Claims that can’t be checked by Gary Marcus, page 24 of 24
Deadlines are judged as of 2026-10-29. About Gary Marcus and the sources covered.
Claims that can’t be checked by Gary Marcus (2353)
Public evidence cannot settle these claims, so they are recorded and attributed, not scored. Most were screened out: both AI providers read the claim on its own and found it a value judgment, rhetoric, hedged, too vague to test, or about something no public evidence could show. The rest were found to be recommendations or too open to test in a full review.
Page 24 of 24: claims 2301 to 2353 of 2353, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: Can’t be checkedSaid
Conditional forecast
“The best argument that Tesla might not survive if its massive bet on self-driving fails comes from Elon Musk himself.”
Our reading: Tesla might not survive as a company if its massive bet on self-driving fails.
No horizon
Only if: if its bet on self-driving fails
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“It’s fantastic that NHTSA is now pushing manufacturers to release incident data, and but we need soooo much more data (eg about miles driven) if we are to look at the money question—how safe is all this, really—in a truly scientific way.”
Our reading: Much more data, such as miles driven, is needed if we are to assess in a truly scientific way how safe driver-assist and self-driving systems really are.
No horizon
Qualifier: if we are to assess safety in a truly scientific way
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“But our experiments in building them need to be strictly regulated, especially when nonconsenting human beings become unwilling participants. And our requirements on data transparency need to be vastly stricter.”
Our reading: Driverless car experiments should be strictly regulated, and data transparency requirements should be vastly stricter.
No horizon
Qualifier: especially when nonconsenting people are involved
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Does that mean that society should give up on building driverless cars? Absolutely not.”
Our reading: Society should not give up on building driverless cars.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“This morning’s fresh data dump from NHTSA should be a bit of a wakeup call to those who have gone long on Tesla, if they are thinking that a solution to Full Self Driving is in sight. The data aren’t devastating, but they aren’t so promising either, and they raise of lot of questions.”
Our reading: The new NHTSA data should be a bit of a wakeup call to those who have gone long on Tesla thinking a Full Self Driving solution is in sight; the data aren't devastating, but they aren't so promising either, and they raise a lot of questions.
No horizon
Scope: those who have gone long on Tesla
Assessment: Can’t be checkedSaid
Rhetoric
“In truth, literally everything that the system says is bullshit.”
Our reading: Everything LaMDA says is bullshit with no real meaning behind it.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Rhetoric
“Fooling people into thinking a program is intelligent is just not the same as building programs that actually are intelligent.”
Our reading: Fooling people into thinking a program is intelligent is not the same as building an intelligent program.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Observation
“Neither LaMDA nor any of its cousins (GPT-3) are remotely intelligent.”
Our reading: Neither LaMDA nor similar large language models such as GPT-3 are remotely intelligent.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Observation
“All they do is match patterns, draw from massive statistical databases of human language. The patterns might be cool, but language these systems utter doesn’t actually mean anything at all.”
Our reading: LaMDA-style language models only match statistical patterns of human language, and their output has no real meaning.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Observation
“But there is no absolutely no reason whatever for us to waste time wondering whether anything anyone in 2022 knows how to build is sentient. It is not.”
Our reading: No AI system that anyone knew how to build in 2022 is sentient.
No horizon
Qualifier: anything anyone in 2022 knows how to build
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Forecast
“None of the software in either of those systems has survived in modern efforts at “artificial general intelligence”, and I am not sure that LaMDA and its cousins will play any important role in the future of AI, either.”
Our reading: Marcus doubts that LaMDA and similar language models will play any important role in the future of AI.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Observation
“What these systems do, no more and no less, is to put together sequences of words, but without any coherent understanding of the world behind them, like foreign language Scrabble players who use English words as point-scoring tools, without any clue about what that mean.”
Our reading: Language models like LaMDA string words together without any coherent understanding of the world.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Observation
“To be sentient is to be aware of yourself in the world; LaMDA simply isn’t.”
Our reading: LaMDA is not sentient.
No horizon
Source: Nonsense on Stilts
Assessment: Can’t be checkedSaid
Forecast
“In my view, no way; I think the actual odds are less than 20%.”
Our reading: The odds that scaling plus CNNs, RNNs and Transformers alone reach AGI with reliable reasoning are below 20%.
No horizon
Qualifier: with scaling and CNNs + RNNs + Transformers alone
Assessment: Can’t be checkedSaid
Forecast
“While there’s no denying that the two of us have often clashed, on this point—the point about scale alone not likely to be enough, and about the need for some new discovery (i.e., paradigm shifts)—we are actually in complete agreement.”
Our reading: Scale alone is not likely to be enough, and new discoveries (paradigm shifts) are needed, a point on which Marcus and LeCun agree.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“GPT-2 is both a triumph for empiricism, and, in light of the massive resources of data and computation that have been poured into them, a clear sign that it is time to consider investing in different approaches.”
Our reading: GPT-2 is a clear sign that it is time to consider investing in approaches other than scaling.
No horizon
Qualifier: in light of massive data and compute poured into GPT-2
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“I say that we have given the scaling hypothesis a really good look (with a larger budget than all but a handful of projects in history), and as such the failures of large scale systems are evidence (not proof) that we ought to seriously consider alternatives, e.g., here:”
Our reading: The failures of large-scale systems, after a heavily funded test of the scaling hypothesis, are evidence that the field should seriously consider alternatives to scaling.
No horizon
Qualifier: given failures of large scale systems
Assessment: Can’t be checkedSaid
Forecast
“That said, I, of course, could be right about foreseeing the need for a paradigm shift, but wrong about what that paradigm shift turns out to be.”
Our reading: A paradigm shift may be needed, but it could take a different form than the neurosymbolic one Marcus advocates.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“That’s why we need new approaches, exactly as I have been arguing.”
Our reading: AI research needs new approaches rather than scale alone.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“Personally I’d put the probability that we need to shift at 90%, well above the 60% that Alexander suggests, and I would put the probability that we will need to embrace symbol-manipulation as part of the mix at 80%, more than double Alexander’s 34%.”
Our reading: Marcus puts a 90% probability on AI needing a paradigm shift to reach AGI.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“Personally I’d put the probability that we need to shift at 90%, well above the 60% that Alexander suggests, and I would put the probability that we will need to embrace symbol-manipulation as part of the mix at 80%, more than double Alexander’s 34%.”
Our reading: Marcus puts an 80% probability on AGI needing symbol manipulation as part of the mix.
No horizon
Assessment: Can’t be checkedSaid
Observation
“Scott’s more marked success on six questions may have been a fluke.”
Our reading: Scott Alexander's finding that InstructGPT got four of six previously failed prompts right may have been a fluke.
No horizon
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Causal claim
“The core problem that follows from that has always been that these systems aren’t stable or trustworthy; a crumbling house follows from a rotten foundation.”
Our reading: Because LLMs lack causal world models, they are not stable or trustworthy.
No horizon
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“I think all that’s scandalous.”
Our reading: Marcus thinks it is scandalous that OpenAI never followed through on its promise of GPT-3 access to him and Davis and never replied to his request for DALL-E 2 access, and that Google Brain never replied to his request for Imagen access.
No horizon
Qualifier: I think
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Observation
“But I see no reason whatsoever to think that the underlying problem — a lack of cognitive models of the world —have been remedied.”
Our reading: Marcus sees no reason whatsoever to think that the underlying problem of these models, a lack of cognitive models of the world, has been remedied.
No horizon
Qualifier: I see no reason whatsoever to think
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Observation
“Either way, though, there is still no real physical, social, or psychological understanding, just richer parroting of larger corpora (and some reinforcement learning to keep the system more on check).”
Our reading: Current LLMs have no real physical, social, or psychological understanding; they are richer parroting of larger corpora.
No horizon
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Conditional forecast
“Maybe we will find fewer absolute howlers per attempt than we did with GPT-3, but it still won’t be so proficient as to have developed a genuine causal model of the world.”
Our reading: If GPT-4 is basically the same architecture as GPT-3 but with more data, it will not be proficient enough to have developed a genuine causal model of the world, though Marcus and Davis may find fewer absolute howlers per attempt than with GPT-3.
No horizon
Only if: assuming GPT-4 is basically the same architecture but with more data
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Observation
“And yes, there has been improvement eg from GPT-2 to GPT-3—but zero qualitative change.”
Our reading: Improvement from GPT-2 to GPT-3 has been quantitative only, with zero qualitative change.
No horizon
Source: What does it mean when an AI fails? A Reply to SlateStarCodex’s riff on Gary Marcus
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Four years later, many people continue to hope that deep learning will be a panacea; that still seems pretty unrealistic to me; I still think we need a whole lot more techniques.”
Our reading: Many people continue to hope deep learning will be a panacea, which seems pretty unrealistic to Marcus, and he still thinks a whole lot more techniques are needed.
No horizon
Qualifier: seems pretty unrealistic to me
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: Can’t be checkedSaid
Conditional forecast
“Seven years is a long time, but the field is going to need to invest in other ideas if we are going to get to AGI before the end of the decade. Or else outliers alone might be enough to keep us from getting there.”
Our reading: Unless the field invests in ideas beyond current techniques like deep learning, outliers alone may prevent reaching AGI before the end of the decade.
No horizon
Only if: unless the field invests in ideas beyond deep learning
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: Can’t be checkedSaid
Forecast
“Unexpected circumstances have been and continue to be the bane of contemporary AI techniques, and probably will be until there is a real revolution.”
Our reading: Unexpected circumstances have been and continue to be the bane of contemporary AI techniques, and probably will remain so until there is a real revolution.
No horizon
Qualifier: until there is a real revolution
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: Can’t be checkedSaid
Forecast
“Being realistic, 7 years might well not be enough to (a) invent those tools (if they are don’t already exist) and (b) take them from the lab into production.”
Our reading: Seven years might well not be enough to invent the further techniques needed, if they do not already exist, and to take them from the lab into production.
No horizon
Qualifier: if they don't already exist
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: Can’t be checkedSaid
Conditional forecast
“Things like programming verification might eventually help, but again, in deep learning we don’t yet have the tools for writing verifiable code. If you are to win the bet, we are probably going to have to solve that problem too, and soon.”
Our reading: Deep learning lacks tools for verifiable code, and AGI by 2029 would probably require solving that soon.
No horizon
Only if: if AGI is to arrive by 2029
Source: Dear Elon Musk, here are five things you might want to consider about AGI
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Against a long history of overclaiming in AI, such strong statements demand an awful lot of evidence. Because Google won’t allow skeptics to try the system, we can only make guesses. But from the few clues that have filtered out, there is enormous reason for skepticism.”
Our reading: From the few clues that have filtered out, there is enormous reason to be skeptical of Google's claim that Imagen has a deep level of language understanding, though without access to the system skeptics can only make guesses.
No horizon
Qualifier: from the few clues that have filtered out
Source: Horse rides astronaut
Assessment: Can’t be checkedSaid
Causal claim
“it casts doubt on “an astronaut riding a horse”, and suggests success there is not because the network knows how to extract meanings from individual words”
Our reading: Imagen's failure on 'a horse riding an astronaut' casts doubt on its success with 'an astronaut riding a horse' and suggests that this success comes not from extracting word meanings and combining them by syntax, but from something more holistic and approximate, a bit like keyword matching.
No horizon
Qualifier: casts doubt / suggests
Source: Horse rides astronaut
Assessment: Can’t be checkedSaid
Observation
“The answer to the second question—does Imagen have a deep understanding of language—is almost certainly “no”: if a system can draw a given picture with incantation X, but not with perfectly sensible alternative incantation Y, it’s pretty hard to say that the system understands Y.”
Our reading: Imagen almost certainly does not have a deep understanding of language.
No horizon
Source: Horse rides astronaut
Assessment: Can’t be checkedSaid
Observation
“DALL-E 2 is, I dare say, not as smart as it seems.”
Our reading: DALL-E 2 is not as smart as it appears.
No horizon
Source: Horse rides astronaut
Assessment: Can’t be checkedSaid
Observation
“Judging by all the premature declarations I have seen from the machine learning community of late, I am not sure AI has yet fully learned it.”
Our reading: The machine learning community has recently made premature declarations and may not have learned the Clever Hans lesson about overclaiming.
No horizon
Source: Horse rides astronaut
Assessment: Can’t be checkedSaid
Forecast
“A new breed of AI systems, with a firmer innate basis, closer to the Kantian model, but out of step with current Zeitgeist, might eventually tell us more.”
Our reading: New AI systems with a firmer innate, Kantian basis might eventually tell us more about the human mind.
No horizon
Source: Noam Chomsky and GPT-3
Assessment: Can’t be checkedSaid
Causal claim
“Human minds try to relate their language to what they know about the world; large language models don’t. Which means the latter just aren’t telling us much about the former.”
Our reading: Because large language models, unlike human minds, do not try to relate their language to what they know about the world, they just aren't telling us much about human minds.
No horizon
Source: Noam Chomsky and GPT-3
Assessment: Can’t be checkedSaid
Causal claim
“Large language models can, parasitically, glom onto human data, but they could glom onto any other data set just as well (e.g. programming languages); as such, they tell us far too little about how humans have come to be what they are.”
Our reading: Because large language models could glom onto any other data set (e.g. programming languages) just as well as human data, they tell us far too little about how humans have come to be what they are.
No horizon
Source: Noam Chomsky and GPT-3
Assessment: Can’t be checkedSaid
Causal claim
“Systems like GPT-3 don’t tell us why human languages have the special character that they do. As such, there is little explanatory value.”
Our reading: Because systems like GPT-3 don't tell us why human languages have the special character that they do, they have little explanatory value.
No horizon
Scope: systems like GPT-3
Source: Noam Chomsky and GPT-3
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Humans know these things; if AI’s are to understand language, they should refract language through this sort basic understanding of common sense, rather than needlessly starting as blank slates and acquiring statistics without concepts.”
Our reading: For AI to understand language, it should process language through built-in commonsense understanding instead of starting as a blank slate and learning statistics.
No horizon
Qualifier: if AI's are to understand language
Source: Noam Chomsky and GPT-3
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“They are an embarrassment to Locke’s conjecture, and a strong argument for exploring their very antithesis, which would be Kantian systems that start with representations of space and time and causality, and at least some kernels of universal grammar.”
Our reading: LLMs' failures argue strongly for exploring Kantian AI systems that start with built-in representations of space, time, causality and some universal grammar.
No horizon
Source: Noam Chomsky and GPT-3
Assessment: Can’t be checkedSaid
Observation
“We are in early days in AI.”
Our reading: AI is still at an early stage of development.
No horizon
Assessment: Can’t be checkedSaid
Observation
“But anyone who is candid will recognize that these kinds of errors reveal that something is, for now, deeply amiss.”
Our reading: Anyone who is candid will recognise that these kinds of errors by current AI systems reveal that something is, for now, deeply amiss.
No horizon
Qualifier: for now
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Let us all encourage a field that is open-minded enough to work in multiple directions, without prematurely dismissing ideas that happen to be not yet fully developed.”
Our reading: The AI field should pursue multiple research directions rather than dismissing undeveloped ideas prematurely.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“It may just be that the best path to artificial (general) intelligence isn’t through Alt Intelligence, after all.”
Our reading: The best path to AGI may not be through data-driven Alt Intelligence approaches.
No horizon
Assessment: Can’t be checkedSaid
Observation
“A large fraction of current AI researchers, or at least those currently in power, don’t (so far as I can tell) give a damn about any of this.”
Our reading: A large fraction of current AI researchers, or at least those currently in power, do not, so far as Marcus can tell, care about AI taking inspiration from natural intelligence.
No horizon
Scope: a large fraction of current AI researchers, or at least those currently in power
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“AI should certainly not be a slavish replica of human intelligence (which after all is flawed in its own ways, saddled with lousy memory and cognitive bias). But it should look to human (and animal cognition) for clues.”
Our reading: AI research should draw clues from human and animal cognition without slavishly replicating human intelligence.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“It may well be better than anything else we currently have, but the fact that it still doesn’t really work, even after all the immense investments that have been made in it, should give us pause.”
Our reading: Gato may well be better than anything else we currently have, but the fact that it still doesn't really work, even after immense investment, should give us pause.
No horizon
Qualifier: may well be better than anything else we currently have
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“It is sheer hubris to believe otherwise.”
Our reading: It is sheer hubris to believe that AGI can be built without learning from how humans reason, understand the world, and represent and acquire language and concepts.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“To some (such as myself), the abiding problem of discomprehension might—despite the immense progress—signal the need for a fundamental rethink, such as the one that Davis and I offered in our book Rebooting AI.”
Our reading: The persistent problem of discomprehension may signal that AI needs a fundamental rethink.
No horizon