Claims checked against evidence, page 11 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 11 of 15: claims 1001 to 1100 of 1432, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Gary Marcus Observation
“Any fool can simply eyeball these data and see that in virtually every condition, people who have access both to the internet and LLMs (darker bars) are more accurate than people without (lighter bars).”
Our reading: In OpenAI's study, participants with both internet and LLM access were more accurate than those without in virtually every condition.
No horizon Evidence as of
Limitation: Higher observed means do not establish significance or practical danger.
Source: Diving deep into OpenAI’s new study on LLM’s and bioweapons
Assessment: UnresolvedSaid
Gary Marcus Observation
“At present, we do not know.”
Our reading: It is currently unknown whether open-source models without guardrails are more useful to bad actors than commercial models with guardrails.
No horizon Evidence as of
Limitation: Earlier research is context because the claim concerns the knowledge state on February 4.
Source: Diving deep into OpenAI’s new study on LLM’s and bioweapons
Assessment: UnresolvedSaid
Gary Marcus Observation
“It is shocking that neither OpenAI nor the media noted this (they also did not discuss in any detail one other paper that reached a somewhat similar conclusion, by Kevin Esvelt’s lab).”
Our reading: Neither OpenAI nor the media noted the fourfold difference in expert success on pathogen formulation, nor discussed in detail a similar paper from Kevin Esvelt's lab.
No horizon Evidence as of
Limitation: The supplied evidence items omit OpenAI's publication and do not provide a sufficiently comprehensive contemporary media record.
Source: Diving deep into OpenAI’s new study on LLM’s and bioweapons
Assessment: ContradictedSaid
Gary Marcus Observation
“As a journal reviewer, I would also note that they reported neither the p value they obtained, nor the test of statistical significance they applied; in a competent peer-reviewed journal, that’s a no-no.”
Our reading: OpenAI's paper reported neither the p value obtained nor the significance test applied, which would be unacceptable in a competent peer-reviewed journal.
No horizon Evidence as of
Limitation: The p-values are in a linked CSV rather than stated directly in footnote C.
Source: Diving deep into OpenAI’s new study on LLM’s and bioweapons
Assessment: UnresolvedSaid
Gary Marcus Observation
“Not every image looks like this, but it’s pretty clear after a few minutes experimentation from a friend that (a) Bard makes fantastic images but that (b) it is drawing from copyrighted sources and (c) as capable as the other systems Reid Southen and I looked at in returning derivative artwork that appears to infringe without attribution.”
Our reading: Based on a friend's few minutes of experimentation, it is pretty clear that Bard makes fantastic images, draws from copyrighted sources, and is as capable as the other systems Marcus and Reid Southen looked at in returning derivative artwork that appears to infringe without attribution, though not every image looks like this.
No horizon Evidence as of
Limitation: Plain-text sources do not provide a complete authenticated visual record.
Assessment: SupportedSaid
Gary Marcus Forecast
“Gary Marcus is standing firmly by his prediction that 2024 will be the year of Generative AI litigation.”
Our reading: 2024 will be the year of generative AI litigation.
Deadline passed Evidence as of
Limitation: “The year of” supplies no numerical threshold; support follows the contract's ordinary reading of substantial, sustained litigation.
Assessment: SupportedSaid
Gary Marcus Observation
“What unifies all of the above is that current systems are good at local coherence, between words, and between pixels, but not at lining up their outputs with a global comprehension of the world.”
Our reading: Current systems are good at local coherence, both between words and between pixels, but are not good at lining up their outputs with a global comprehension of the world.
No horizon Evidence as of
Limitation: Support is qualitative and depends on the stated behavioral interpretation.
Source: One GenAI image; Ten Errors
Assessment: UnresolvedSaid
Gary Marcus Observation
“Third, at least so far OpenAI lacks both profits and a moat.”
Our reading: As of January 2024, OpenAI has neither profits nor a competitive moat.
No horizon Evidence as of
Limitation: Earlier losses cannot be carried forward as proof of current losses.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Such errors clearly aren’t disappearing any time soon.”
Our reading: Errors of discomprehension in neural-network-based systems will not disappear any time soon.
No deadline stated Evidence as of
Limitation: Unresolved does not mean the later evidence runs against the forecast.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Examples like these, in which ChatGPT confidently generates an image that does not actually meet requirements, are nicely complement the visual hallucinations that Ernest Davis and I reported here in October, in which visual ChatGPT misinterpreted various images:”
Our reading: In examples like those shown, ChatGPT confidently generated images that did not actually meet requirements, and in examples reported in October, visual ChatGPT misinterpreted various images.
No horizon Evidence as of
Limitation: This is an access and verification gap, not evidence that Marcus's descriptions were false.
Assessment: SupportedSaid
Gary Marcus Forecast
“We can of course expect related problems of discomprehension in efforts to use these systems to address copyright issues, as I wrote earlier this week.”
Our reading: Efforts to use generative AI systems to address copyright issues will run into problems of discomprehension.
Settled before its deadline Evidence as of
Limitation: The decisive study is a preprint.
Assessment: UnresolvedSaid
Gary Marcus Observation
“It might be trivial to do it for the most popular characters, presented in the most canonical ways, but getting it right for all copyright materials is an entirely different matter.”
Our reading: Building a classifier that detects copyrighted material might be trivial for the most popular characters presented in the most canonical ways, but getting it right for all copyrighted materials is an entirely different matter.
No horizon Evidence as of
Limitation: The packet contains no qualifying evaluation of the January 2024 comparison.
Source: No, multimodal ChatGPT is not going to “trivially” solve Generative AI's copyright problems
Assessment: UnresolvedSaid
Gary Marcus Observation
“Reid spent about 45 minutes looking into this earlier today (why not more? see below) with images generated by Midjourney, and it quickly became apparent that using ChatGPT as intern to trace down sources is, well, pretty intermittent.”
Our reading: In a 45-minute test, multimodal ChatGPT was unreliable at identifying the copyrighted sources of Midjourney-generated images.
No horizon Evidence as of
Limitation: The packet has no independent record of the images Southen tested or of ChatGPT's answers to them.
Source: No, multimodal ChatGPT is not going to “trivially” solve Generative AI's copyright problems
Assessment: SupportedSaid
Gary Marcus Forecast
“More than that, GenAI developers won’t get off so easy, and lawyers looking at copyright infringement will still have plenty of work to do.”
Our reading: GenAI developers will not easily escape copyright problems, and copyright infringement lawyers will have plenty of work.
Settled before its deadline Evidence as of
Limitation: The assessment treats substantial contested proceedings as the ordinary meaning of plenty of legal work.
Source: No, multimodal ChatGPT is not going to “trivially” solve Generative AI's copyright problems
Assessment: UnresolvedSaid
Gary Marcus Observation
“Notice, too, that Katie asked Bing “are they original?”, and Bing, untruthfully, but with a cozy emoticon, answered in the affirmative.”
Our reading: Bing falsely said its apparently infringing images were original.
No horizon Evidence as of
Limitation: The packet has no independent record of Bing's reply or of the generated images.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: UnresolvedSaid
Gary Marcus Observation
“Fundamentally, virtually any guardrail has to thread a needle between the Scylla of being too restrictive and Charybdis of being too permissive. None thus far have done this effectively.”
Our reading: No AI guardrail so far has effectively balanced being neither too restrictive nor too permissive.
No horizon Evidence as of
Limitation: The cited evaluations cover selected models, prompts, and attacks.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Any one example can be filtered out, but there are too many examples, and current systems are too erratic to reliably get things right.”
Our reading: Because there are too many examples and current systems are too erratic, guardrails cannot reliably get things right even though any single example can be filtered.
No horizon Evidence as of
Limitation: The controlled defense study is contextual because its experiments predate the target period.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: UnresolvedSaid
Gary Marcus Observation
“Guardrail builders generally wind up playing whack-a-mole.”
Our reading: Guardrail builders generally end up patching individual failures one by one instead of solving the problem in general.
No horizon Evidence as of
Limitation: Public summaries provide less detail than internal remediation records.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: UnresolvedSaid
Gary Marcus Observation
“Other prompts still elicited apparent copyright infringements, where new filters did not suffice.”
Our reading: Even after the new filters, some prompts to Bing's DALL-E 3 still produced apparent copyright infringements.
No horizon Evidence as of
Limitation: The independent demonstration lacks authenticated generation timestamps and filter-version information.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Since that would violate the Berne Convention and infuriate every “content provider” (from individual artists and writers to major studios and publishers) ever, we hope and expect that this exemption will not be granted.”
Our reading: The UK will not grant OpenAI the exemption from copyright responsibility it requested.
No deadline stated Evidence as of
Limitation: The exemption Marcus described is not precisely identified.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: SupportedSaid
Gary Marcus Observation
“The opposite problem happens too; guardrails can wind up too permissive. This is still happening over a year into the chatbot revolution.”
Our reading: Guardrails sometimes end up too permissive, and this is still happening over a year into the chatbot revolution.
No horizon Evidence as of
Limitation: Support rests on a primary researcher summary rather than fully reported contemporary replication results.
Source: DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight
Assessment: UnresolvedSaid
Gary Marcus Observation
“It’s likewise a flaw that that current AI cannot learn without a tendency to inadvertently regurgitate big bits of text and images that look uncannily like their sources, but not a logical necessity.”
Our reading: Current AI cannot learn without a tendency to inadvertently regurgitate big bits of text and images that look uncannily like their sources, which is a flaw but not a logical necessity.
No horizon Evidence as of
Limitation: The extraction studies do not establish an unavoidable tendency across contemporary text and image generators [e1, e2].
Assessment: UnresolvedSaid
Gary Marcus Forecast
“LLMs may eventually be replaced by better technologies, but for now, they aren’t going anywhere, even if some of the commercial purveyors go out of business.”
Our reading: LLMs will not disappear for now even if some commercial providers go out of business, though they may eventually be replaced by better technologies.
No deadline stated Evidence as of
Limitation: Unresolved does not mean the observed trend contradicts the forecast.
Assessment: UnresolvedSaid
Gary Marcus Observation
“OpenAI’s lobbying campaign, simply put, is based on a false dichotomy (give everything to us free or we will die)—and also a threat: either we get to use all the existing IP we want for free, or you won’t get to generative AI anymore.”
Our reading: OpenAI's copyright lobbying campaign rests on a false dichotomy and an implied threat that generative AI will go away without free use of IP.
No horizon Evidence as of
Limitation: This is an evidence-access limitation, not a finding that Marcus’s characterization is accurate.
Assessment: SupportedSaid
Gary Marcus Observation
“To begin with, the threat is empty: open source LLMs are already out in the wild, and a large number of people know how to build them.”
Our reading: OpenAI's threat that generative AI would disappear is empty because open source LLMs already exist and many people can build them.
No horizon Evidence as of
Limitation: The result uses the ordinary public-model reading of open source.
Assessment: UnresolvedSaid
Gary Marcus Observation
“And worse, infringement can happen even the user isn’t looking to infringe and doesn’t mention any character or film by name:”
Our reading: DALL-E can produce infringing images even when the user does not intend to infringe or name any character or film.
No horizon Evidence as of
Limitation: Reading infringement merely as recognizable copyrighted imagery would weaken the claim's stated legal commitment.
Source: Things are about to get a lot worse for Generative AI
Assessment: SupportedSaid
Gary Marcus Observation
“Generative AI systems like DALL-E and ChatGPT have been trained on copyrighted materials;”
Our reading: Generative AI systems such as DALL-E and ChatGPT were trained on copyrighted materials.
No horizon Evidence as of
Limitation: The records do not provide a complete inventory of training works or establish which material entered every model version.
Source: Things are about to get a lot worse for Generative AI
Assessment: UnresolvedSaid
Gary Marcus Observation
“OpenAI’s image software (which we accessed through Bing) is perfectly capable of verbatim and near-verbatim repetition of sources as well.”
Our reading: OpenAI's image generator can reproduce source images verbatim or near-verbatim.
No horizon Evidence as of
Limitation: A broader reading that counts recognizable character designs as near-verbatim sources would require different criteria.
Source: Things are about to get a lot worse for Generative AI
Assessment: UnresolvedSaid
Gary Marcus Observation
“The output was not marked as being directly premised on copyrighted work”
Our reading: Midjourney's output was not labelled as being directly based on copyrighted work.
No horizon Evidence as of
Limitation: Absence of labelling is hard to document from secondary sources.
Source: An artist fights back, and Midjourney has embarrassed themselves
Assessment: SupportedSaid
Gary Marcus Observation
“It’s also hardly the first time AI is helped with mathematics.”
Our reading: FunSearch is not the first time AI has helped with mathematics.
No horizon Evidence as of
Limitation: This does not adjudicate the narrower claim that FunSearch was a first for LLM-based mathematical discovery.
Source: Sorry, but FunSearch probably isn’t a milestone in scientific discovery
Assessment: SupportedSaid
Gary Marcus Observation
“There is no evidence that the problem was heretofore “unsolvable””
Our reading: There is no evidence that the problem FunSearch addressed was previously unsolvable.
No horizon Evidence as of
Limitation: Support is limited to the ordinary distinction between impossibility and an open problem.
Source: Sorry, but FunSearch probably isn’t a milestone in scientific discovery
Assessment: SupportedSaid
Gary Marcus Observation
“Human mathematicians had to write problem-specific code for each separate mathematical problem.”
Our reading: FunSearch required human mathematicians to write problem-specific code for each separate problem.
No horizon Evidence as of
Limitation: The outcome depends on interpreting mathematicians as human task designers and separate problems as different task families.
Source: Sorry, but FunSearch probably isn’t a milestone in scientific discovery
Assessment: SupportedSaid
Gary Marcus Observation
“The whole project was quietly dropped a year or two later.”
Our reading: OpenAI quietly dropped its Rubik's cube robotics project a year or two after the demo.
No horizon Evidence as of
Limitation: The decisive company statement is reproduced in reputable news reporting; a standalone primary shutdown announcement was not retrieved.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“What we are going to see, in the fullness of time, I promise you, is that Gemini is more or less in the same ball park as GPT-4, handy for a bunch of things, but untethered in reality, still with dicey, unpredictable reasoning, and a very limited understanding of the world.”
Our reading: Gemini will be shown to still have dicey, unpredictable reasoning and a very limited understanding of the world.
No deadline stated Evidence as of
Limitation: Competitive benchmark performance does not prove broad world understanding.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Regular readers will recall how that turned out: the Rubik’s cube was instrumented with sensors, and not straight off the shelf, and what worked for the Rubik’s cube in the demo video didn’t turn out have a whole lot of application in the real world.”
Our reading: OpenAI's Rubik's cube robot demo relied on a sensor-instrumented cube and had little real-world application.
No horizon Evidence as of
Limitation: The evidence does not identify which demonstration video Marcus referenced.
Assessment: SupportedSaid
Gary Marcus Observation
“Note that Gates and Altman have both been dropping hints, and GPT-5 isn’t here after a year despite immense commercial desire.”
Our reading: Gates and Altman have both been dropping hints, and GPT-5 has not arrived after a year despite immense commercial desire.
No horizon Evidence as of
Limitation: Support depends on treating the year as an approximate ChatGPT-era anniversary.
Assessment: UnresolvedSaid
Gary Marcus Observation
“From a commercial standpoint GPT-4 is no longer unique.”
Our reading: Commercially, GPT-4 is no longer a unique offering.
No horizon Evidence as of
Limitation: The disclosures provide no matched evaluation establishing broad commercial comparability with GPT-4 on December 6 [e1, e2].
Assessment: SupportedSaid
Gary Marcus Observation
“But that’s exactly what’s happening: Messrs. LeCun and Zuckerberg seem to have talked it out, and decided what’s best (in their not-so-humble opinion) for all of us.”
Our reading: Marcus says it is exactly what's happening that a couple of tech lords are deciding for us: LeCun and Zuckerberg seem to have talked out whether to open-source Meta's AI and decided what's best, in their opinion, for all of us.
No horizon Evidence as of
Limitation: Decision authority partly rests on retrospective reporting.
Source: Critical national security questions should not be decided unilaterally by unelected tech leaders
Assessment: UnresolvedSaid
Gary Marcus Observation
“So far, Big Tech rather than the US government is calling the shots.”
Our reading: On sharing AI technology with other nations (e.g., OpenAI's G42 partnership), Big Tech rather than the US government is currently making the decisions.
No horizon Evidence as of
Limitation: Private diplomacy and agreement terms are incompletely public.
Source: Critical national security questions should not be decided unilaterally by unelected tech leaders
Assessment: UnresolvedSaid
Gary Marcus Observation
“But virtually all existing laws were written before AI was really on the scene, and so very few really envision how AI changes things.”
Our reading: Virtually all existing laws were written before AI was really on the scene, and so very few of them really envision how AI changes things.
No horizon Evidence as of
Limitation: The cited evidence describes earlier enactments and is context for the publication-date legal stock.
Assessment: SupportedSaid
Gary Marcus Observation
“Its pioneering, world-leading AI Act is on the ropes.”
Our reading: As of November 2023, the EU AI Act is in danger of failing.
No horizon Evidence as of
Limitation: The decisive source is reporting of participant interviews, not an official transcript or complete negotiating record.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Big tech wants the world to revolve around its new plaything, Generative AI (combined total revenue so far a few billion dollars, just a tiny tiny fraction of the economy), and to exempt it from any regulation.”
Our reading: Generative AI's combined total revenue so far is only a few billion dollars, a tiny fraction of the economy.
No horizon Evidence as of
Limitation: Enterprise estimates omit or differently classify consumer products and infrastructure.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Actually, there was one clear overall winner: Tegmark. As much as Ernie Davis and I utterly disagreed with Tegmark’s more technical map of the world/LLM world models paper a few weeks ago, pretty much every word he said this week about regulation was true.”
Our reading: Nearly everything Tegmark said that week about AI regulation was true.
No horizon Evidence as of
Limitation: The evaluable reading concerns factual substance; the broader endorsement also contains opinion.
Assessment: SupportedSaid
Gary Marcus Forecast
“And I do mean “we”; what the EU decides will affect all of us.”
Our reading: The EU's decision on regulating generative AI will affect people everywhere, not just in the EU.
Settled before its deadline Evidence as of
Limitation: Support depends on reading all of us as worldwide relevance rather than a literal claim about every person.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Bing,powered by Dall-E 3, was an even bigger loser, drawing “Hinton vs LeCun vs Ng vs Tegmark, boxing” with four boxers not boxing, not one of them looking remotely like any of our heroes:”
Our reading: Bing powered by DALL-E 3, prompted to draw Hinton, LeCun, Ng and Tegmark boxing, produced four boxers not boxing and resembling none of the people named.
No horizon Evidence as of
Limitation: No visual inspection was possible.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Eventually, we will be able to build machines that help us reason critically, overcome human bias, and objectively, the way that we have calculators that spit out just the facts, without confabulation.”
Our reading: Eventually, we will be able to build machines that help us reason critically, overcome human bias, and reason objectively, giving just the facts without confabulation, the way calculators do.
No deadline stated Evidence as of
Limitation: Small, task-specific studies cannot establish broad capability.
Source: AI versus The Clumsy Human Mind
Assessment: UnresolvedSaid
Gary Marcus Observation
“But “breakthroughs” rarely turn to be general to live up to initial rosy expectations. Often advances work in some contexts, not otherwise.”
Our reading: Claimed AI breakthroughs rarely prove general enough to live up to initial expectations, often working only in some contexts.
No horizon Evidence as of
Limitation: The result depends on which advances and which expectations are counted.
Source: About that OpenAI “breakthrough”
Assessment: SupportedSaid
Gary Marcus Observation
“getting their algorithm to work for one object (which, cheesily, turned out to be a special Cube instrumented with sensors and LEDS, unlike a Rubik’s cube you’d buy in a store) in carefully controlled lab circumstances hardly guaranteed that the solution would work more broadly in the complex open-ended real world.”
Our reading: OpenAI's 2019 Rubik's cube robot result, achieved on a sensor-instrumented cube in controlled lab conditions, did not guarantee generalization to the open-ended real world.
No horizon Evidence as of
Limitation: The selected documentation does not establish the precise claim that one demonstrated cube contained both sensors and LEDs.
Source: About that OpenAI “breakthrough”
Assessment: SupportedSaid
Gary Marcus Observation
“AV’s still aren’t general enough that you can just plop down a car that was tuned in pilot studies in Menlo Park, SF and Arizona and expect it to drive gracefully and safely in Sicily or Mumbai.”
Our reading: Autonomous vehicles still are not general enough that you can just plop down a car tuned in pilot studies in Menlo Park, SF and Arizona and expect it to drive gracefully and safely in Sicily or Mumbai.
No horizon Evidence as of
Limitation: The evidence documents Waymo's own testing process; it does not establish every developer's capabilities [e1].
Source: About that OpenAI “breakthrough”
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“It autocompletes sentences, some of those completions are true given that the training data is so vast, and some aren’t, given that the training data aren’t infinite.”
Our reading: GPT autocompletes sentences; some outputs are true because training data is vast and some are false because training data is not infinite.
No horizon Evidence as of
Limitation: This assessment does not interpret finite data as the sole asserted cause.
Source: A Tale of Two Liars
Assessment: SupportedSaid
Gary Marcus Observation
“It’s fun to compare the two—they have so many differences—even though neither can be trusted.”
Our reading: GPT cannot be trusted.
No horizon Evidence as of
Limitation: The conclusion concerns unchecked factual reliance; the summarization results do not establish that every GPT answer is false or that GPT has no useful applications [e6].
Source: A Tale of Two Liars
Assessment: UnresolvedSaid
Gary Marcus Observation
“The real value was always the people, not the IP, not the data, not the customer list, not the infrastructure.”
Our reading: OpenAI's real value was always its people, not its IP, data, customer list or infrastructure.
No horizon Evidence as of
Limitation: Workforce and technology value may depend on each other.
Source: Top 5 reasons why OpenAI was probably never really worth $86 billion
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“The whole nonprofit-hybrid model, introduced in 2019 (the company was originally pure non-profit) was always a ticking time bomb, because the people trying to make money were never going to have a perfect relationship with those in the nonprofit that were charged with keeping the world safe.”
Our reading: OpenAI's nonprofit-hybrid structure was a ticking time bomb because profit-seekers and safety-focused nonprofit stewards would inevitably conflict.
No horizon Evidence as of
Limitation: Both principal causal accounts come from parties involved in the dispute.
Source: Top 5 reasons why OpenAI was probably never really worth $86 billion
Assessment: SupportedSaid
Gary Marcus Observation
“OpenAI’s strength has been in scaling up an existing paradigm (Transformers) that was developed elsewhere (Google), not developing new paradigms.”
Our reading: OpenAI's strength has been scaling the Transformer paradigm developed at Google, not developing new paradigms.
No horizon Evidence as of
Limitation: This is a qualitative assessment of the principal GPT advances, not a ranking of every OpenAI research contribution.
Source: Top 5 reasons why OpenAI was probably never really worth $86 billion
Assessment: SupportedSaid
Gary Marcus Observation
“And the overriding nonprofit mission was always going to be at odds with a pure for profit play.”
Our reading: OpenAI's nonprofit mission was inherently at odds with a pure for-profit business.
No horizon Evidence as of
Limitation: The outcome depends on reading at odds as a structural constraint, not perpetual operational conflict.
Source: Top 5 reasons why OpenAI was probably never really worth $86 billion
Assessment: SupportedSaid
Gary Marcus Observation
“There has never been a solution to the hallucination problem, and it’s not clear that there will be until we find a new paradigm, as Altman himself partly acknowledged last week, when he did his best Gary Marcus impression.”
Our reading: The hallucination problem has never been solved and may not be until a new paradigm is found.
No horizon Evidence as of
Limitation: Support concerns a general solution, not whether particular applications could achieve useful reliability.
Source: Top 5 reasons why OpenAI was probably never really worth $86 billion
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Large language models alone were never going to solve the alignment problem.”
Our reading: Large language models alone were never going to solve the alignment problem.
No deadline stated Evidence as of
Limitation: The experiments do not establish reliable alignment across all relevant deployment settings.
Source: Top 5 reasons why OpenAI was probably never really worth $86 billion
Assessment: SupportedSaid
Gary Marcus Observation
“and then, perhaps in keeping with my surmise that Sam’s bluff was called, negotiations broke down altogether.”
Our reading: Negotiations (over Sam Altman's return to OpenAI) broke down altogether, which Marcus says is perhaps in keeping with his surmise that Altman's bluff was called.
No horizon Evidence as of
Limitation: The assessment depends on reading “altogether” within the post's immediate time frame.
Source: Everybody’s out
Assessment: SupportedSaid
Gary Marcus Observation
“We pretty obviously don’t have a say.”
Our reading: The public has no say in how OpenAI's governance crisis is resolved, including its implications for safety and humanity.
No horizon Evidence as of
Limitation: The result depends on interpreting “a say” as formal participation.
Assessment: MixedSaid
Gary Marcus Observation
“The spirit of the original arrangement was that everything that the for-profit did was supposed to be in the service of the non-profit. A board with no financial interest was supposed to look out for humanity.”
Our reading: OpenAI's original structure was meant to make the for-profit serve the non-profit, with a financially disinterested board looking out for humanity.
No horizon Evidence as of
Limitation: This assesses the intended structure, not its effectiveness.
Assessment: SupportedSaid
Gary Marcus Forecast
“All signs are that those financially-interested stakeholders will quickly emerge victorious. (Arguably, they already have).”
Our reading: All signs are that OpenAI's financially interested stakeholders (employees and investors) will quickly win out over the nonprofit board, and arguably they already have.
Settled before its deadline Evidence as of
Limitation: Marcus supplied no exact deadline for 'quickly'.
Assessment: SupportedSaid
Gary Marcus Observation
“In reality, things have gone quite differently. When the nonprofit Board felt it was prudent (for undisclosed reasons) to remove Altman, per the goals of the nonprofit that they were trying to represent, at the risk of potentially devastating commercial potential, those with a stake in the nominally subordinate for-profit (both employees and investors) quickly set to work to push out the board and to undo its decisions.”
Our reading: After OpenAI's nonprofit board removed Altman, employees and investors in the for-profit quickly worked to push out the board and undo its decision, contrary to the intended structure.
No horizon Evidence as of
Limitation: News reporting is used because the private negotiations lack accessible public primary records.
Assessment: SupportedSaid
Gary Marcus Observation
“Breaking news: the draft EU AI Act—the closest thing we have to AI regulation with real teeth—is in serious jeopardy, potentially a direct victim of regulatory capture, governments doing the bidding of those they are supposed govern.”
Our reading: The draft EU AI Act is in serious jeopardy as of November 2023.
No horizon Evidence as of
Limitation: No official minutes of the 10 November meeting were obtained; the assessment relies on reporting and retrospective legal analysis.
Source: Crisis point in AI?
Assessment: SupportedSaid
Gary Marcus Observation
“Update: A few hours after I posted this Cruise CEO Kyle Vogt essentially confirmed that their “driverless” cars need very regular human intervention:”
Our reading: Cruise's CEO essentially confirmed that Cruise's driverless cars require very regular human intervention.
No horizon Evidence as of
Limitation: Supported for recurring operational assistance; strict necessity for safety is not established.
Assessment: SupportedSaid
Gary Marcus Forecast
“Shareholders will certainly sue, and if it’s bad as it looks, I doubt that GM will continue the project, which was recently suspended.”
Our reading: Shareholders will sue over Cruise.
Settled before its deadline Evidence as of
Limitation: This assessment concerns the lawsuit prediction only.
Assessment: ContradictedSaid
Gary Marcus Observation
“I realize there’s been almost zero public disclosure of how any of the driverless car companies are using remote operators. It’s not just Cruise.”
Our reading: Driverless car companies in general have disclosed almost nothing publicly about their use of remote operators.
No horizon Evidence as of
Limitation: The result depends on reading the claim as about disclosure of operators’ roles, rather than complete quantitative transparency.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Certainly no commercial product could ever work at a profit if you needed remote operators anything like that often.”
Our reading: Certainly no commercial product could ever work at a profit if it needed remote operators anything like as often as Cruise reportedly does.
No horizon Evidence as of
Only if: if remote operators were needed anything like as often as reported
Assessment: SupportedSaid
Gary Marcus Observation
“Nobody knows for SURE that there are no serious possible consequences of open source AI, e.g in terms of bioweapons, misinformation generation etc, as Séb Krier at DeepMind nicely highlights above.”
Our reading: No one knows for certain that open source AI has no serious possible consequences, such as for bioweapons or misinformation generation.
No horizon Evidence as of
Limitation: The experiments concern public Llama 2-Chat weights and do not establish equivalent results for every open model or for access through a controlled service [e1].
Source: Where we are right now on open source and potential AI risk.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“How effective this actually becomes depends a lot on the exact wording, and how things are enforced, and how much of it is binding as opposed to merely voluntary.”
Our reading: The Executive Order's effectiveness depends heavily on exact wording, enforcement, and how much is binding rather than voluntary.
No horizon Evidence as of
Limitation: The available evaluations cover particular activities rather than the order's overall effects.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Companies will poke for loopholes and surely find them.”
Our reading: AI companies will look for and find loopholes in the Executive Order.
No deadline stated Evidence as of
Limitation: Company compliance deliberations may not be public.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“To get there, though, what we need is a much smarter AI, a kind that can reason, and not hallucinate, grounded in reality and not just corpus statistics— which require fundamental research of a sort that is currently getting starved out in the LLM euphoria.”
Our reading: Getting to reliable self-driving cars that are safer than humans requires a much smarter AI that can reason, does not hallucinate, and is grounded in reality rather than just corpus statistics, and building that AI requires fundamental research.
No horizon Evidence as of
Limitation: The key capabilities and required research advances lack precise definitions.
Source: Why driverless cars might—or might not—be at the end of the road
Assessment: SupportedSaid
Gary Marcus Forecast
“I—truly a techno-optimist, popular perceptions to the contrary—do think driverless will come, eventually.”
Our reading: Driverless cars will eventually arrive.
Settled before its deadline Evidence as of
Limitation: Marcus supplied no explicit adoption threshold, so interpretation materially affects the result.
Source: Why driverless cars might—or might not—be at the end of the road
Assessment: SupportedSaid
Gary Marcus Causal claim
“you don’t have to be superintelligent to create serious problems.The real issue is control.”
Our reading: AI need not be superintelligent to create serious problems; the real issue is control.
No horizon Evidence as of
Limitation: The example concerns automated driving, not an LLM.
Assessment: SupportedSaid
Gary Marcus Observation
“We have no way of guaranteeing that large language models will reliably stick to facts, a point so thoroughly established by now I don’t think it needs a reference.”
Our reading: There is no way to guarantee that large language models reliably stick to facts.
No horizon Evidence as of
Limitation: Support rests on a contemporary research synthesis, not an exhaustive proof of nonexistence.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Current AI has no way to reliably anticipate consequences of actions that it might recommend, Asimov’s First Law of Robotics called for robots to do no harm, but current AI has no reliable way to anticipate harm.”
Our reading: Current AI cannot reliably anticipate the consequences or harms of actions it recommends.
No horizon Evidence as of
Limitation: The scope of current AI and the meaning of reliably are unspecified.
Assessment: SupportedSaid
Gary Marcus Observation
“We have no way of guaranteeing that large language models won’t be jailbroken into saying unethical things. To the contrary, a just-published technique shows that current LLMs can be jailbroken quickly, and automatically.”
Our reading: There is no way to guarantee LLMs won't be jailbroken, and current LLMs can be jailbroken quickly and automatically.
No horizon Evidence as of
Limitation: The guarantee clause depends on the stated general-immunity interpretation.
Assessment: SupportedSaid
Gary Marcus Observation
“But, crucially, it often gets these wrong, particularly when they involve spatial reasoning beyond a cookie-cutter formula or reasoning about sequences of disparate events.”
Our reading: GPT-4 with either plug-in often gets wrong problems that a human solver would need significant thought to translate into math notation, particularly those involving spatial reasoning beyond a cookie-cutter formula or reasoning about sequences of disparate events.
No horizon Evidence as of
Limitation: The selected problems do not establish a general error rate for translation-intensive tasks or a measured comparison with human difficulty [e1].
Source: Getting GPT to work with external tools is harder than you think
Assessment: UnresolvedSaid
Gary Marcus Observation
“In a few cases GPT-4 set up the calculations correctly and the plug-in returned the correct answer, but then GPT-4 either misunderstood or refused to accept that answer.”
Our reading: In a few test cases GPT-4 set up the calculations correctly and the plug-in returned the correct answer, but GPT-4 then either misunderstood or refused to accept that answer.
No horizon Evidence as of
Limitation: A 2024 transcript revision supplies important qualifications about the 2023 tests.
Source: Getting GPT to work with external tools is harder than you think
Assessment: MixedSaid
Gary Marcus Observation
“In many cases, a naive user might take the results, invariably presented as fact, as correct, when they are not.”
Our reading: GPT-4 with plug-ins invariably presents its results as fact, and in many cases its results are incorrect even though a naive user might take them as correct.
No horizon Evidence as of
Limitation: The mixed result depends on reading invariably literally.
Source: Getting GPT to work with external tools is harder than you think
Assessment: SupportedSaid
Gary Marcus Observation
“Finding a way to get LLMs to reliably use external tools is far from trivial.”
Our reading: Getting LLMs to reliably use external tools is far from trivial.
No horizon Evidence as of
Limitation: The conclusion uses an ordinary qualitative reading of difficulty.
Source: Getting GPT to work with external tools is harder than you think
Assessment: UnresolvedSaid
Gary Marcus Observation
“Thus far we have seen consistently unreliable results, from math to fact-checking.”
Our reading: So far, the authors have seen consistently unreliable results from LLMs using external tools, from math to fact-checking.
No horizon Evidence as of
Limitation: The outcome depends on retaining the explicit personal scope, 'we have seen'.
Source: Getting GPT to work with external tools is harder than you think
Assessment: SupportedSaid
Gary Marcus Causal claim
“Certainly, adding these plug-ins does improve things.”
Our reading: Adding the Code Interpreter and Wolfram Alpha plug-ins improves GPT-4's math performance.
No horizon Evidence as of
Limitation: The shared test set comprises difficult competition problems.
Source: Getting GPT to work with external tools is harder than you think
Assessment: UnresolvedSaid
Gary Marcus Observation
“First, neither GPT-4 nor the plug-ins have the commonsense world knowledge and reasonable abilities needed to reliably translate a word problem into a mathematical calculation.”
Our reading: Neither GPT-4 nor its plug-ins have the commonsense knowledge and reasoning needed to reliably translate word problems into calculations.
No horizon Evidence as of
Limitation: The cited example comes from a college electromagnetism task.
Source: Getting GPT to work with external tools is harder than you think
Assessment: SupportedSaid
Gary Marcus Observation
“GPT-4 and similar current language-based AI programs notoriously struggle with mathematical calculations.”
Our reading: GPT-4 and similar current language-based AI programs struggle with mathematical calculations.
No horizon Evidence as of
Limitation: Results depend on operand sizes, operation mixtures, and prompting.
Source: Getting GPT to work with external tools is harder than you think
Assessment: SupportedSaid
Gary Marcus Observation
“On some problems that capable students might well find hard, the AI did perfectly; on other problems that lot of mathematically-inclined college students people would find easy, the AI failed miserably.”
Our reading: In the authors' tests, GPT-4 with plug-ins did perfectly on some problems that capable students might well find hard, and failed miserably on other problems that many mathematically inclined college students would find easy.
No horizon Evidence as of
Limitation: No student comparison group was tested, so the evidence does not establish student success rates or a measured ranking against students [e1].
Source: Getting GPT to work with external tools is harder than you think
Assessment: UnresolvedSaid
Gary Marcus Observation
“To our knowledge, no LLM-based system can yet reliably do what SHRLDU tried to do then:”
Our reading: As far as the authors know, no LLM-based system can yet reliably do the blocks-world modeling that SHRDLU attempted in the 1970s.
No horizon Evidence as of
Limitation: Planning benchmarks cover only part of the ordinary SHRDLU comparison.
Source: Hello, Multimodal Hallucinations
Assessment: SupportedSaid
Gary Marcus Observation
“As is by now widely known, they are a hallmark of systems that generalize based on word distribution.”
Our reading: As is by now widely known, hallucinations are a hallmark of systems that generalize based on word distribution.
No horizon Evidence as of
Limitation: This establishes neither a single hallucination rate nor that all models perform equally poorly.
Source: Hello, Multimodal Hallucinations
Assessment: UnresolvedSaid
Gary Marcus Forecast
“And no matter what investors and CEOs might tell you, hallucinations will remain inevitable.”
Our reading: Hallucinations in large language models will remain inevitable.
No deadline stated Evidence as of
Limitation: A mathematical reading and a practical-risk reading could yield different assessments.
Source: Hello, Multimodal Hallucinations
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“But in other ways, large language models are a profound step backwards; they lack stable models of the world, which means that they are poor at planning, and unable to reason reliably; everything becomes hit or miss..”
Our reading: LLMs lack stable world models, which makes them poor at planning and unable to reason reliably.
No horizon Evidence as of
Limitation: Evidence supporting the behavioral component is not sufficient to score the entire causal proposition supported.
Source: Hello, Multimodal Hallucinations
Assessment: UnresolvedSaid
Gary Marcus Observation
“ChatGPT went utterly wild when Ernie pushed on the last one, omitting some that are there and and confabulating a standalone island in the middle of the kitchen that simply isn’t there:”
Our reading: When questioned about the kitchen image, ChatGPT's vision model left out drawers that are there and made up a kitchen island that is not there.
No horizon Evidence as of
Limitation: The page reader does not reproduce the relevant image and transcript screenshots.
Source: Hello, Multimodal Hallucinations
Assessment: SupportedSaid
Gary Marcus Observation
“As we can see, multimodal training is not magically making hallucinations go away.”
Our reading: Multimodal training is not making hallucinations go away.
No horizon Evidence as of
Limitation: The evidence does not isolate the causal effect of multimodal training or establish that it cannot reduce hallucination rates.
Source: Hello, Multimodal Hallucinations
Assessment: UnresolvedSaid
Gary Marcus Observation
“The first author’s nine-year-old can do five digit multiplication better than an LLM.”
Our reading: The first author's own nine-year-old child can do five-digit multiplication better than an LLM.
No horizon Evidence as of
Limitation: A private comparison may have occurred.
Assessment: UnresolvedSaid
Gary Marcus Observation
“There are some applications where the current systems are reliable enough to be practically useful, though not nearly as many as is often claimed.”
Our reading: Current systems are reliable enough to be useful in some applications, but far fewer than often claimed.
No horizon Evidence as of
Limitation: The existence of useful assistance is easier to test than the claimed extent of overstatement.
Assessment: SupportedSaid
Gary Marcus Observation
“Transformers and generative AI are getting a lot of attention, and have considerable utility, but they are nothing like the all purpose tools some people seems to imagine them to be.”
Our reading: Transformers and generative AI are getting a lot of attention and have considerable utility, but they are nothing like the all-purpose tools some people seem to imagine them to be.
No horizon Evidence as of
Limitation: The result depends on interpreting all-purpose as broadly reliable practical competence.
Assessment: SupportedSaid
Gary Marcus Forecast
“No doubt the next generation of systems will have greater abilities and more extensive applications and will somewhat mitigate the weaknesses.”
Our reading: The next generation of systems will, without doubt, have greater abilities and more extensive applications and will somewhat mitigate the weaknesses of current systems.
Settled before its deadline Evidence as of
Limitation: The evidence is concentrated in the Claude successor family.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“About 18 months have passed; although there are certainly improvements (the images are vastly better), owing presumably in large part to a larger training set, the fundamental approach (mapping words to images, rather than constructing intermediate models of the world and how language and images relate to those models) remains the same, and so the core issue remains.”
Our reading: About 18 months after DALL-E 2, the images are vastly better, presumably largely because of a larger training set, but the fundamental approach of mapping words to images rather than building intermediate world models is the same, and so the core compositionality issue remains.
No horizon Evidence as of
Limitation: The technical paper was read through a third-party reproduction because the original PDF exceeded the reader's size limit.
Source: Race, statistics, and the persistent cognitive limitations of DALL-E
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“It’s the same issue of canonical versus noncanonical statistics, all over again.”
Our reading: Multimodal ChatGPT's trouble telling time from watch images comes from the same canonical-versus-noncanonical statistics problem seen in DALL-E.
No horizon Evidence as of
Limitation: Later model evaluations cannot be substituted for the historical deployment.
Source: Race, statistics, and the persistent cognitive limitations of DALL-E
Assessment: UnresolvedSaid
Gary Marcus Forecast
“though I agree with Geoff that eventually AI will have a big impact on drug design. Perhaps with current techniques, perhaps not; we will have to see.”
Our reading: AI will eventually have a big impact on drug design, possibly but not necessarily with current techniques.
No deadline stated Evidence as of
Limitation: The threshold for substantial impact is an evaluator interpretation.
Source: What was 60 Minutes thinking, in that interview with Geoff Hinton?
Assessment: UnresolvedSaid
Gary Marcus Observation
“I think it’s fair to say that generative AI works better than most people expected.”
Our reading: Generative AI works better than most people expected.
No horizon Evidence as of
Limitation: A narrower reading about informed AI researchers could produce a different assessment.
Source: What was 60 Minutes thinking, in that interview with Geoff Hinton?
Assessment: MixedSaid
Gary Marcus Causal claim
“It makes current AI hard to debug (nobody knows for example how to ground them in facts), and it makes them difficult predict, which means, unlike calculators or spreadsheets, we don’t really know what’s going to happen when we ask them a question.”
Our reading: Because we don't really understand how trained neural networks do what they do, current AI is hard to debug (for example, nobody knows how to ground them in facts) and difficult to predict, so unlike calculators or spreadsheets we don't really know what will happen when we ask them a question.
No horizon Evidence as of
Limitation: The supplied Anthropic passage describes interpretability obstacles, rather than directly measuring difficulty diagnosing or fixing failures; its demonstrated progress concerns a small model [e1].
Source: What was 60 Minutes thinking, in that interview with Geoff Hinton?