Supported claims by Gary Marcus, page 6 of 6
Deadlines are judged as of 2026-10-29. About Gary Marcus and the sources covered.
Supported claims by Gary Marcus (530)
The dated evidence matches what the claim committed to, under the criteria frozen before grading.
Page 6 of 6: claims 501 to 530 of 530, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Observation
“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.”
Our reading: LLMs like GPT-3 are not great technology for reliably inferring user intent from what users say.
No horizon Evidence as of
Limitation: The principal evidence is a research benchmark, not a representative household deployment study.
Assessment: SupportedSaid
Observation
“Pitch perfect and utterly bogus imitations of science and math, presented as the real thing.”
Our reading: The Galactica examples Andrew Sundstrom shared are pitch-perfect but utterly bogus imitations of science and math, presented as the real thing.
No horizon Evidence as of
Limitation: Support depends on including the companion examples expressly linked in the post.
Source: A Few Words About Bullshit
Assessment: SupportedSaid
Observation
“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.”
Our reading: Knockoffs of GPT-3 are getting cheaper and cheaper.
No horizon Evidence as of
Limitation: The result depends on including inference operating costs in cheaper.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: SupportedSaid
Causal claim
“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.”
Our reading: Cheaper GPT-3 knockoffs will drive the cost of generating misinformation toward zero.
No horizon Evidence as of
Limitation: Support depends on interpreting zero as negligible marginal cost.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: SupportedSaid
Observation
“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.”
Our reading: On the Ruis et al. pragmatics task, the best model reached only 80.6%, and for most models scaling had at best a negligible effect.
No horizon Evidence as of
Limitation: Support depends on reading 80.6% as the overall mean, not the maximum across every prompt configuration.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: SupportedSaid
Observation
“Scaling is an empirical observation, not a guaranteed, continued law of nature.”
Our reading: AI scaling is an empirical observation, not a law of nature that is guaranteed to keep holding.
No horizon Evidence as of
Limitation: An empirical relationship can remain useful and predictive without being guaranteed.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: SupportedSaid
Observation
“Of course we see the same thing over and over in language systems, too.”
Our reading: Language systems repeatedly show the same mix of fluent output and lack of understanding.
No horizon Evidence as of
Limitation: Support concerns the recurring behavioral pattern, not the exact anecdotes.
Assessment: SupportedSaid
Observation
“Sooner or later he will recognize that humanoid robots, too, pose a whole set of challenges that lie well beyond our current grasp.”
Our reading: Humanoid robots pose a set of challenges well beyond current technology's grasp.
No horizon Evidence as of
Limitation: The decisive account addresses shared robotics limitations rather than directly testing a humanoid in a home.
Source: Sub-Optimal
Assessment: SupportedSaid
Observation
“Over and over, the field of AI has discovered otherwise. Guy van den Broeck’s study is a perfect example of this.”
Our reading: The AI field has repeatedly found that doing well on a benchmark designed for X does not mean a system has actually solved X.
No horizon Evidence as of
Limitation: The studies cover selected logical-reasoning and language-inference tasks, not all AI benchmarks or models [e4, e5].
Assessment: SupportedSaid
Observation
“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.”
Our reading: State-of-the-art models still learn to exploit spurious statistical patterns rather than learning meaning flexibly as humans do, so benchmark gains warrant caution.
No horizon Evidence as of
Limitation: The evidence covers selected NLP models and tasks, not every leading model available by September 23, 2022 [e6, e7].
Assessment: SupportedSaid
Observation
“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).”
Our reading: Image synthesis systems only inconsistently match their linguistic input to the images produced, doing well on some prompts and poorly on others.
No horizon Evidence as of
Limitation: The strongest experimental evidence covers DALL-E 2.
Source: Form, function, and the giant gulf between drawing a picture and understanding the world
Assessment: SupportedSaid
Observation
“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.”
Our reading: Current AI systems are still very weak at compositionality, which is central to how language works.
No horizon Evidence as of
Limitation: The result depends on reading current AI as prominent contemporary neural systems rather than every AI architecture.
Source: Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?
Assessment: SupportedSaid
Observation
“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”
Our reading: Alexander's test shows only that Imagen beat DALL-E 2 on a handful of items, not that Imagen systematically understands compositionality.
No horizon Evidence as of
Limitation: The scores reflect Alexander's judgments, not independent verification [e1].
Source: Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?
Assessment: SupportedSaid
Observation
“This should be a piece of cake, but for currently popular systems, it’s not.”
Our reading: Currently popular vision-language systems cannot reliably tell apart captions that use the same words in a different order.
No horizon Evidence as of
Limitation: The result applies to the evaluated models and task.
Source: Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?
Assessment: SupportedSaid
Observation
“Performance hovered at around 75% in the best case, when the robots had a limited number of options to consider.”
Our reading: PaLM-SayCan's performance was around 75% at best, when the robot had a limited number of options.
No horizon Evidence as of
Limitation: Best case is interpreted as the best overall setting.
Assessment: SupportedSaid
Observation
“Since then, the results on benchmarks have gotten better, but there’s still something missing.”
Our reading: Since 2019 large language models have done better on benchmarks, but something is still missing from them.
No horizon Evidence as of
Limitation: TruthfulQA deliberately tests susceptibility to misconceptions; its percentages are not general error rates for all language use.
Source: Three ideas from linguistics that everyone in AI should know
Assessment: SupportedSaid
Observation
“noncanonical reorderings such as these remain problematic for current AI approaches.)”
Our reading: Current AI approaches still struggle with noncanonical word orders such as 'the mat is on the cat'.
No horizon Evidence as of
Limitation: The evidence concerns tested vision-language models, not every contemporary AI approach [e1].
Source: Three ideas from linguistics that everyone in AI should know
Assessment: SupportedSaid
Observation
“Because it shows that the people who really claim to be working on full self-driving (which Tesla waffles on1) are having plenty of problems of their own.”
Our reading: Companies working on full (Level 5) self-driving are having plenty of problems, per NHTSA ADS data.
No horizon Evidence as of
Limitation: Support is for repeated crash involvement, not technical fault or comparative danger.
Assessment: SupportedSaid
Observation
“Level 5 has to handle it all; everyday stopped vehicles, weird outliers and so forth. We’re just not there.”
Our reading: Level 5 self-driving that handles everything has not been achieved.
No horizon Evidence as of
Limitation: The industry-wide finding relies on contemporaneous reporting; the primary permit corroborates a particular deployment.
Assessment: SupportedSaid
Observation
“Indeed, the NHTSA report data show that even some pretty ordinary circumstances, like avoiding stopped vehicles, are continuing to prove problematic.”
Our reading: NHTSA data show even ordinary circumstances like avoiding stopped vehicles remain problematic for automated driving systems.
No horizon Evidence as of
Limitation: The decisive source is NHTSA's contemporaneous investigation, not a verified extraction of the specific June 15 summary tables.
Assessment: SupportedSaid
Observation
“The basic finding was: loads of things scale, but not at all ( as foreseen both by Kurenkov in his essay last year and in my much lampooned but basically accurate essay, Deep learning is hitting a wall).”
Our reading: BIG-bench shows many capabilities improve with scale but not all, which bears out Marcus's 'Deep learning is hitting a wall' essay.
No horizon Evidence as of
Limitation: The finding does not establish a permanent scaling barrier or what happens beyond the tested model sizes [e1].
Assessment: SupportedSaid
Observation
“The massive paper looks at scaling on many measures, and sees progress on some– but not others. The stuff that isn’t scaling is the “wall”.”
Our reading: The BIG-bench paper looked at scaling on many measures and found progress on some but not others, and the measures that did not scale in it are what Marcus calls the 'wall'.
No horizon Evidence as of
Limitation: The finding applies to the evaluated models, scales and metrics.
Assessment: SupportedSaid
Observation
“so many of the unfortunate consequences of a webscraping/big data approach to AI are disproportionately borne by women and minorities.”
Our reading: Many of the harmful consequences of the webscraping/big-data approach to AI fall disproportionately on women and minorities.
No horizon Evidence as of
Limitation: Support depends on reading many qualitatively and including representational and data-exclusion harms.
Assessment: SupportedSaid
Forecast
“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.”
Our reading: GPT-4 will very likely still have a fundamental lack of reliability, though it may take longer prompts to break.
Settled before its deadline Evidence as of
Limitation: Support applies to recurring factual and reasoning unreliability, not a claim that GPT-4 has no useful capabilities.
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: SupportedSaid
Observation
“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.”
Our reading: Musk said in 2015 that truly self-driving cars were two years away and has pretty much said the same thing every year since, and truly self-driving cars still had not arrived.
No horizon Evidence as of
Limitation: Several predictions are documented through contemporary reporting because readable original transcripts were unavailable.
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: SupportedSaid
Observation
“Even within perception 3D perception remains a challenge, and scene understanding is by no means solved.”
Our reading: 3D perception and scene understanding remain unsolved for current AI.
No horizon Evidence as of
Limitation: The studies cover particular tasks rather than every form of perception or scene understanding.
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: SupportedSaid
Observation
“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.”
Our reading: The long-tail problem of rare events is a very serious issue for the currently popular AI techniques, which rely more on big data than on reasoning.
No horizon Evidence as of
Limitation: Evidence is strongest for visual recognition.
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: SupportedSaid
Forecast
“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.”
Our reading: Tesla will not ship level 5 self-driving cars in 2022 or 2023.
Deadline passed Evidence as of
Limitation: The assessment concerns delivered capability, not whether Tesla sold an option named Full Self-Driving.
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: SupportedSaid
Observation
“But you and I both know that they continue to be a major problem that as yet has no known robust solution.”
Our reading: Outliers remain a major AI problem with no known robust solution.
No horizon Evidence as of
Limitation: The absence of a broadly validated solution is an inference from the inspected public research record, not proof that every possible solution was absent.
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: SupportedSaid
Observation
“That’s where the deep understanding of language should live—but, despite the PR, it isn’t there.”
Our reading: Imagen lacks deep understanding of language despite Google's PR.
No horizon Evidence as of
Limitation: The result depends on interpreting deep understanding through the role-and-relationship behavior discussed in the post.
Source: Horse rides astronaut