Unresolved claims by Gary Marcus, page 9 of 11
Deadlines are judged as of 2026-10-29. About Gary Marcus and the sources covered.
Unresolved claims by Gary Marcus (1005)
The record was checked, and the evidence available by the cutoff does not settle it. Forecasts whose deadline has not arrived are listed apart, as not yet due.
Page 9 of 11: claims 801 to 900 of 1005, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: UnresolvedSaid
Observation
“And the financial challenges are starting to mount up.”
Our reading: Financial challenges facing ChatGPT/OpenAI were starting to mount as of August 2023.
No horizon Evidence as of
Limitation: A narrower reading limited to accumulating legal risks has more support than a reading about demonstrated financial deterioration.
Source: The Rise and Fall of ChatGPT?
Assessment: UnresolvedSaid
Causal claim
“This is even more true four years later, now that we have systems that ingest massive amounts of data from both public and commercial sources, quite possibly including eg sample law school and medical exams etc.”
Our reading: Because current systems ingest massive data possibly including sample exams, benchmark success is even less indicative of intelligence than in 2019.
No horizon Evidence as of
Limitation: OpenAI's audit was a developer assessment with acknowledged detection limits.
Assessment: UnresolvedSaid
Observation
“We are not even close to machines that exceed the capacity of typical humans to cope with unusual circumstances while driving”
Our reading: Machines are not close to exceeding typical humans at coping with unusual circumstances while driving.
No horizon Evidence as of
Limitation: Official complaints document incidents rather than controlled comparisons.
Assessment: UnresolvedSaid
Observation
“Without a sense of what it is in the training corpus, it’s almost impossible to conclude anything about what a machine truly understands and how general that is.”
Our reading: Without knowing the training corpus, one can hardly conclude what a machine understands or how general it is.
No horizon Evidence as of
Limitation: A reading limited to whether any bounded behavioral conclusion is possible would weigh more strongly against Marcus.
Assessment: UnresolvedSaid
Observation
“Humans can write summaries of things without hallucination; LLMs cannot do so reliably.”
Our reading: LLMs cannot reliably write summaries without hallucinating.
No horizon Evidence as of
Limitation: The inferred monthly observation window is narrower than a general review of 2023 models.
Assessment: UnresolvedSaid
Observation
“18 months ago, before the LLM euphoria, we all took for granted that AGI wasn’t just about beating ordinary humans.”
Our reading: Around early 2022, before LLM euphoria, the field assumed AGI meant more than beating ordinary humans.
No horizon Evidence as of
Limitation: The later review provides historical definitions, not a representative measurement of February 2022 views [e1].
Assessment: UnresolvedSaid
Observation
“in my view it’s a subtle redefinition that dramatically moves the goal posts.”
Our reading: In Marcus's view, Legg's definition of AGI as better than typical human performance on virtually all cognitive tasks is a subtle redefinition that dramatically moves the goal posts, compared with how Legg and Marcus publicly discussed the term last year.
No horizon Evidence as of
Limitation: Missing original exchange records prevent a finding either way.
Assessment: UnresolvedSaid
Observation
“Nobody has yet figured out how to build a driverless car that can deal with that fact.”
Our reading: Nobody has yet figured out how to build a driverless car that can deal with the real world's endless crazy and out-of-the-ordinary circumstances.
No horizon Evidence as of
Limitation: Reliable handling and acceptable human assistance are not defined in the source.
Source: Face it, self-driving cars still haven’t earned their stripes
Assessment: UnresolvedSaid
Conditional forecast
“Edge cases are everywhere; they are in driving, they are in medicine, they there will be a zillion of them humanoid robots, if and when we roll them out. Anyone who thinks any of this is going to be easy is fooling themselves.”
Our reading: Edge cases are everywhere, including in driving and medicine; there will be a zillion of them in humanoid robots if and when they are rolled out; and anyone who thinks any of this will be easy is fooling themselves.
No deadline stated Evidence as of
Only if: if and when humanoid robots are rolled out
Source: Face it, self-driving cars still haven’t earned their stripes
Assessment: UnresolvedSaid
Observation
“In fact, the truth is we don’t actually have any truly self-driving cars yet.”
Our reading: There are not yet any truly self-driving cars: every 'self-driving vehicle' on public roads has either a human safety driver on board or a remote human who can help it when issues arise.
No horizon Evidence as of
Limitation: The strongest contemporaneous evidence covers the two San Francisco operators.
Source: Face it, self-driving cars still haven’t earned their stripes
Assessment: UnresolvedSaid
Observation
“We have no such thing; building it would require a major reshaping of research directions, towards an AI based in facts and competent in reasoning, rather than just the powerful tools for text prediction that we have now.”
Our reading: We have no AI smart enough to know when it is being used to spew falsehood, and building it would require a major reshaping of research directions toward AI based in facts and competent in reasoning, rather than just the powerful text-prediction tools we have now.
No horizon Evidence as of
Limitation: The cited evaluations precede the publication-date snapshot and are context.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: UnresolvedSaid
Observation
“It’s a powerful reminder that our existing laws are not sufficient to protect us from the full scope of harms that generative AI is beginning to enable .”
Our reading: Existing laws are not sufficient to protect against the full scope of harms generative AI is beginning to enable.
No horizon Evidence as of
Limitation: The legal evidence primarily concerns federal election advertising.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: UnresolvedSaid
Forecast
“Automatic tools like these will surely accelerate the enshittification of the internet that I discussed yesterday.”
Our reading: Automatic misinformation generation tools will accelerate the degradation of the internet.
No deadline stated Evidence as of
Limitation: The assessment uses misinformation contamination as the ordinary contextual meaning of degradation.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: UnresolvedSaid
Observation
“And even worse, Amazon is starting to be a receptacle for inaccurate and exploitative quickie histories of recent events:”
Our reading: Amazon is starting to be a receptacle for inaccurate and exploitative quickie histories of recent events.
No horizon Evidence as of
Limitation: The retained dated evidence does not complete verification of specific material errors in the book.
Assessment: UnresolvedSaid
Forecast
“Coding is a solid use case that will continue to keep generative AI in the limelight for a long time. Programmers will never go back.”
Our reading: Coding will remain a solid use case keeping generative AI prominent for a long time, and programmers will not abandon it.
No deadline stated Evidence as of
Limitation: Survey findings describe respondents, not a census of programmers.
Assessment: UnresolvedSaid
Observation
“Driverless cars are powered by AI, but so far the money that has gone into them, on order of $100 billion dollars, vastly exceeds the actual revenue that has come out of them.”
Our reading: Investment in driverless cars, around $100 billion, vastly exceeds the revenue they have generated so far.
No horizon Evidence as of
Limitation: Corroborating the investment magnitude alone does not establish the complete claim.
Assessment: UnresolvedSaid
Observation
“Perhaps the only use case that really seemed compelling to either of us economically was search (e.g., using Bing powered by ChatGPT instead of Google Search) — but the technical problems there are immense; there is no reason to think that the hallucination problem will be solved soon.”
Our reading: There is no reason to think that the hallucination problem, part of the immense technical problems facing the search use case, will be solved soon.
No horizon Evidence as of
Limitation: The two studies do not comprehensively assess the leading approaches or their prospects for near-term deployment.
Assessment: UnresolvedSaid
Forecast
“I think coders will remain happy with generative AI assistance; its autocomplete nature is fabulous for their line of work, and they have the training to detect and fix the not-infrequent errors.”
Our reading: Marcus thinks coders will remain happy with generative AI assistance, because its autocomplete nature is fabulous for their work and they have the training to detect and fix its not-infrequent errors.
No deadline stated Evidence as of
Limitation: Survey respondents are not necessarily representative of all programmers.
Assessment: UnresolvedSaid
Observation
“And, reality check, we have no concrete reason, other than sheer technoptimism, for thinking that solutions to any of these problems is imminent.”
Our reading: There is no concrete reason, other than sheer techno-optimism, for thinking that solutions to any of generative AI's core problems (such as hallucination, unreliable tool use and month-to-month instability) are imminent.
No horizon Evidence as of
Limitation: This is an assessment of contemporary grounds for expectation, not a forecast scored against later events.
Assessment: UnresolvedSaid
Observation
“Second, there is speed; virtually everyone recognizes that large language models carry risks as well as opportunities, and that there is an urgent need to start to address these risks.”
Our reading: Virtually everyone recognizes that large language models carry risks as well as opportunities, and that there is an urgent need to start addressing these risks.
No horizon Evidence as of
Limitation: The governance context makes an informed-stakeholder reading plausible.
Source: Jumpstarting AI Governance
Assessment: UnresolvedSaid
Observation
“I don’t see any reason to think these plugins are in fact safe. (They are far less sandboxed and less rigorously controlled than Apple app store apps.)”
Our reading: Marcus says he does not see any reason to think ChatGPT plugins are in fact safe, noting that they are far less sandboxed and less rigorously controlled than Apple App Store apps.
No horizon Evidence as of
Limitation: The packet lacks a sufficiently complete account of OpenAI's review and enforcement during the target period.
Source: Two models of AI oversight - and how things could go deeply wrong
Assessment: UnresolvedSaid
Observation
“Neurosymbolic AI might offer a better way of resolving these issues, since it traffics in facts and explicit reasoning, but neurosymbolic AI is out of fashion, and neither well-developed nor strongly supported by the VC world.”
Our reading: Neurosymbolic AI is out of fashion, not well developed, and not strongly supported by venture capital.
No horizon Evidence as of
Limitation: The field boundaries and all three qualitative thresholds are unspecified.
Source: How China might crush the West in the race to AGI if we don’t up our regulatory game
Assessment: UnresolvedSaid
Observation
“At present, however, there is no legal requirement in the west that AI companies resolve the truthfulness or alignment issues.”
Our reading: There is currently no legal requirement in the West for AI companies to resolve truthfulness or alignment issues.
No horizon Evidence as of
Limitation: The schema has no source type for official regulatory announcements; official_statistics is used for the government documents.
Source: How China might crush the West in the race to AGI if we don’t up our regulatory game
Assessment: UnresolvedSaid
Causal claim
“When we do so, we amplify the hype around AI, and lead people into thinking that these machines are trustworthy oracles capable of manipulation or decision-making, which they are not.”
Our reading: When we treat AI models like people, we amplify the hype around AI and lead people into thinking these machines are trustworthy oracles capable of manipulation or decision-making, which they are not.
No horizon Evidence as of
Limitation: The reliability disclosure concerns GPT-4 at release and does not test effects of anthropomorphic treatment.
Assessment: UnresolvedSaid
Causal claim
“With technology more and more able to emulate human responses based on larger and larger samples of text (and “reinforcement learning” from humans who instruct the machines), the problem has grown even more pernicious.”
Our reading: Growing ability of AI to emulate human responses has made the overattribution (ELIZA effect) problem more pernicious.
No horizon Evidence as of
Limitation: The central comparative paper appeared after the post, although before the evaluation cutoff.
Assessment: UnresolvedSaid
Forecast
“Their forte has been and always will be making shit up; they can’t for the life of them (speaking metaphorically of course) check facts.”
Our reading: For GPT models on their own, making things up has been and always will be their forte, and they cannot check facts.
No deadline stated Evidence as of
Limitation: A literal zero-ability reading would yield a different assessment of the fact-checking component.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“It will be even better than GPT-4 at creating convincing sounding prose.”
Our reading: GPT-5 will be better than GPT-4 at creating convincing-sounding prose.
No deadline stated Evidence as of
Limitation: Writing preference varies with genre, prompting, and generation settings.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“GPT-5 is not going to be magic. It will not give China, the US, or anyone else interstellar starships; it will not give anyone an insuperable military advantage.”
Our reading: GPT-5 will not give China, the US, or anyone else interstellar starships, and it will not give anyone an insuperable military advantage.
No deadline stated Evidence as of
Limitation: Military capabilities may not be publicly observable.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“All told, I don’t doubt that GPT-5 will have some impact on defense and security.”
Our reading: GPT-5 will have some impact on defense and security.
No deadline stated Evidence as of
Limitation: A broader reading that includes subsequent GPT-5.x models would give the military deployment substantial supporting weight.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“(Not one of my predictions about GPT-4 proved to be incorrect; every flaw I predicted would persist persisted.)”
Our reading: Marcus says none of his predictions about GPT-4 proved incorrect, and every flaw he predicted would persist in GPT-4 did persist.
No horizon Evidence as of
Limitation: Confirming several central predictions cannot establish a universal track-record claim.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“GPT-4 can’t even play a good game of chess (it’s about as good as chess computers from 1978).”
Our reading: GPT-4 cannot play a good game of chess and is about as good as 1978 chess computers.
No horizon Evidence as of
Limitation: The admitted GPT-4 evidence consists of one recorded game and an account of two attempted games [e1, e2].
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“And very little of what GPT-4 does is fully replicable. Sometimes summarization (aka compression) works (super cool!) and sometimes it doesn’t, as many were quick to point out:”
Our reading: Very little of what GPT-4 does is fully replicable; its summarization sometimes works and sometimes fails.
No horizon Evidence as of
Limitation: The task population and meaning of full replication are unspecified.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Conditional forecast
“Some form of AI may eventually do everything people are imagining, revolutionizing science and technology and so on, but LLMs will be at most only a tiny part of whatever as-yet-uninvented technology does that.”
Our reading: If some as-yet-uninvented form of AI eventually does everything people are imagining, such as revolutionizing science and technology, LLMs will be at most only a tiny part of it.
No deadline stated Evidence as of
Only if: if some form of AI eventually does everything people are imagining
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“It might be on the nice-to-have list; it will not mark the mythical singularity.”
Our reading: GPT-5 will not mark the singularity.
No deadline stated Evidence as of
Limitation: The cited failure rate concerns one task, not a measurement of sustained, self-amplifying intelligence improvement [e1].
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“Vulnerable patients shouldn’t be talking to chatbots that aren’t competent for this situation, and too much of the discussion in the media have made chatbots sound more intelligent and even more friendly than they are.”
Our reading: Too much media discussion has made chatbots sound more intelligent and friendly than they are.
No horizon Evidence as of
Limitation: The phrase “too much” remains partly evaluative.
Assessment: UnresolvedSaid
Observation
“Virtually everyone, even at OpenAI, has acknowledged that there are serious risks, but thus far few tangible steps have been taken to mitigate them—either by government or industry.”
Our reading: Although virtually everyone, including people at OpenAI, acknowledges serious AI risks, government and industry have so far taken few tangible steps to mitigate them.
No horizon Evidence as of
Limitation: Government guidance, legal requirements and technical safeguards differ substantially in reach and effectiveness.
Source: The Open Letter Controversy
Assessment: UnresolvedSaid
Causal claim
“The paper’s core claim— “GPT-4 attains a form of general intelligence [as] demonstrated by its core mental capabilities (such as reasoning, creativity, and deduction)”—literally cannot be tested with serious scrutiny, because the scientific community has no access to the training data.”
Our reading: The Sparks of AGI paper's claim that GPT-4 attains a form of general intelligence cannot be seriously tested because scientists have no access to its training data.
No horizon Evidence as of
Limitation: This is not support for the claim that all serious testing requires training-data access.
Assessment: UnresolvedSaid
Observation
“if the definition of reasoning is to obtain valid conclusions from known facts, GPT-4 frequently falls short there, too.”
Our reading: GPT-4 frequently fails to reason, if reasoning means reaching valid conclusions from known facts.
No horizon Evidence as of
Limitation: Later evaluations were not rejected merely because they were published after the post; the problem is their uncertain connection to the target model period.
Source: This Week in AI Doublespeak
Assessment: UnresolvedSaid
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
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
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: UnresolvedSaid
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
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
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: UnresolvedSaid
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
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
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: UnresolvedSaid
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: UnresolvedSaid
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
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: UnresolvedSaid
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: UnresolvedSaid
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
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: UnresolvedSaid
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: UnresolvedSaid
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
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: UnresolvedSaid
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
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
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: UnresolvedSaid
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
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
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: UnresolvedSaid
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: UnresolvedSaid
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
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: UnresolvedSaid
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
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: UnresolvedSaid
Observation
“And, bonus, there is little if any government regulation in place to do much about this.”
Our reading: There is little if any government regulation in place to address ChatGPT's risks.
No horizon Evidence as of
Limitation: This is a targeted review, not a complete inventory of national and subnational laws.
Assessment: UnresolvedSaid
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
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
Observation
“Deep learning has undeniably made amazing progress, on other dimensions, but the fact is it hasn’t solved the core problems that many of us thought were essential 30 years ago.”
Our reading: Deep learning has made progress on other dimensions but has not solved the core problems thought essential 30 years ago, so in that sense it has hit a wall.
No horizon Evidence as of
Limitation: The packet lacks direct evaluations establishing the separate abstraction and individual-versus-kind components.
Assessment: UnresolvedSaid
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
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
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
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: UnresolvedSaid
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
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
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
Forecast
“Anyone who thinks about how AI is used should know about it—especially as we are about to head into an era of ubiquitous yet fallible AI.”
Our reading: We are about to enter an era in which AI is ubiquitous but still fallible.
No deadline stated Evidence as of
Limitation: Organizational adoption, consumer chatbot use, and embedded AI exposure measure different things.
Source: The CNET Fake News Fiasco, Autopilot, and the Uncanny Cognitive Valley
Assessment: UnresolvedSaid
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
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
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: UnresolvedSaid
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
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: UnresolvedSaid
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: UnresolvedSaid
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
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: UnresolvedSaid
Forecast
“We are going to see a lot of this – misuses of ChatGPT in which people trust it, and even trumpet it—when it’s flat-out wrong.”
Our reading: There will be many cases of people trusting and promoting ChatGPT outputs that are flat-out wrong.
No deadline stated Evidence as of
Limitation: This is a finding about the evidence verified here, not a finding that numerous qualifying incidents did not occur.
Source: Scientists, please don’t let your chatbots grow up to be co-authors
Assessment: UnresolvedSaid
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: UnresolvedSaid
Observation
“For another, although OpenAI may be tracking errors like these for internal purposes, the scientific community has no access to whatever they have collected, which leaves it in the lurch.”
Our reading: The scientific community has no access to whatever error data OpenAI collects on ChatGPT.
No horizon Evidence as of
Limitation: Earlier OpenAI feedback datasets do not answer this ChatGPT-specific claim.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
Observation
“For one thing, although many people seem to have heard of a few of these errors (e.g. from reports in the media), few realize how pervasive or broad in scope they are; indeed a recent Twitter thread that claimed falsely that these problems had been resolved got a surprising amount of traction.”
Our reading: Although many people seem to have heard of a few of ChatGPT's errors, few realize how pervasive or broad in scope they are, and a recent Twitter thread falsely claiming these problems had been resolved got a surprising amount of traction.
No horizon Evidence as of
Limitation: The packet contains no matching contemporaneous awareness research for a defined audience.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
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: UnresolvedSaid
Causal claim
“It is therefore impossible for external scientists to determine to what extent any specific correct answer merely reflects some very similar examples in the training data.”
Our reading: Because ChatGPT's training corpus has not been made available to the public, it is impossible for external scientists to determine to what extent any specific correct answer merely reflects some very similar examples in the training data.
No horizon Evidence as of
Limitation: A timeless reading would give the later extraction evidence greater weight.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
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
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
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: UnresolvedSaid
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: UnresolvedSaid
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
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
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.