, if we are to get to AGI.”

  42. Assessment: UnresolvedSaid

    “His hypothesis, now generally called scaling maximalism, remains extremely popular, in no small part because bigger and bigger models have indeed continued to do ever more impressive things.”

  43. Assessment: UnresolvedSaid

    “As I have long argued, capturing this without cognitive models and common sense is really hard.”

  44. Assessment: UnresolvedSaid

    “We know how to build machines that do math (augmentation); we don’t know how to build machines that can read tax codes (replacement).”

  45. Assessment: UnresolvedSaid

    “We can’t really build humanoid domestic robots without general intelligence, and we don’t yet have any serious candidates.”

  46. Assessment: UnresolvedSaid

    “Hedging with probably makes it sounds like we think there is a viable competitor out there for human general intelligence in the AI world. There isn’t.”

  47. Assessment: UnresolvedSaid

    “Every truck I know is still driven by a person (except in some limited-use test pilots), and no radiologists have yet been replaced.”

  48. Assessment: UnresolvedSaid

    “Human children are constantly learning new things, often from tiny amounts of data. There is literally nothing like that in the AI world.”

  49. Assessment: UnresolvedSaid

    “I hereby bet publicly that AI will not fully displace radiologists (as opposed to merely augmenting them) before the year 2033.”

  50. Assessment: SupportedSaid

    “having a machine look at a radiology scan is very different from replacing the entire job of a radiologist”

    What happened: The society's contemporary statement describes patient advocacy, safety, treatment and communication responsibilities alongside diagnosis. These substantive duties support the distinction between interpreting images and replacing the whole clinical role. (European Society of Radiology; Insights into Imaging, )

  51. Assessment: UnresolvedSaid

    “Eleven years later, comprehension is still lacking (see any of my recent essays here) and very few if any jobs have actually been replaced by AI.”

  52. Assessment: SupportedSaid

    “It’s way too much charity to deep learning to claim that there is any kind of direct link.”

    What happened: The author's account describes mapping, correlations and effects of changing model depth and attention. Inferring that these do not establish identical biological mechanisms is the evaluator's assessment, not an explicit concession in this quotation. (Charlotte Caucheteux, reproduced by Thread Reader, )

  53. Assessment: SupportedSaid

    “So if you have a correlation of R = .5, you actually “explain” (really, just “predict”) only 25% of the variance—which means fully three-quarters (not half) of the variability remains unexplained.”

    What happened: The paragraph ties this statement to R = 0.50. Squaring that coefficient yields 25% accounted for and 75% remaining. (Scientific Reports, )

  54. Assessment: UnresolvedSaid

    “There is no evidence whatsoever that what has gotten us, sort of, into the game of reading scans is going to bring us all the way into the promised land of a radiologist in an app any time soon.”

  55. Assessment: UnresolvedSaid

    “Well, no. Augmentation is way easier, because you don’t have to solve the whole job.”

  56. Assessment: SupportedSaid

    “Of course we see the same thing over and over in language systems, too.”

    What happened: The surrounding results describe recurrent false-premise acceptance, excessive hedging, and constraint failures despite strong language-task performance. These directly match the behavioral pattern Marcus describes. (arXiv; OpenAI research authors, )

  57. Assessment: UnresolvedSaid

    “It’s also, as ever, stuck in the liminal space between superintelligence and super-lost.”

  58. Assessment: UnresolvedSaid

    “This is the uncanny valley we are in, and one that we occupy for a while.”

  59. Assessment: UnresolvedSaid

    “We in AI have a long long way to go. Data and compute alone won’t be enough.”

  60. Assessment: UnresolvedSaid

    “Musk barely even acknowledged the problem, and certainly didn’t lay out any sort of credible strategy for attacking it..”

  61. Assessment: UnresolvedSaid

    “The reality is that AI needs genuine, paradigm-shift level innovation. Simply building ever bigger neural networks won’t cut it.”

  62. Assessment: ContradictedSaid

    “There was a lot of bluster (we will 100x the world’s productivity) but no road map.”

    What happened: Tesla supplied tentative delivery timing. The same transcript outlines prototype improvement followed by factory deployment and progressively broader uses. (Tesla, transcribed by Stock Analysis, )

  63. Assessment: UnresolvedSaid

    “Second, there was very little vision for how Tesla would build the cognitive part of the AI they will need, beyond the basics of motor control (which Boston Dynamics already does so well), nor much recognition about why robotics is so hard in the real world.”

  64. Assessment: SupportedSaid

    “Sooner or later he will recognize that humanoid robots, too, pose a whole set of challenges that lie well beyond our current grasp.”

    What happened: Google's robotics research head describes unresolved real-world adaptation and understanding, with helpful robots still limited to restricted kitchen tasks and explicit safety controls. Applying these shared robotics limitations to general household humanoids is an inference, rather than a direct humanoid safety test. (Google, )

  65. Assessment: SupportedSaid

    “The real problem w robots has never been demos, anyway (there have been many, for decades); it’s making robots in robust in the real world.”

    What happened: SRI dates Shakey's development to 1966–1972 and describes integrated perception, reasoning, navigation, and task execution. The endpoint records demonstrated robotic capabilities roughly five decades before the post. (SRI International, )

  66. Assessment: UnresolvedSaid

    “Musk is almost never right about deadlines.”

  67. Assessment: UnresolvedSaid

    “Current neural nets lack the cognitive depth and robustness that would be necessary to support reliable L5 self-driving form vision alone.”

  68. Assessment: UnresolvedSaid

    “The problem for the neural network community is not that I have changed my goal posts, it’s that my 2012 concerns still stand.”

  69. Assessment: SupportedSaid

    “Over and over, the field of AI has discovered otherwise. Guy van den Broeck’s study is a perfect example of this.”

    What happened: Zhang and colleagues, including van den Broeck, found near-perfect performance within a training distribution but substantial failures under different sampling of the same logical problem space. Their analysis attributes this to statistical features rather than acquisition of the intended reasoning procedure. (arXiv, )

  70. Assessment: UnresolvedSaid

    “The argument I made there is that … systems like DALL-E don’t “understand what wheels are—or what they are for”, because (as I discovered on Sunday) they systematically fail to properly illustrate requests like these:”

  71. Assessment: SupportedSaid

    “Over and over I have stressed that you could have a good model of engineering without being a good model of humans, or vice versa.”

    What happened: Faggella's interview report describes Marcus contrasting evolved human memory with more reliable computer memory. It records an earlier example of engineering desirability differing from human fidelity; the quotation preserves the source's apparent typo. (Emerj; Dan Faggella, )

  72. Assessment: SupportedSaid

    “But the smart money is still on being cautious about this, and in recognizing that what the Facebook researchers said in 2019 is still the case.”

    What happened: Reducing misleading correlations improved independent-test performance for BERT and stronger RoBERTa and ALBERT models, supporting continued susceptibility in 2022. The same experiments found better generalization from larger models and debiasing, which limits any claim that no genuine progress occurred. (arXiv; ACL 2022 author manuscript, )

  73. Assessment: UnresolvedSaid

    “On #1, the answer is a clear yes; only highly trained human artists could do better.”

  74. Assessment: SupportedSaid

    “On #2, the answer is mixed. They do well on some inputs (like astronaut rides horse) but more poorly on others (like horse rides astronaut, which I discussed in an earlier post).”

    What happened: The study's human judgments show some successful matching alongside widespread relational failures. Its results were reported by July; the precise image-generation dates are not supplied. (arXiv; Colin Conwell and Tomer Ullman, )

  75. Assessment: UnresolvedSaid

    “Nine more tries, and only one very marginal success (top right corner):”

  76. Assessment: UnresolvedSaid

    “Even “draw a white bicycle with green wheels”, which focuses purely on part-whole relationships without function or complex syntax, is problematic:”

  77. Assessment: UnresolvedSaid

    “Negation is, as ever, a problem.”

  78. Assessment: UnresolvedSaid

    “Google cloaks its current work the trappings of science (article preprints with citations and graphs and so forth) but when it comes to community inspection, they’ve systematically opted out.”

  79. Assessment: UnresolvedSaid

    “Notwithstanding the rumors about Google Imagen, nobody has yet publicly demonstrated a machine that can relate the meanings of sentences to their parts the way a five-year-old child can.”

  80. Assessment: SupportedSaid

    “The dirty secret in current AI is that for all the bloviating about how current systems have mastered human language, they are still really weak on compositionality, which most linguists (since Frege, over a century ago) would agree is at the very core of how language works.”

    What happened: Winoground found large human-model gaps across several vision-language model families on image-caption matching that depends on composition. The finding supports a broad weakness within the tested contemporary systems. (arXiv; Thrush and colleagues, )

  81. Assessment: SupportedSaid

    “The progress that Alexander is vaunting shows that the system did better on a handful of items than DALL-E2 (which was not even state of the art at compositionality when it appeared), but not that the newer system (Imagen) systematically groks compositionality”

    What happened: The page specifies five prompts, reports DALL-E 2 at 0/5 and Imagen at 3/5 under Alexander's scoring, and contains no systematic retest of earlier successes. Alexander also expressly declines to claim full mastery, so the methodological limitation does not establish that he claimed otherwise. (Astral Codex Ten, )

  82. Assessment: ContradictedSaid

    “we were right to be skeptical; so far, four and half years later, Duplex hasn’t gotten a whole lot further.)”

    What happened: Google reported over a million completed bookings and over three million business-information updates across eight countries, alongside the quoted automation rate. These reported deployments show substantial operational progress beyond a limited demonstration. (Google, )

  83. Assessment: UnresolvedSaid

    “But many people have claimed over the years to solve the problem, and none of those systems have proven to be reliable; every proposed solution has been like Clever Hans, working in dim light, leveraging large databases to some degree, but falling apart upon careful inspection; they might get 60% on some task, but they never really master it.”

  84. Assessment: SupportedSaid

    “This should be a piece of cake, but for currently popular systems, it’s not.”

    What happened: The group score requires all relevant caption-image comparisons to be correct; every tested model scored below its random baseline. Some models exceeded chance on the less demanding text score, but remained far behind humans and did not reliably solve the complete matching task. (arXiv; Thrush and colleagues, )

  85. Assessment: UnresolvedSaid

    “Building a robot on top of language system that has such little comprehension of the world can’t be a recipe for success.”

  86. Assessment: UnresolvedSaid

    “Cool demo, yes. Real world product, anytime soon? Fuggedaboutit.”

  87. Assessment: SupportedSaid

    “Driverless car demos have been around for decades, but making them work reliably has proven far harder than most people realized.”

    What happened: The report records a direct industry admission and describes executives narrowing expectations because of technical complexity. A readable primary transcript was not obtained, so contemporary reporting is used. (WIRED, )

  88. Assessment: UnresolvedSaid

    “Big data won’t be enough to solve the problems of robotics”

  89. Assessment: SupportedSaid

    “Performance hovered at around 75% in the best case, when the robots had a limited number of options to consider.”

    What happened: The reported overall execution rates are 74% in the mock kitchen and 60% in the real kitchen. Thus the better overall setting is approximately 75%; planning is a separate measure. (arXiv; Michael Ahn and colleagues, )

  90. Assessment: UnresolvedSaid

    “Facebook M was canceled, and 7 years later nobody yet can buy a super-smart (or even decently smart) robot at any price.”

  91. Assessment: SupportedSaid

    “First, the language technology that the new system relies on is well-known to be problematic and second it is likely to be even more problematic in the context of robots.”

    What happened: PaLM's own announcement acknowledges an existing research literature on risks and describes analyses of bias and model outputs. This directly supports recognition of problems in the relevant technology. (Google Research, )

  92. Assessment: UnresolvedSaid

    “Google makes a big deal about how the system is to do some degree interpretable, but there is no obvious way to integrate large language models with the kind of formal formal verification methods that are now routine in the design of microprocessors, USB drivers and large aircraft.”

  93. Assessment: UnresolvedSaid

    “Indeed systems like GPT-3 are spectacular at memorization, but many onlookers recognize them for what they are, stochastic imitations of their input, with very little real coherence and very little comprehension.”

  94. Assessment: SupportedSaid

    “it’s even less clear how it would work with words like no and not (a major sore spot for current neural networks).”

    What happened: The study reports substantial RoBERTa performance losses on subsets where negation matters: QNLI macro F1 is 0.67 for important negations versus 0.92 for unimportant negations. These are dataset-subset comparisons, not matched sentence pairs; the findings were already reported in the March preprint. (Association for Computational Linguistics, )

  95. Assessment: UnresolvedSaid

    “The thing is, current AI systems that (roughly speaking) do a lot of cutting and pasting without those grammar rules and trees aren’t really all that close to understanding human language.”

  96. Assessment: UnresolvedSaid

    “We don’t know how kids do it; we do know that what we are doing with machines currently isn’t really working.”

  97. Assessment: UnresolvedSaid

    “All normal children manage to acquire language in this sense; no existing machine does.”

  98. Assessment: UnresolvedSaid

    “In our view, inadequate attention to these three factors has serious consequences, including:”

  99. Assessment: UnresolvedSaid

    “here, DALL-E mini struggles to accurately represent the implicit action for the woman, capturing the apple but not reliably capturing the eating, often sticks an apple in the man’s mouth rather than the sandwich, and in most cases misses the implication that the man and woman are supposed to be eating different things.”

  100. Assessment: UnresolvedSaid

    “The fact that its word sequence predictions are often so good tempts many into ascribing meaning, but this is more a reflection of the tremendous size of the input corpus than of a system that genuinely maps sentence onto reference and cognitive models.”