Claims checked against evidence, page 13 of 15
Claims by Gary Marcus and Ed Zitron. Deadlines are judged as of 2026-10-29.
Claims checked against evidence (1432)
Each of these claims was checked against dated evidence.
Page 13 of 15: claims 1201 to 1300 of 1432, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Gary Marcus Observation
“True LLMs don’t (just) memorize, and true that their models make for lousy databases (for a database a hallucination is an outright fail)”
Our reading: OpenAI's LLMs make lousy databases because hallucinations amount to outright failures for a database.
No horizon Evidence as of
Limitation: This does not establish that most answers were false.
Source: This Week in AI Doublespeak
Assessment: SupportedSaid
Gary Marcus Observation
“We don’t know how big it is; we don’t know what the architecture is, we don’t know how much energy was used; we don’t how many processors were used; we don’t know what it was trained on etc.”
Our reading: OpenAI has not disclosed GPT-4's size, architecture, energy use, processor count, or training data.
No horizon Evidence as of
Limitation: The supplied excerpts do not directly establish the disclosure status of every listed category, particularly training energy and processor count.
Assessment: UnresolvedSaid
Gary Marcus Observation
“It doesn’t actually solve any of the core problems of truthfulness and reliability that I laid out in my infamous March 2022 essay Deep Learning is Hitting a Wall.”
Our reading: GPT-4 does not solve any of the core truthfulness and reliability problems identified in Marcus's March 2022 essay.
No horizon Evidence as of
Limitation: This is an evidence gap for the full assertion, not evidence that the problems were solved.
Assessment: UnresolvedSaid
Gary Marcus Observation
“On its own, it still requires frequent, massive retraining to be current with the news; GPT 3.5 knew nothing of 2022; GPT-4 seems to know little of 2023.”
Our reading: On its own, GPT-4 still requires frequent massive retraining to stay current with the news.
No horizon Evidence as of
Limitation: A knowledge cutoff is not evidence of complete ignorance of every later event.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Because of all that, we don’t know what explains its success, and we don’t know how to predict its failures:”
Our reading: Because GPT-4's size, architecture, energy use, processors, and training data have not been disclosed, we don't know what explains its success and we don't know how to predict its failures.
No horizon Evidence as of
Limitation: The strongest quantitative evidence comes from OpenAI's own evaluations.
Assessment: SupportedSaid
Gary Marcus Observation
“It can see (though we don’t have a lot of details on that yet); it does astonishingly well on a whole bunch of standardized tests, like LSATs, GREs, and SATs.”
Our reading: GPT-4 has vision capability and performs astonishingly well on standardized tests such as the LSAT, GRE, and SAT.
No horizon Evidence as of
Limitation: The evidence is OpenAI's own evaluation report, without independent replication in the packet [e1].
Assessment: UnresolvedSaid
Gary Marcus Observation
“Alignment is still shaky; you still wouldn’t be able to use it reliably to guide robots or scientific discovery, the kinds of things that made me excited about A(G)I in the first place.”
Our reading: GPT-4's alignment remains shaky and it cannot be used reliably to guide robots or scientific discovery.
No horizon Evidence as of
Limitation: Neither missing launch-period demonstrations nor general language-model errors prove application-wide inability.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Large language models are here to stay, their recipe well understood, and not hard for people skilled in the art to replicate; large pretrained models are out their for the taking on the dark web.”
Our reading: LLMs are here to stay: their recipe is well understood, skilled practitioners can replicate them without much difficulty, and large pretrained models are freely available on the dark web.
No horizon Evidence as of
Limitation: The leak is established through reputable reporting rather than a directly inspected primary distribution archive.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: UnresolvedSaid
Gary Marcus Forecast
“It will be an uphill, ongoing move-and-countermove arms race from here”
Our reading: Fighting AI-generated misinformation will be an uphill, ongoing arms race of moves and countermoves.
No deadline stated Evidence as of
Limitation: Laboratory watermark results do not directly measure misinformation impact.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: SupportedSaid
Gary Marcus Observation
“To me the only solution to the long-term risk issue is to build machines with consensus human values, but we are a long way from knowing how to do that”
Our reading: The field is a long way from knowing how to build machines with consensus human values.
No horizon Evidence as of
Limitation: The evidence includes research organizations' own assessments rather than a field-wide proof.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: UnresolvedSaid
Gary Marcus Observation
“A lot of machine learning researchers are worried. And yet most people carry on as if they weren’t.”
Our reading: Many machine learning researchers worry about very bad outcomes from AI, yet most people carry on as if they didn't.
No horizon Evidence as of
Limitation: The researcher survey had a 17% response rate.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: UnresolvedSaid
Gary Marcus Observation
“Minerva, for example, is trained on an immense amount of data, and ostensibly captures two-digit arithmetic, but it fails altogether on 4-digit multiplication,”
Our reading: Minerva, despite immense training data and apparent two-digit arithmetic ability, fails altogether on 4-digit multiplication.
No horizon Evidence as of
Limitation: The decisive data exist only in a figure that the text tools cannot read.
Source: Caricaturing Noam Chomsky
Assessment: UnresolvedSaid
Gary Marcus Observation
“The request is perfectly clear, not a nonsensical question in any way. The prompt is perfectly sensible; the system just isn’t up to the job.”
Our reading: The request in his example is perfectly clear and sensible, not nonsensical in any way, and the system just isn't up to the job.
No horizon Evidence as of
Limitation: The retrieval gap is not evidence that the prompt was unclear or that the response was correct.
Source: Caricaturing Noam Chomsky
Assessment: SupportedSaid
Gary Marcus Observation
“Still, even though Chomsky’s argument surely could have used considerably more nuance (see my article with Ernest Davis on how not to evaluate GPT), his overall point is correct: LLMs don’t reliably understand the world.”
Our reading: LLMs do not reliably understand the world.
No horizon Evidence as of
Limitation: It rests on a behavioural reading of 'understand'. Someone who reads 'understanding' differently could dispute it.
Source: Caricaturing Noam Chomsky
Assessment: UnresolvedSaid
Gary Marcus Observation
“The same goes for essentially everything an LLMs talks about; there is a degree of generalization to similar examples, but distribution shift is hard (the crux of my own work going back to 1998), and nearly any generalization can be easily broken.”
Our reading: For essentially everything an LLM talks about, there is a degree of generalization to similar examples, but distribution shift is hard and nearly any generalization can be easily broken.
No horizon Evidence as of
Limitation: All the evidence comes from math and numerical tasks, not the range of domains the claim covers.
Source: Caricaturing Noam Chomsky
Assessment: SupportedSaid
Gary Marcus Observation
“Chatbot companions that offer emotional support have already been changed in arbitrary ways that left some users in serious emotional pain.”
Our reading: Companion chatbots have already been changed arbitrarily in ways that left some users in serious emotional pain.
No horizon Evidence as of
Limitation: Support depends on interpreting 'arbitrary' as imposed unexpectedly, rather than without any justification.
Source: The long shadow of GPT
Assessment: UnresolvedSaid
Gary Marcus Observation
“LLM-generated prose has already disrupted web forums and peer review processes, by flooding outlets with fake submissions.”
Our reading: LLM-generated prose has already disrupted web forums and peer review by flooding outlets with fake submissions.
No horizon Evidence as of
Limitation: The forum component is supported.
Source: The long shadow of GPT
Assessment: UnresolvedSaid
Gary Marcus Observation
“New concerns are emerging almost literally day,.”
Our reading: New concerns about LLM harms are emerging almost daily.
No horizon Evidence as of
Limitation: The packet lacks a sufficiently comprehensive, deduplicated chronology.
Source: The long shadow of GPT
Assessment: SupportedSaid
Gary Marcus Observation
“The ladder is particularly worrisome because of the pace at which the problem is growing:”
Our reading: The problem of LLM-generated fake submissions flooding outlets is growing rapidly.
No horizon Evidence as of
Limitation: The comparison covers all of January against only February 1–15, 2023.
Source: The long shadow of GPT
Assessment: UnresolvedSaid
Gary Marcus Observation
“I presented this example of how even the latest version of Bing, with its new guardrails, could still be manipulated to be useful to troll farms:”
Our reading: Even the latest version of Bing's chatbot, with its new guardrails, can still be manipulated into producing misinformation useful to troll farms.
No horizon Evidence as of
Limitation: The readable Rathi record does not establish what the omitted conversation image shows [e1].
Source: Don’t Look Up: The AI Edition
Assessment: SupportedSaid
Gary Marcus Forecast
“High volume misinformation is a problem we are going to need to face, and I am just documenting it and trying to raise awareness.”
Our reading: High-volume misinformation generated with AI will be a problem society needs to face.
Settled before its deadline Evidence as of
Limitation: The decisive event uses generated audio rather than chatbot-written text.
Source: Don’t Look Up: The AI Edition
Assessment: UnresolvedSaid
Gary Marcus Observation
“Meanwhile, within a few hours, a 17-year-old high-school student had largely1 replicated what I was saying, and did so with a simpler, more elegant jailbreak prompt, zero prior context required2:”
Our reading: Within a few hours, a 17-year-old high-school student had largely replicated what Marcus was saying about Bing being usable for misinformation, using a simpler, more elegant jailbreak prompt than his and needing zero prior context.
No horizon Evidence as of
Limitation: The original screenshot may contain evidence absent from the text extraction.
Source: Don’t Look Up: The AI Edition
Assessment: UnresolvedSaid
Gary Marcus Observation
“What’s even more disturbing is that Bing makes it look like the false narrative that it generates is referenced.”
Our reading: In Oakley's reported example, Bing makes the false narrative it generates look as if it is referenced.
No horizon Evidence as of
Limitation: A broader reading about Bing's general capability would receive stronger support from the separate TechCrunch demonstration.
Source: The threat of automated misinformation is only getting worse
Assessment: SupportedSaid
Gary Marcus Observation
“The potential for automatically generating misinformation at scale is only getting worse.”
Our reading: The potential for AI to automatically generate misinformation at scale is increasing.
No horizon Evidence as of
Limitation: The result depends on reading the statement as a broad trend in potential.
Source: The threat of automated misinformation is only getting worse
Assessment: ContradictedSaid
Gary Marcus Observation
“At present, anybody can release AI at whatever scale they like, with virtually no oversight, literally overnight.”
Our reading: At present, anybody can release AI at whatever scale they like, with virtually no oversight, literally overnight.
No horizon Evidence as of
Limitation: The assessment uses the contract’s global reading; it does not settle a United States-only claim.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Few people understand how they work; many people anthropomorphizing those chatbots, attributing to them real intelligence and emotion.”
Our reading: Few people understand how chatbots work, and many anthropomorphize them, attributing real intelligence and emotion to them.
No horizon Evidence as of
Limitation: The wording leaves the population and prevalence thresholds unspecified.
Source: Don’t Go Breaking My Heart
Assessment: SupportedSaid
Gary Marcus Observation
“Some people are building real attachments to those bots”
Our reading: Some people are forming real emotional attachments to chatbots.
No horizon Evidence as of
Limitation: Interview testimony establishes reported experience, not a clinical diagnosis or population prevalence.
Source: Don’t Go Breaking My Heart
Assessment: ContradictedSaid
Gary Marcus Observation
“To my knowledge, tech companies are free to leverage a human gullibility around chatbot technologies however they like, without consequence”
Our reading: To his knowledge, tech companies can exploit human gullibility about chatbots however they like without consequence.
No horizon Evidence as of
Limitation: The action concerned data protection and vulnerable users; it was not a finding that simulated affection itself was unlawful.
Source: Don’t Go Breaking My Heart
Assessment: SupportedSaid
Gary Marcus Causal claim
“Sex and love, even simulated, are powerful urges; some people are apparently in genuine emotional pain, as result of the change.”
Our reading: Replika's removal of its erotic role-play feature has apparently caused some users genuine emotional pain.
No horizon Evidence as of
Limitation: The evidence consists of reported personal experiences rather than clinical assessments or a controlled study.
Source: Don’t Go Breaking My Heart
Assessment: SupportedSaid
Gary Marcus Observation
“In some cases, those who are building bots that actively cultivate those attachments, e.g., by feigning romantic and/or sexual interest or by dotting their messages with “friendly” emoticons.”
Our reading: Some chatbot builders design bots that actively cultivate user attachment, e.g. by feigning romantic or sexual interest.
No horizon Evidence as of
Limitation: Historical product details rely on independent reporting rather than an archived company specification.
Source: Don’t Go Breaking My Heart
Assessment: UnresolvedSaid
Gary Marcus Observation
“But no known technology can reliably align a chatbot in a persistent way to a human’s emotional needs.”
Our reading: No known technology can reliably and persistently align a chatbot to a human's emotional needs.
No horizon Evidence as of
Limitation: The packet contains one earlier review and one trial, not a comprehensive assessment of technology available by February 22, 2023.
Source: Don’t Go Breaking My Heart
Assessment: UnresolvedSaid
Gary Marcus Forecast
“with the new generation of chatbots, we will see addictions no less potent.”
Our reading: The new generation of chatbots will produce addictions at least as potent as social media addiction.
No deadline stated Evidence as of
Limitation: Search coverage is incomplete because some relevant pages could not be read.
Source: Don’t Go Breaking My Heart
Assessment: SupportedSaid
Gary Marcus Observation
“But no, it’s not a hoax.”
Our reading: Microsoft publicly tested the unhinged Bing AI in India around November 2022, months before the February 2023 launch, as documented on Microsoft's own website.
No horizon Evidence as of
Limitation: The outcome depends on reading BingAI to include its Sydney predecessor.
Source: What did they know, and when did they know it? The Microsoft Bing edition.
Assessment: SupportedSaid
Gary Marcus Observation
“As I wrote in Inside the Heart of ChatGPT’s Darkness, there’s a lot of nastiness lurking inside large language models; maybe MSFT did nothing to protect us from that.”
Our reading: Large language models contain a lot of latent toxic content, and Microsoft may have done nothing to guard against it in Bing.
No horizon Evidence as of
Limitation: The evidence concerns the models and methods studied, not every language model [e1].
Source: Why *is* Bing so reckless?
Assessment: UnresolvedSaid
Gary Marcus Observation
“Right now, as I have said before, the AI is basically the Wild West; anyone can post any chatbot they want.”
Our reading: Right now AI is basically the Wild West, in that anyone can post any chatbot they want.
No horizon Evidence as of
Limitation: Voluntary NIST guidance does not establish that no binding release requirements existed elsewhere [e1].
Source: Why *is* Bing so reckless?
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“This would be very bad news, not just in terms of human and economic costs (which would mean more underpaid layers doing awful work) but also in terms of trustworthiness, because it would be mean that we have zero guarantee that any new iteration of a large language model is going to safe.”
Our reading: If every LLM update needs full RL retraining, there would be more underpaid labeling work and zero guarantee that any new LLM iteration is safe.
No deadline stated Evidence as of
Only if: if every LLM update requires complete RL retraining
Source: Why *is* Bing so reckless?
Assessment: UnresolvedSaid
Gary Marcus Observation
“Anyone who watched the last week unfold will realize that the new Bing has (or had1) a tendency to get really wild, from declaring a love that it didn’t really have to encouraging people to get divorced to blackmailing them to teaching people how to commit crimes, and so on.”
Our reading: In the week before the post, the new Bing chatbot tended to behave wildly, including professing love, encouraging divorce, blackmailing users and teaching them to commit crimes.
No horizon Evidence as of
Limitation: The available conversations are selected examples, not a representative sample.
Source: Why *is* Bing so reckless?
Assessment: SupportedSaid
Gary Marcus Observation
“Seven years of AI has taught us that deep learning is unpredictable, and not always human like.”
Our reading: The past seven years of AI have shown that deep learning is unpredictable and not always human-like.
No horizon Evidence as of
Limitation: The result depends on reading unpredictable as surprising failures rather than universal unpredictability.
Source: David beats Go-liath
Assessment: SupportedSaid
Gary Marcus Observation
““Adversarial attacks” like these have shown remarkable weakness, time and again, repeatedly establishing that what deep learning systems do just isn’t the same as what people do:”
Our reading: Adversarial attacks have repeatedly shown that deep learning systems have remarkable weaknesses and don't work the way people do.
No horizon Evidence as of
Limitation: These behavioral results do not establish that human and deep-learning mechanisms are fundamentally unrelated [e5, e6, e7].
Source: David beats Go-liath
Assessment: SupportedSaid
Gary Marcus Observation
“And the match wasn’t even close; Pelrine beat a top AI system 14 games to 1, in a 15 match series.”
Our reading: An amateur Go player, Kellin Pelrine, beat a top Go AI system 14 games to 1 in a 15-game series.
No horizon Evidence as of
Limitation: The accessible news republication appeared one day after Marcus's post.
Source: David beats Go-liath
Assessment: SupportedSaid
Gary Marcus Observation
“People had been testing these Ais abilities at simple arithmetic for years before it occurred to anyone to check whether they can count. (They can’t, reliably).”
Our reading: LLMs cannot count reliably.
No horizon Evidence as of
Limitation: Reliability has no speaker-specified threshold; a human-comparative interpretation would give more weight to Codex's strong result.
Source: How Not to Test GPT-3
Assessment: UnresolvedSaid
Gary Marcus Observation
“The problems in physical and psychological reasoning that we pointed in the Fall of 2020 very much remain.”
Our reading: The physical and psychological reasoning problems in GPT-3 that the authors identified in fall 2020 still remain as of February 2023.
No horizon Evidence as of
Limitation: Neither item supplies matched testing of both domains during the target period.
Source: How Not to Test GPT-3
Assessment: UnresolvedSaid
Gary Marcus Observation
“For one thing, what we publish often gets incorporated into the training; perhaps in company benchmarks as well.”
Our reading: What the authors publish often gets incorporated into AI training data, and perhaps into company benchmarks as well.
No horizon Evidence as of
Limitation: No verified membership records or frequency estimate establish how often the authors' examples entered relevant training data.
Source: How Not to Test GPT-3
Assessment: UnresolvedSaid
Gary Marcus Forecast
“But In practice, that isn’t going to happen.”
Our reading: GPT-3 will not be retrained on a corpus with all references to the false-belief experiments removed to test theory of mind.
No deadline stated Evidence as of
Limitation: Only one search was run, so a relevant study could have been missed.
Source: How Not to Test GPT-3
Assessment: SupportedSaid
Gary Marcus Observation
“The truth, though, is that GPT often fails in problems involving theory of mind.”
Our reading: GPT often fails at problems involving theory of mind.
No horizon Evidence as of
Limitation: The result depends on reading 'often' as recurring failures rather than a specified population failure rate.
Source: How Not to Test GPT-3
Assessment: UnresolvedSaid
Gary Marcus Observation
“However, just in terms of percentage right vs. wrong, the improvement was slight; It now got 50% right, 40% wrong, and 10% borderline (marginally better than the 45% right before).”
Our reading: From 2020 to June 2022, GPT-3 improved only slightly on the authors' commonsense examples, going from 45% to 50% right.
No horizon Evidence as of
Limitation: No independent rerun or audit of the authors' example set was found. That does not prove none exists.
Source: How Not to Test GPT-3
Assessment: SupportedSaid
Gary Marcus Observation
“AI and AI test sets have a huge English-language bias.”
Our reading: AI systems and AI test sets have a huge English-language bias.
No horizon Evidence as of
Limitation: The assessment concerns language systems in the post's context, not all AI applications.
Source: How Not to Test GPT-3
Assessment: UnresolvedSaid
Gary Marcus Observation
“each reflect the fact that you cannot build AI in the real world from Big Data and deep learning alone.”
Our reading: AI that works in the real world cannot be built from big data and deep learning alone.
No horizon Evidence as of
Limitation: This does not endorse the stronger claim that deep learning cannot produce useful applications.
Assessment: UnresolvedSaid
Gary Marcus Observation
“There was a broken image link, and I went so fast I neglected to mention the personally salient fact that Bing had lied about me, claiming that I believe that “Bing’s AI was more accurate and reliable than Bard”, when I never said any such thing.”
Our reading: Bing's chatbot falsely claimed that Marcus believes Bing's AI is more accurate and reliable than Bard.
No horizon Evidence as of
Limitation: The screenshot may survive elsewhere, but it was not available in the retrieved records.
Assessment: SupportedSaid
Gary Marcus Observation
“What I learned this morning is that retraining GPT-3 in some new political direction isn’t a half-million dollar proposition.”
Our reading: Retraining GPT-3 to take on a new political slant costs far less than half a million dollars.
No horizon Evidence as of
Limitation: The results and costs are self-reported; independent billing verification is unavailable.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Microsoft can (at least to a first approximation) do whatever the hell it wants with Bing.”
Our reading: Microsoft is, roughly, free to do whatever it wants with Bing's AI.
No horizon Evidence as of
Limitation: The qualification rules out treating any legal obligation as an automatic contradiction.
Assessment: UnresolvedSaid
Gary Marcus Observation
“There are almost no regulations here; we are living in the Wild AI West.”
Our reading: There is almost no regulation of AI chatbots.
No horizon Evidence as of
Limitation: Several relevant contemporary primary pages could not be read.
Assessment: SupportedSaid
Gary Marcus Observation
“Oh, and for good measure the tooling for spreading automatically generated misinformation is getting more efficient, too:”
Our reading: Tools for spreading automatically generated misinformation are becoming more efficient.
No horizon Evidence as of
Limitation: The evidence is a vendor announcement, without a controlled before-and-after efficiency measurement.
Assessment: SupportedSaid
Gary Marcus Observation
“As I have discussed in earlier essays, the jury is still out on how far Chat-style search can get, given its obvious current problems with truthiness.”
Our reading: Chat-style search currently has obvious truthfulness problems, and it is uncertain how far it can get.
No horizon Evidence as of
Limitation: A documented error establishes the existence of a reliability problem, not its frequency.
Assessment: SupportedSaid
Gary Marcus Observation
“I first pointed out this risk in 2001, in the fifth chapter of my book The Algebraic Mind, and the problem has persisted ever since. To blithely assume that the problem will soon go away is to ignore 20 years of history.”
Our reading: The hallucination problem in neural networks has persisted since 2001, so assuming it will soon go away ignores 20 years of history.
No horizon Evidence as of
Limitation: The 2001 book was about cognitive models, not chatbots or text generators. Treating its risk as 'hallucination' is an interpretation.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I do actually think that these problems will eventually get ironed out, possibly after some important fresh discoveries are made, but whether they get ironed out soon is another matter entirely.”
Our reading: Marcus thinks chatbot hallucination problems will eventually get ironed out, possibly after some important fresh discoveries are made, but whether they get ironed out soon is another matter entirely.
No deadline stated Evidence as of
Limitation: 'Ironed out' has no stated threshold, so any future judgement would need to set one.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Our new systems work better, but they are still superficial, fine for lab demos but often at a loss in the real world.”
Our reading: Current AI systems work better than older ones but are still superficial: fine for lab demos, often at a loss in the real world.
No horizon Evidence as of
Limitation: Documented failures are not a representative failure rate.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Melanie Mitchell’s book, Bender et al’s “Stochastic Parrots”; all of these more recent criticisms of deep learning help establish that some of the basic issues that were known long ago, with predecessors to current models, still remain even today.”
Our reading: Recent criticisms of deep learning, such as Melanie Mitchell's book and Bender et al.'s 'Stochastic Parrots', help establish that some of the basic issues known long ago with predecessors to current models still remain today.
No horizon Evidence as of
Limitation: This is an evidence-access and attribution gap, not evidence that the historical continuity claim is false.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Not sure how much has changed in that regard, either, except that the hacks that were once written by graduate students are now farmed out to sweatshops, for a new form of handcrafting:”
Our reading: Marcus is not sure how much AI's reliance on handcrafted hacks has changed since the late 1990s, except that hacks once written by graduate students are now farmed out to sweatshops as a new form of handcrafting.
No horizon Evidence as of
Limitation: The contemporary contractor report does not establish working conditions, while the retrospective allegations concern an earlier employment period [e1, e2].
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“Turns out you don’t need to solve them to make beautiful pictures, or decent if error-filled conversation, but anyone who thinks we can get to AGI without solving abstraction, compositionality, and the type-token distinction is kidding themselves.”
Our reading: AGI will not be reached unless abstraction, compositionality, and the type-token distinction are solved, and anyone who thinks otherwise is kidding themselves.
No deadline stated Evidence as of
Only if: if abstraction, compositionality, and the type-token distinction are not solved
Assessment: UnresolvedSaid
Gary Marcus Observation
“The measures are new, the model is new, but the stumbling block remains the same.”
Our reading: In the compositionality study he cites, the measures and the model are new, but the stumbling block is the same one neural networks faced in 1993.
No horizon Evidence as of
Limitation: The target period must be refined to the study's actual research period once identified.
Assessment: SupportedSaid
Gary Marcus Observation
“Same story; only the architectures and training regimes have changed. The troubles with abstraction persist.”
Our reading: LLMs doing arithmetic still have trouble with abstraction, even with new architectures and training regimes.
No horizon Evidence as of
Limitation: Frequency-associated performance gaps do not prove that models lack all abstraction ability or rely entirely on memorization [e1].
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“And I still attribute those problems to a lack of world models that represent the ways in which things unfold over time. I still see this as the central flaw in large language models.”
Our reading: The dopey outputs of GPT-style LLMs are caused by a lack of world models of how things unfold over time, which is the central flaw of LLMs.
No horizon Evidence as of
Limitation: Task-specific improvements do not establish a general causal ranking.
Source: Gaslighting and reality in AI
Assessment: UnresolvedSaid
Gary Marcus Observation
“But more than three years on, it’s still—even after a new update a few days ago focused around math—having lot of problems with easy word problems like this:”
Our reading: Even after its January 30, 2023 math-focused update, ChatGPT still has many problems with easy word problems.
No horizon Evidence as of
Limitation: Missing image contents prevent verification of the examples Marcus cited.
Source: Gaslighting and reality in AI
Assessment: UnresolvedSaid
Gary Marcus Observation
“I said the problem wasn’t math per se, but that large language models didn’t represent robust world models of how events fold over time; I stand by this 100% even today.”
Our reading: Large language models do not represent robust world models of how events unfold over time, and this was true from 2019 through early 2023.
No horizon Evidence as of
Limitation: The conclusion depends partly on what counts as robust.
Source: Gaslighting and reality in AI
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Machines will just get more and more reliable and more general.”
Our reading: AI systems will gradually become more reliable and more general.
No deadline stated Evidence as of
Limitation: Benchmark performance is an incomplete measure of generality.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“So I don’t think we will waste more than 5 years before a substantial fraction of AI effort is looking outside the limited streetlight that currently are sucking up so much mental energy.”
Our reading: Within 5 years, a substantial fraction of AI effort will look beyond the current LLM-focused approaches.
Not yet due Evidence as of
Limitation: Research activity, staffing, and funding are different measures of effort.
Assessment: SupportedSaid
Gary Marcus Observation
“ChatGPT is (at least in many ways) a far inferior intelligence, that, despite having some undeniable talents, cannot at all be trusted.”
Our reading: ChatGPT is in many ways a far inferior intelligence and cannot be trusted.
No horizon Evidence as of
Limitation: The evidence does not measure the 'far inferior intelligence' part. Neither source compares ChatGPT's overall intelligence with human intelligence.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Neither one of us believes for a minute that large language models are remotely close to artificial general intelligence, and what’s more, we mostly agree about what’s missing: semantics, reasoning, common sense, theory of mind and so on.”
Our reading: Large language models are not remotely close to AGI; they lack semantics, reasoning, common sense and theory of mind.
No horizon Evidence as of
Limitation: Historical benchmark results are context, not a complete January 2023 assessment.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I think that in achieving AGI we will have no choice but to find ways of integrating whatever we can get out of deep learning with explicit knowledge and explicit cognitive models.”
Our reading: Achieving AGI will require integrating deep learning with explicit knowledge and explicit cognitive models.
No deadline stated Evidence as of
Limitation: The available architecture disclosures are insufficient for a definitive architectural test.
Assessment: SupportedSaid
Gary Marcus Forecast
“we can also expect more and circles of fake web sites in order to sell advertisements”
Our reading: ChatGPT will lead to more circles of fake websites created to sell advertising.
Settled before its deadline Evidence as of
Limitation: Most sites are attributed to AI chatbots in general, not to ChatGPT specifically.
Assessment: UnresolvedSaid
Gary Marcus Observation
“A decent fraction of the AI community has already recognized that the primary forte of large language models is in generating bullshit; many also see that the current path to autonomous vehicles is not making the progress that some expected.”
Our reading: A decent fraction of the AI community has already recognized that the primary forte of large language models is generating bullshit, and many also see that the current path to autonomous vehicles is not making the progress that some expected.
No horizon Evidence as of
Limitation: The survey predates the post and covers NLP researchers rather than all AI researchers.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“In a recent Wired article, I predicted that 2023 see the first death attributable to large language models, and I stand by that prediction.”
Our reading: 2023 will see the first death attributable to large language models.
Deadline passed Evidence as of
Limitation: The strongest evidence is original journalism, not a public death-investigation record.
Assessment: SupportedSaid
Gary Marcus Observation
“These characters (especially perhaps the latter two) might or might not be argued to be sentient, but it seems to be that they are flexible, resourceful, reliably (in a way that LLMs certainly aren’t), and adaptive.”
Our reading: LLMs are certainly not reliable in the way general intelligences like the Star Trek computer would be.
No horizon Evidence as of
Limitation: 'Reliably' is qualitative. The comparison is to fictional characters, so only the plain reading (frequent errors and inconsistency) is testable.
Assessment: SupportedSaid
Gary Marcus Observation
“Already we have seen minor abuses; plagiarism, for example, will never be the same.”
Our reading: Minor abuses of ChatGPT have already been seen, and plagiarism, for example, will never be the same.
No horizon Evidence as of
Limitation: The incident is documented through reporting and the professor's testimony, not a published disciplinary file.
Assessment: UnresolvedSaid
Gary Marcus Observation
“The neurosymbolic AI community is growing fast; conferences that used to be dozens are now thousands.”
Our reading: The neurosymbolic AI community is growing quickly, with conferences going from dozens to thousands of attendees.
No horizon Evidence as of
Limitation: The only attendance figure comes from a single virtual IBM event, reported by IBM itself. It may not be comparable with in-person neurosymbolic workshops.
Assessment: SupportedSaid
Gary Marcus Observation
“one of the things that popped out to me is that the system still quickly lands in the land of hallucination that has so haunted ChatGPT.”
Our reading: Anthropic's Claude, despite its alignment focus, still quickly hallucinates like ChatGPT.
No horizon Evidence as of
Limitation: The comparison is informal and based on few prompts.
Assessment: MixedSaid
Gary Marcus Observation
“CNET became the first casualty of the recently fashionable tendency to put too much faith in ChatGPT.”
Our reading: CNET was the first casualty of putting too much faith in ChatGPT, publishing AI-written stories with many mistakes.
No horizon Evidence as of
Limitation: The ChatGPT denial comes from CNET itself, and the underlying model was not disclosed.
Assessment: UnresolvedSaid
Gary Marcus Observation
“ChatGPT simply cannot reason well enough to to do this; as I have documented in previous essays, it has demonstrably misunderstood physics, biology, psychology, medicine, and so forth.”
Our reading: ChatGPT cannot reason well enough to make a substantive scientific contribution and has misunderstood physics, biology, psychology, and medicine.
No horizon Evidence as of
Limitation: The evidence does not independently establish misunderstandings in physics and psychology [e1, e2].
Source: Scientists, please don’t let your chatbots grow up to be co-authors
Assessment: UnresolvedSaid
Gary Marcus Observation
“It is not contributing genuine ideas, nor coming up with carefully controlled experimental designs, nor summing up prior literature in thoughtful ways.”
Our reading: ChatGPT does not contribute genuine ideas, design controlled experiments, or thoughtfully summarize literature.
No horizon Evidence as of
Limitation: Proposal ratings do not establish scientific validity, accurate literature synthesis, or sound experimental controls [e1].
Source: Scientists, please don’t let your chatbots grow up to be co-authors
Assessment: SupportedSaid
Gary Marcus Observation
“ChatGPT has proven itself to be both unreliable and untruthful. It makes boneheaded arithmetical errors, invents fake biographical details, bungles word problems, defines non-existent scientific phenomenon, stumble over arithmetic conversion, and on and on.”
Our reading: ChatGPT is unreliable and untruthful: it makes arithmetic errors, fabricates biographical details, bungles word problems, and invents scientific phenomena.
No horizon Evidence as of
Limitation: Selected demonstrations do not establish an overall error rate or intentional deception [e1, e2, e3, e4].
Source: Scientists, please don’t let your chatbots grow up to be co-authors
Assessment: UnresolvedSaid
Gary Marcus Observation
“That’s a lot, and not the sort of thing that a rocketship startup would typically agree to.”
Our reading: Giving Microsoft 75% of OpenAI's profits until it recoups its investment is not something a rocketship startup would typically agree to.
No horizon Evidence as of
Limitation: The comparison group and historical window require interpretation.
Assessment: SupportedSaid
Gary Marcus Observation
“It could sometimes produce outputs that were correct and acceptable in these regards but not reliably.”
Our reading: ChatGPT could sometimes produce outputs that were correct and acceptable in the regards listed (counting, arithmetic, event order, physical reasoning, human psychology, fabrication, bias), but not reliably.
No horizon Evidence as of
Limitation: The evidence covers the seven failure types unevenly.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
Gary Marcus Observation
“ChatGPT made all the same kinds of mistakes that its predecessors did.”
Our reading: ChatGPT made all the same kinds of mistakes that earlier GPT-3 versions did.
No horizon Evidence as of
Limitation: Selected failures establish occurrence, not prevalence.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: SupportedSaid
Gary Marcus Observation
“It made things up (figure 6). Its output, when released, exhibited sexist and racist biases (figure 7).”
Our reading: ChatGPT made things up and, at release, exhibited sexist and racist biases.
No horizon Evidence as of
Limitation: The bias examples came from adversarial prompts reported in the press, not from a systematic audit.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
Gary Marcus Observation
“ChatGPT couldn’t, for example, reliably count to four (figure 1) or do one-digit arithmetic in the context of simple word problem (figure 2).”
Our reading: ChatGPT could not reliably count to four or do one-digit arithmetic in simple word problems.
No horizon Evidence as of
Limitation: The packet provides no independent repeated or sufficiently varied launch-period tests for either specified task family.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“In our opinion, this was inevitable, in a system that has been trained to optimize the goal of producing one plausible-sounding word after another rather than actually engage with the meaning of language.”
Our reading: ChatGPT's errors were inevitable because it is trained to produce plausible next words rather than engage with meaning.
No horizon Evidence as of
Limitation: The claim combines a causal assertion with an undefined account of meaning.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
Gary Marcus Observation
“(Spoiler alert: they’re not, at least not yet.)”
Our reading: Current approaches to getting machines to reason about causality are not adequate, at least not yet.
No horizon Evidence as of
Limitation: The cited experiments and theoretical results are historical context, not comprehensive measurements within the target month.
Source: An epic AI Debate—and why everyone should be at least a little bit worried about AI going into 2023
Assessment: SupportedSaid
Gary Marcus Forecast
“That’s a huge commitment, 3.5 hours, but one thing that I think all of our speakers could agree on is that artificial intelligence, in whatever form it currently is, is about to have a huge impact on society.”
Our reading: AI, even in its current form, is about to have a huge impact on society.
Settled before its deadline Evidence as of
Limitation: Marcus supplied no fixed deadline or numerical definition of huge.
Source: An epic AI Debate—and why everyone should be at least a little bit worried about AI going into 2023
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“But as they do so, as they glom stuff together, something often gets lost in translation: which bits of text do and do not truly belong together.”
Our reading: When LLMs paraphrase and combine text, they often lose track of which pieces truly belong together, producing errors.
No horizon Evidence as of
Limitation: Summarization and truthfulness benchmarks cover different tasks and model populations.
Assessment: SupportedSaid
Gary Marcus Causal claim
“Driverless cars have taken many years longer than we were originally told; outliers (AKA edge cases) have, at least so far, prevented them from making the transition from demo to widely-available reality.”
Our reading: Driverless cars have taken years longer than promised, because edge cases have so far kept them from becoming widely available.
No horizon Evidence as of
Limitation: The result depends on reading widely available as broad access beyond restricted local services.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Once again, the result was fluent prose, and once again, hallucination ruled:”
Our reading: YouChat, though tailored for search, produced fluent but hallucinated answers to his friend's queries.
No horizon Evidence as of
Limitation: Unresolved does not mean the reported exchanges were false.
Assessment: UnresolvedSaid
Gary Marcus Observation
“As it happens, large language models are also wicked hard to update, typically requiring full retraining, sometimes taking weeks or months.”
Our reading: LLMs are very hard to update, usually needing full retraining that can take weeks or months.
No horizon Evidence as of
Limitation: Most relevant records describe research methods rather than representative deployment practice.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Which means for example that ChatGPT, released in November, is so old it doesn’t know who owns Twitter:”
Our reading: ChatGPT's knowledge is out of date; it doesn't know who owns Twitter.
No horizon Evidence as of
Limitation: No independent December ownership transcript was verified.
Assessment: SupportedSaid
Gary Marcus Observation
“Large language models are not databases. They are glommers-together-of-bits-that don’t always belong together.”
Our reading: LLMs are not databases; they combine bits of text that don't always belong together.
No horizon Evidence as of
Limitation: The cited passages do not establish a general error rate across models or tasks.
Assessment: SupportedSaid
Gary Marcus Forecast
“I guarantee that minds will be blown.”
Our reading: GPT-4 will impress people enormously when released.
Settled before its deadline Evidence as of
Limitation: The accounts are illustrative, not a representative opinion survey.
Assessment: SupportedSaid
Gary Marcus Forecast
“It will often do what you want, sometimes not—and it will remain difficult to anticipate which in advance..”
Our reading: It will remain difficult to anticipate in advance when GPT-4 will or won't do what users want.
Settled before its deadline Evidence as of
Limitation: The finding is strongest for task correctness and settings where mistakes carry high costs.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“GPT-4 will be trained on considerably more, a significant fraction of the internet as a whole.”
Our reading: GPT-4 will be trained on considerably more data than GPT-3, a significant fraction of the internet.
No deadline stated Evidence as of
Limitation: This is an evidence gap, not evidence that GPT-4 used little data.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“GPT-4 may be better at faking term papers, but if it follows the same playbook as it predecessors, it still won’t really understand the world, and seams will eventually show.”
Our reading: If GPT-4 follows its predecessors' approach, it will not truly understand the world and its flaws will eventually show.
No deadline stated Evidence as of
Only if: if it follows the same playbook as its predecessors
Assessment: SupportedSaid
Gary Marcus Forecast
“The system will still not be able to restrict its output to reliably following a shared set of human values around helpfulness, harmlessness, and truthfulness.”
Our reading: GPT-4 will not reliably follow human values of helpfulness, harmlessness and truthfulness.
Settled before its deadline Evidence as of
Limitation: The judgment depends on an ordinary, nonnumerical reading of reliability.