Claims checked against evidence by Gary Marcus, page 14 of 15
Deadlines are judged as of 2026-10-29. About Gary Marcus and the sources covered.
Claims checked against evidence by Gary Marcus (1471)
Each of these claims was checked against dated evidence.
Page 14 of 15: claims 1301 to 1400 of 1471, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Observation
“ChatGPT is (at least in many ways) a far inferior intelligence, that, despite having some undeniable talents, cannot at all be trusted.”
Our reading: ChatGPT is in many ways a far inferior intelligence and cannot be trusted.
No horizon Evidence as of
Limitation: The evidence does not measure the 'far inferior intelligence' part. Neither source compares ChatGPT's overall intelligence with human intelligence.
Assessment: UnresolvedSaid
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: SupportedSaid
Forecast
“we can also expect more and circles of fake web sites in order to sell advertisements”
Our reading: ChatGPT will lead to more circles of fake websites created to sell advertising.
Settled before its deadline Evidence as of
Limitation: Most sites are attributed to AI chatbots in general, not to ChatGPT specifically.
Assessment: UnresolvedSaid
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: SupportedSaid
Observation
“These characters (especially perhaps the latter two) might or might not be argued to be sentient, but it seems to be that they are flexible, resourceful, reliably (in a way that LLMs certainly aren’t), and adaptive.”
Our reading: LLMs are certainly not reliable in the way general intelligences like the Star Trek computer would be.
No horizon Evidence as of
Limitation: 'Reliably' is qualitative. The comparison is to fictional characters, so only the plain reading (frequent errors and inconsistency) is testable.
Assessment: SupportedSaid
Observation
“Already we have seen minor abuses; plagiarism, for example, will never be the same.”
Our reading: Minor abuses of ChatGPT have already been seen, and plagiarism, for example, will never be the same.
No horizon Evidence as of
Limitation: The incident is documented through reporting and the professor's testimony, not a published disciplinary file.
Assessment: UnresolvedSaid
Observation
“The neurosymbolic AI community is growing fast; conferences that used to be dozens are now thousands.”
Our reading: The neurosymbolic AI community is growing quickly, with conferences going from dozens to thousands of attendees.
No horizon Evidence as of
Limitation: The only attendance figure comes from a single virtual IBM event, reported by IBM itself. It may not be comparable with in-person neurosymbolic workshops.
Assessment: SupportedSaid
Observation
“one of the things that popped out to me is that the system still quickly lands in the land of hallucination that has so haunted ChatGPT.”
Our reading: Anthropic's Claude, despite its alignment focus, still quickly hallucinates like ChatGPT.
No horizon Evidence as of
Limitation: The comparison is informal and based on few prompts.
Assessment: MixedSaid
Observation
“CNET became the first casualty of the recently fashionable tendency to put too much faith in ChatGPT.”
Our reading: CNET was the first casualty of putting too much faith in ChatGPT, publishing AI-written stories with many mistakes.
No horizon Evidence as of
Limitation: The ChatGPT denial comes from CNET itself, and the underlying model was not disclosed.
Assessment: UnresolvedSaid
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: SupportedSaid
Observation
“ChatGPT has proven itself to be both unreliable and untruthful. It makes boneheaded arithmetical errors, invents fake biographical details, bungles word problems, defines non-existent scientific phenomenon, stumble over arithmetic conversion, and on and on.”
Our reading: ChatGPT is unreliable and untruthful: it makes arithmetic errors, fabricates biographical details, bungles word problems, and invents scientific phenomena.
No horizon Evidence as of
Limitation: Selected demonstrations do not establish an overall error rate or intentional deception [e1, e2, e3, e4].
Source: Scientists, please don’t let your chatbots grow up to be co-authors
Assessment: UnresolvedSaid
Observation
“That’s a lot, and not the sort of thing that a rocketship startup would typically agree to.”
Our reading: Giving Microsoft 75% of OpenAI's profits until it recoups its investment is not something a rocketship startup would typically agree to.
No horizon Evidence as of
Limitation: The comparison group and historical window require interpretation.
Assessment: SupportedSaid
Observation
“It could sometimes produce outputs that were correct and acceptable in these regards but not reliably.”
Our reading: ChatGPT could sometimes produce outputs that were correct and acceptable in the regards listed (counting, arithmetic, event order, physical reasoning, human psychology, fabrication, bias), but not reliably.
No horizon Evidence as of
Limitation: The evidence covers the seven failure types unevenly.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
Observation
“ChatGPT made all the same kinds of mistakes that its predecessors did.”
Our reading: ChatGPT made all the same kinds of mistakes that earlier GPT-3 versions did.
No horizon Evidence as of
Limitation: Selected failures establish occurrence, not prevalence.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: SupportedSaid
Observation
“It made things up (figure 6). Its output, when released, exhibited sexist and racist biases (figure 7).”
Our reading: ChatGPT made things up and, at release, exhibited sexist and racist biases.
No horizon Evidence as of
Limitation: The bias examples came from adversarial prompts reported in the press, not from a systematic audit.
Source: Large Language Models like ChatGPT say The Darnedest Things
Assessment: UnresolvedSaid
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: SupportedSaid
Forecast
“That’s a huge commitment, 3.5 hours, but one thing that I think all of our speakers could agree on is that artificial intelligence, in whatever form it currently is, is about to have a huge impact on society.”
Our reading: AI, even in its current form, is about to have a huge impact on society.
Settled before its deadline Evidence as of
Limitation: Marcus supplied no fixed deadline or numerical definition of huge.
Source: An epic AI Debate—and why everyone should be at least a little bit worried about AI going into 2023
Assessment: UnresolvedSaid
Causal claim
“But as they do so, as they glom stuff together, something often gets lost in translation: which bits of text do and do not truly belong together.”
Our reading: When LLMs paraphrase and combine text, they often lose track of which pieces truly belong together, producing errors.
No horizon Evidence as of
Limitation: Summarization and truthfulness benchmarks cover different tasks and model populations.
Assessment: SupportedSaid
Causal claim
“Driverless cars have taken many years longer than we were originally told; outliers (AKA edge cases) have, at least so far, prevented them from making the transition from demo to widely-available reality.”
Our reading: Driverless cars have taken years longer than promised, because edge cases have so far kept them from becoming widely available.
No horizon Evidence as of
Limitation: The result depends on reading widely available as broad access beyond restricted local services.
Assessment: UnresolvedSaid
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.
Assessment: SupportedSaid
Observation
“Large language models are not databases. They are glommers-together-of-bits-that don’t always belong together.”
Our reading: LLMs are not databases; they combine bits of text that don't always belong together.
No horizon Evidence as of
Limitation: The cited passages do not establish a general error rate across models or tasks.
Assessment: SupportedSaid
Forecast
“I guarantee that minds will be blown.”
Our reading: GPT-4 will impress people enormously when released.
Settled before its deadline Evidence as of
Limitation: The accounts are illustrative, not a representative opinion survey.
Assessment: UnresolvedSaid
Forecast
“Without external aids it won’t be able beat Meta’s Cicero in Diplomacy; it won’t be able to drive a car reliably; it won’t be able to reliably guide a robot like Optimus to be anything like as versatile as Rosie the Robot.”
Our reading: Without external aids, GPT-4 won't be able to beat Meta's Cicero in Diplomacy, won't be able to drive a car reliably, and won't be able to reliably guide a robot like Optimus to be anything like as versatile as Rosie the Robot.
No deadline stated Evidence as of
Limitation: The assistance boundary materially affects which demonstrations qualify.
Assessment: SupportedSaid
Forecast
“It will often do what you want, sometimes not—and it will remain difficult to anticipate which in advance..”
Our reading: It will remain difficult to anticipate in advance when GPT-4 will or won't do what users want.
Settled before its deadline Evidence as of
Limitation: The finding is strongest for task correctness and settings where mistakes carry high costs.
Assessment: UnresolvedSaid
Forecast
“GPT-4 will be trained on considerably more, a significant fraction of the internet as a whole.”
Our reading: GPT-4 will be trained on considerably more data than GPT-3, a significant fraction of the internet.
No deadline stated Evidence as of
Limitation: This is an evidence gap, not evidence that GPT-4 used little data.
Assessment: UnresolvedSaid
Forecast
“Its natural language output still won’t be something that one can reliably hook up to downstream programs; it won’t be something, for example, that you can simply and directly hook up to a database or virtual assistant, with predictable results.”
Our reading: GPT-4's natural language output still won't be something one can reliably hook up to downstream programs, for example simply and directly to a database or virtual assistant with predictable results.
No deadline stated Evidence as of
Limitation: The numerical evaluation is provider-reported and concerns complex schemas.
Assessment: UnresolvedSaid
Conditional forecast
“GPT-4 may be better at faking term papers, but if it follows the same playbook as it predecessors, it still won’t really understand the world, and seams will eventually show.”
Our reading: If GPT-4 follows its predecessors' approach, it will not truly understand the world and its flaws will eventually show.
No deadline stated Evidence as of
Only if: if it follows the same playbook as its predecessors
Assessment: UnresolvedSaid
Forecast
“GPT-4 will not have reliable models of the things that it talks about that are accessible to external programmers in a way that reliably feeds downstream processes.”
Our reading: GPT-4 will not have reliable models of the things it talks about that are accessible to external programmers in a way that reliably feeds downstream processes.
No deadline stated Evidence as of
Limitation: The meaning of accessible models is ambiguous.
Assessment: SupportedSaid
Forecast
“The system will still not be able to restrict its output to reliably following a shared set of human values around helpfulness, harmlessness, and truthfulness.”
Our reading: GPT-4 will not reliably follow human values of helpfulness, harmlessness and truthfulness.
Settled before its deadline Evidence as of
Limitation: The judgment depends on an ordinary, nonnumerical reading of reliability.
Assessment: UnresolvedSaid
Observation
“As I told NYT columnist Farhad Manjoo, ChatGPT, like earlier, related systems is “still not reliable, still doesn’t understand the physical world, still doesn’t understand the psychological world and still hallucinates.””
Our reading: ChatGPT is not reliable, does not understand the physical or psychological world, and hallucinates.
No horizon Evidence as of
Limitation: The result depends on treating understanding as practical reasoning competence.
Assessment: UnresolvedSaid
Forecast
“In retrospectives written in 2043, intellectual historians will conclude that there was an initial overemphasis on large language models, and a gradual but critical shift of the pendulum back to more structured systems with deeper comprehension.”
Our reading: In retrospectives written in 2043, intellectual historians will conclude that there was an initial overemphasis on large language models and a gradual but critical shift of the pendulum back to more structured systems with deeper comprehension.
Not yet due Evidence as of
Limitation: The forecast leaves the author population and degree of agreement unspecified.
Assessment: UnresolvedSaid
Forecast
“Although GPT-4 will definitely seem smarter than its predecessors, its internal architecture remains problematic.”
Our reading: GPT-4 will seem smarter than predecessors but its architecture remains problematic.
No deadline stated Evidence as of
Limitation: The comparative evaluation was conducted by the model's developer.
Assessment: SupportedSaid
Forecast
“GPT-4 will solve many of the individual specific items used in prior benchmarks, but still get tripped up, particularly in longer and more complex scenarios.”
Our reading: GPT-4 will solve many prior benchmark items but still fail, especially on longer, complex scenarios.
Settled before its deadline Evidence as of
Limitation: Benchmark success does not establish whether individual items were learned or memorized.
Assessment: SupportedSaid
Forecast
“As OpenAI has learned, bigger in many ways means better, with outputs more and more humanlike with each iteration. GPT-4 is going to be a monster.”
Our reading: Because scale improves output in many ways, GPT-4 will be very powerful.
Settled before its deadline Evidence as of
Limitation: The decisive evaluation was conducted by the model developer.
Assessment: SupportedSaid
Forecast
“It will still make a significant number of shake-your-head stupid errors, in ways that are hard to fully predict.”
Our reading: GPT-4 will make a significant number of hard-to-predict stupid errors.
Settled before its deadline Evidence as of
Limitation: The judgment depends on reading hard to predict as unexpectedly uneven performance, rather than requiring a direct prediction experiment.
Assessment: UnresolvedSaid
Observation
“Which is to say it makes stuff up, regularly; a fair bit of what it is says simply isn’t true.”
Our reading: ChatGPT regularly makes things up and a fair amount of its output is false.
No horizon Evidence as of
Limitation: This does not mean fabrication was absent or rare.
Assessment: SupportedSaid
Forecast
““Alignment” between what humans want and what machines do will continue to be a critical, unsolved problem.”
Our reading: Alignment will remain a critical unsolved problem with GPT-4.
Settled before its deadline Evidence as of
Limitation: This assesses behavioral alignment in GPT-4, not every meaning of alignment.
Assessment: UnresolvedSaid
Forecast
““Scaling” alone—building bigger and models until they absorb the entire internet — will prove useful, but only to a point.”
Our reading: Scaling alone will prove useful only up to a point.
No deadline stated Evidence as of
Limitation: The claim specifies no deadline, capability threshold, or operational test of trustworthy general intelligence.
Assessment: SupportedSaid
Observation
“In short, ChatGPT might sound like the Star Trek computer, but for now, you still can’t trust it.”
Our reading: ChatGPT cannot currently be trusted.
No horizon Evidence as of
Limitation: The conclusion concerns unverified factual reliance, not every possible use.
Assessment: SupportedSaid
Forecast
“Some of its advice will be head-scratchingly bad.”
Our reading: Some of GPT-4's advice will be very bad.
Settled before its deadline Evidence as of
Limitation: The study concerns a particular infectious-disease setting and does not measure all GPT-4 advice.
Assessment: SupportedSaid
Forecast
“When queried on medicine it will either resist answering (if there are aggressive guardrails) or occasionally spout plausible-sounding but dangerous nonsense.”
Our reading: On medical queries GPT-4 will either refuse (with aggressive guardrails) or occasionally produce plausible but dangerous nonsense.
Settled before its deadline Evidence as of
Limitation: The decisive evaluation concerns bloodstream-infection management and the tested GPT-4 version.
Assessment: SupportedSaid
Observation
“Although ChatGPT can write about anything, it is also easily confused.”
Our reading: ChatGPT is easily confused.
No horizon Evidence as of
Limitation: The arithmetic demonstrations were selected to probe weaknesses.
Assessment: SupportedSaid
Forecast
“It will remain turbocharged pastiche generator, and a fine tool for brainstorming, and for first drafts, but not trustworthy general intelligence.”
Our reading: GPT-4 will be a pastiche generator useful for brainstorming and drafts but not trustworthy general intelligence.
Settled before its deadline Evidence as of
Limitation: The experiment evaluates people working with GPT-4 on consulting tasks, not dependable performance across all general tasks.
Assessment: SupportedSaid
Forecast
“GPT-4 will still, like its predecessors, be a bull in a china shop, reckless and hard to control.”
Our reading: GPT-4 will be reckless and hard to control like its predecessors.
Settled before its deadline Evidence as of
Limitation: Adversarial test results do not establish an ordinary-use failure rate.
Assessment: SupportedSaid
Observation
“Alas, the genie can no longer be stuffed back in the bottle.”
Our reading: Misinformation-capable LLM technology can no longer be contained or withdrawn.
No horizon Evidence as of
Limitation: Shows availability during the period, not long-term containment.
Source: AI's Jurassic Park moment
Assessment: SupportedSaid
Observation
“Large language models lack mechanisms for verifying truth; we need to find new ways to integrate them with the tools of classical AI, such as databases, webs of knowledge, and reasoning.”
Our reading: LLMs lack mechanisms for verifying truth.
No horizon Evidence as of
Limitation: The result depends on distinguishing standalone models from augmented systems.
Source: AI's Jurassic Park moment
Assessment: SupportedSaid
Observation
“For another, chatGPT, just released by OpenAI, is more or less just as capable of producing similar nonsense, such as instant essays on adding wood chips to breakfast cereal.”
Our reading: ChatGPT is about as capable as Galactica of producing nonsense misinformation.
No horizon Evidence as of
Limitation: The packet found no head-to-head test of ChatGPT and Galactica, so 'more or less just as capable' is judged qualitatively, not measured.
Source: AI's Jurassic Park moment
Assessment: UnresolvedSaid
Causal claim
“they cost almost nothing to operate, and so they are on a path to reducing the cost of generating disinformation to zero.”
Our reading: Because generative AI systems like ChatGPT, Dall-E 2, and Lensa cost almost nothing to operate, they are on a path to reducing the cost of generating disinformation to zero.
No horizon Evidence as of
Limitation: The economically negligible reading of 'zero' is an interpretation.
Source: AI's Jurassic Park moment
Assessment: UnresolvedSaid
Observation
“they can easily be automated to generate misinformation at unprecedented scale.”
Our reading: Generative AI systems like ChatGPT, Dall-E 2, and Lensa can easily be automated to generate misinformation at unprecedented scale.
No horizon Evidence as of
Limitation: The supplied experiment predates the November–December 2022 target period [e1].
Source: AI's Jurassic Park moment
Assessment: UnresolvedSaid
Conditional forecast
“If the website is flooded with worthless code examples, programmers will no longer go there, its database of over 30 million questions and answers will become untrustworthy, and the 14 year old website will die.”
Our reading: If Stack Overflow is flooded with worthless generated code, programmers will leave, its database will become untrustworthy, and the site will die.
No deadline stated Evidence as of
Only if: if the website is flooded with worthless AI-generated code examples
Source: AI's Jurassic Park moment
Assessment: SupportedSaid
Causal claim
“GPT doesn’t talk randomly, because it’s pastiching things actual people said.”
Our reading: GPT's output is not random, because it recombines things real people have said.
No horizon Evidence as of
Limitation: The result depends on reading pastiching as learned recombination rather than literal copying.
Source: How come GPT can seem so brilliant one minute and so breathtakingly dumb the next?
Assessment: UnresolvedSaid
Causal claim
“When GPT sounds plausible, it is because every paraphrased bit that it pastes together is grounded in something that actual humans said, and there is often some vague (but often irrelevant) relationship between..”
Our reading: GPT sounds plausible because each piece it combines comes from something humans actually said, and the pieces are often loosely related.
No horizon Evidence as of
Limitation: The meanings of bit, paraphrase and grounded materially affect the test.
Source: How come GPT can seem so brilliant one minute and so breathtakingly dumb the next?
Assessment: SupportedSaid
Observation
“It could not be immediately applied to the challenge of, say, customer service or guiding the actions of a domestic robot, or well, almost anything else, really.”
Our reading: Cicero cannot be immediately applied to tasks such as customer service or domestic robotics, or almost anything besides blitz Diplomacy.
No horizon Evidence as of
Limitation: The evidence is a developer capability statement, not an independent transfer benchmark.
Source: What does Meta AI’s Diplomacy-winning Cicero Mean for AI?
Assessment: SupportedSaid
Observation
“Strikingly, and in opposition to much of the Zeitgeist, Cicero relies quite heavily on hand-crafting, both in the data sets, and in the architecture; in this sense it is in many ways more reminiscent of classical “Good Old Fashioned AI” than deep learning systems that tend to be less structured, and less customized to particular problems.”
Our reading: Against much of the Zeitgeist, Cicero relies quite heavily on hand-crafting in both its data sets and its architecture, and in this sense it is in many ways more reminiscent of classical Good Old Fashioned AI than of deep learning systems, which tend to be less structured and less customized to particular problems.
No horizon Evidence as of
Limitation: Quite heavily is assessed through substantive design choices, not a measured percentage of effort or performance.
Source: What does Meta AI’s Diplomacy-winning Cicero Mean for AI?
Assessment: UnresolvedSaid
Observation
“As it turns out, the architecture of Cicero differs profoundly from most of what’s been talked about in recent years in AI.”
Our reading: Cicero's architecture differs profoundly from most AI approaches discussed in recent years.
No horizon Evidence as of
Limitation: Selected examples establish differences, not prevalence.
Source: What does Meta AI’s Diplomacy-winning Cicero Mean for AI?
Assessment: SupportedSaid
Observation
“Cicero makes extensive use of machine learning, but is hardly a poster child for simply making ever bigger models (so-called “scaling maximalism”), nor for the currently popular view of “end-to-end” machine learning of in which some single general learning algorithm applies across the board, with little internal structure and zero innate knowledge.”
Our reading: Cicero makes extensive use of machine learning but is hardly a poster child for scaling maximalism or for the popular end-to-end view of machine learning with little internal structure and zero innate knowledge.
No horizon Evidence as of
Limitation: The architecture alone does not establish how much each component contributed to performance.
Source: What does Meta AI’s Diplomacy-winning Cicero Mean for AI?
Assessment: UnresolvedSaid
Conditional forecast
“See also my essay on Google’s Palm-SayCan, for what might happen if this tech were embedded in robots, which would be even more risky.”
Our reading: Embedding LLM technology in robots would be even more risky than using it in home assistants.
No deadline stated Evidence as of
Only if: if LLM tech were embedded in robots
Assessment: UnresolvedSaid
Conditional forecast
“if Alexa could converse freely, user expectations would go through the roof, and mostly be unmeetable.”
Our reading: If Alexa could converse freely, user expectations would rise to a level that mostly could not be met.
No deadline stated Evidence as of
Only if: if Alexa could converse freely
Assessment: SupportedSaid
Observation
“Bottom line: From the outset Large Language Models like GPT-3 have great at generating surrealist prose, and they can beat a lot of benchmarks, but they are not (and may never be) great tech for reliably inferring user intent from what users say.”
Our reading: LLMs like GPT-3 are not great technology for reliably inferring user intent from what users say.
No horizon Evidence as of
Limitation: The principal evidence is a research benchmark, not a representative household deployment study.
Assessment: UnresolvedSaid
Forecast
“Turning LLMs into a product that controls your home and talks to you in a way that would be reliable enough to use at scale in millions of homes is still a long, long way away.”
Our reading: A reliable LLM-based conversational home-control product usable at scale in millions of homes is still a long, long way away.
No deadline stated Evidence as of
Limitation: Large-scale participation is company-reported.
Assessment: UnresolvedSaid
Observation
“Just like every other large language model I have seen.”
Our reading: Like Galactica, every other large language model the author has seen prevaricates (fabricates content) a lot.
No horizon Evidence as of
Limitation: False answers on deliberately challenging questions are not a direct estimate of fabrication in ordinary use.
Source: A Few Words About Bullshit
Assessment: SupportedSaid
Observation
“Pitch perfect and utterly bogus imitations of science and math, presented as the real thing.”
Our reading: The Galactica examples Andrew Sundstrom shared are pitch-perfect but utterly bogus imitations of science and math, presented as the real thing.
No horizon Evidence as of
Limitation: Support depends on including the companion examples expressly linked in the post.
Source: A Few Words About Bullshit
Assessment: UnresolvedSaid
Forecast
“High school students will love it, and use it to fool and intimidate (some of) their teachers.”
Our reading: High school students will love Galactica and will use it to fool and intimidate some of their teachers.
No deadline stated Evidence as of
Limitation: A search that finds no qualifying incidents cannot establish that none occurred.
Source: A Few Words About Bullshit
Assessment: UnresolvedSaid
Forecast
“The problem is about to get much, much worse.”
Our reading: The problem of misinformation on Twitter is about to get much, much worse.
No deadline stated Evidence as of
Limitation: An increase in misinformation exposure could occur without an increase in posting volume.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: UnresolvedSaid
Conditional forecast
“As the pace of machine-generated misinformation picks up, Twitter’s existing effort, Community Notes (aka Birdwatch), which is mostly done manually, by humans, is going to get left in the dust.”
Our reading: As machine-generated misinformation accelerates, Twitter's mostly manual Community Notes will be overwhelmed.
No deadline stated Evidence as of
Only if: as the pace of machine-generated misinformation picks up
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: UnresolvedSaid
Observation
“Large language models are without equal at generating misinformation, but they suck at detecting it.”
Our reading: LLMs are unmatched at generating misinformation but poor at detecting it.
No horizon Evidence as of
Limitation: No admitted comparison establishes unmatched misinformation generation during the target period.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: UnresolvedSaid
Forecast
“Scaling won’t solve this.”
Our reading: Scaling LLMs will not solve their inability to reason about truth and detect misinformation.
No deadline stated Evidence as of
Limitation: Truthful answer generation and misinformation detection are related but distinct tasks.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: SupportedSaid
Observation
“Knockoffs of GPT-3 are getting cheaper and cheaper, which means that the cost of generating misinformation is going to zero, and the quantity of misinformation is going to rise—probably exponentially.”
Our reading: Knockoffs of GPT-3 are getting cheaper and cheaper.
No horizon Evidence as of
Limitation: The result depends on including inference operating costs in cheaper.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: SupportedSaid
Causal claim
“Knockoffs of GPT-3 are getting cheaper and cheaper, which means that the cost of generating misinformation is going to zero, and the quantity of misinformation is going to rise—probably exponentially.”
Our reading: Cheaper GPT-3 knockoffs will drive the cost of generating misinformation toward zero.
No horizon Evidence as of
Limitation: Support depends on interpreting zero as negligible marginal cost.
Source: Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”
Assessment: SupportedSaid
Observation
“Scaling here was largely AWOL; even the best model was only at 80.6%, and for most models, scaling had at best a neglible effect.”
Our reading: On the Ruis et al. pragmatics task, the best model reached only 80.6%, and for most models scaling had at best a negligible effect.
No horizon Evidence as of
Limitation: Support depends on reading 80.6% as the overall mean, not the maximum across every prompt configuration.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: SupportedSaid
Observation
“Scaling is an empirical observation, not a guaranteed, continued law of nature.”
Our reading: AI scaling is an empirical observation, not a law of nature that is guaranteed to keep holding.
No horizon Evidence as of
Limitation: An empirical relationship can remain useful and predictive without being guaranteed.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: UnresolvedSaid
Forecast
“Scaling maximalism is an interesting hypothesis, but I stand by my prediction that it won’t get us to AGI.”
Our reading: Scaling maximalism will not get us to AGI.
No deadline stated Evidence as of
Limitation: This is not a finding that AGI is absent everywhere; it is a finding that the reviewed evidence does not settle this forecast.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: UnresolvedSaid
Conditional forecast
“Any one of them would mean we need a real paradigm shift, if we are to get to AGI.”
Our reading: If any one of the three failure modes (not enough data, not enough compute, tasks that don't scale) holds, reaching AGI will need a real paradigm shift.
No deadline stated Evidence as of
Only if: if any of the three failure modes (data, compute, non-scaling tasks) holds
Source: What “game over” for the latest paradigm in AI might look like
Assessment: UnresolvedSaid
Causal claim
“His hypothesis, now generally called scaling maximalism, remains extremely popular, in no small part because bigger and bigger models have indeed continued to do ever more impressive things.”
Our reading: Scaling maximalism remains extremely popular, in no small part because bigger and bigger AI models have continued to do ever more impressive things.
No horizon Evidence as of
Limitation: The survey concerns NLP researchers, not all AI researchers, companies, investors, or the public.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: UnresolvedSaid
Observation
“As I have long argued, capturing this without cognitive models and common sense is really hard.”
Our reading: Capturing the pragmatic implications of language without cognitive models and common sense is really hard.
No horizon Evidence as of
Limitation: The finding does not deny the demonstrated difficulty.
Source: What “game over” for the latest paradigm in AI might look like
Assessment: SupportedSaid
Observation
“Of course we see the same thing over and over in language systems, too.”
Our reading: Language systems repeatedly show the same mix of fluent output and lack of understanding.
No horizon Evidence as of
Limitation: Support concerns the recurring behavioral pattern, not the exact anecdotes.
Assessment: UnresolvedSaid
Observation
“It’s also, as ever, stuck in the liminal space between superintelligence and super-lost.”
Our reading: Make a Video, as with AI systems before it, is caught between very impressive output and basic errors, such as giving a cat a human hand or a painting that never changes.
No horizon Evidence as of
Limitation: The original moving clips were not independently inspected in this assessment.
Assessment: UnresolvedSaid
Forecast
“This is the uncanny valley we are in, and one that we occupy for a while.”
Our reading: AI systems such as video, image, and language generators are now in an uncanny valley of impressive output with basic errors, and will stay there for a while.
No deadline stated Evidence as of
Limitation: This outcome does not mean the observed persistence is unsupported; it reflects the missing duration and resolution threshold.
Assessment: UnresolvedSaid
Causal claim
“We in AI have a long long way to go. Data and compute alone won’t be enough.”
Our reading: AI has a long way to go, and data and compute alone will not be enough.
No horizon Evidence as of
Limitation: Benchmark performance covers only part of the capability claim.
Source: Sub-Optimal
Assessment: UnresolvedSaid
Observation
“Musk barely even acknowledged the problem, and certainly didn’t lay out any sort of credible strategy for attacking it..”
Our reading: Musk barely acknowledged the common-sense problem and laid out no credible strategy for addressing it.
No horizon Evidence as of
Limitation: An absence claim requires stronger coverage than targeted keyword searches.
Source: Sub-Optimal
Assessment: UnresolvedSaid
Causal claim
“The reality is that AI needs genuine, paradigm-shift level innovation. Simply building ever bigger neural networks won’t cut it.”
Our reading: AI needs paradigm-shift innovation; simply scaling neural networks will not be enough.
No horizon Evidence as of
Limitation: The capability target and paradigm boundary are not operationally defined.
Source: Sub-Optimal
Assessment: ContradictedSaid
Observation
“There was a lot of bluster (we will 100x the world’s productivity) but no road map.”
Our reading: The Optimus presentation contained bluster about productivity but no road map.
No horizon Evidence as of
Limitation: The delivery estimate was explicitly tentative [e1]. The evidence does not establish whether the proposed sequence or timing was later achieved.
Source: Sub-Optimal
Assessment: UnresolvedSaid
Observation
“Second, there was very little vision for how Tesla would build the cognitive part of the AI they will need, beyond the basics of motor control (which Boston Dynamics already does so well), nor much recognition about why robotics is so hard in the real world.”
Our reading: Tesla showed very little vision for building the cognitive AI Optimus needs beyond motor control, and little recognition of why real-world robotics is hard.
No horizon Evidence as of
Limitation: The cited notes are secondary and include automated transcription.
Source: Sub-Optimal
Assessment: SupportedSaid
Observation
“Sooner or later he will recognize that humanoid robots, too, pose a whole set of challenges that lie well beyond our current grasp.”
Our reading: Humanoid robots pose a set of challenges well beyond current technology's grasp.
No horizon Evidence as of
Limitation: The decisive account addresses shared robotics limitations rather than directly testing a humanoid in a home.
Source: Sub-Optimal
Assessment: UnresolvedSaid
Observation
“Musk is almost never right about deadlines.”
Our reading: Elon Musk's deadlines for technology projects are almost never met.
No horizon Evidence as of
Limitation: The usable evidence is concentrated on Tesla.
Source: Top 5 reasons Tesla’s Optimus announcement may turn out be a dud
Assessment: SupportedSaid
Observation
“Over and over, the field of AI has discovered otherwise. Guy van den Broeck’s study is a perfect example of this.”
Our reading: The AI field has repeatedly found that doing well on a benchmark designed for X does not mean a system has actually solved X.
No horizon Evidence as of
Limitation: The studies cover selected logical-reasoning and language-inference tasks, not all AI benchmarks or models [e4, e5].
Assessment: SupportedSaid
Observation
“But the smart money is still on being cautious about this, and in recognizing that what the Facebook researchers said in 2019 is still the case.”
Our reading: State-of-the-art models still learn to exploit spurious statistical patterns rather than learning meaning flexibly as humans do, so benchmark gains warrant caution.
No horizon Evidence as of
Limitation: The evidence covers selected NLP models and tasks, not every leading model available by September 23, 2022 [e6, e7].
Assessment: UnresolvedSaid
Observation
“On #1, the answer is a clear yes; only highly trained human artists could do better.”
Our reading: Image synthesis systems generate high quality images that only highly trained human artists could surpass.
No horizon Evidence as of
Limitation: Image quality can vary by task and evaluator.
Source: Form, function, and the giant gulf between drawing a picture and understanding the world
Assessment: SupportedSaid
Observation
“On #2, the answer is mixed. They do well on some inputs (like astronaut rides horse) but more poorly on others (like horse rides astronaut, which I discussed in an earlier post).”
Our reading: Image synthesis systems only inconsistently match their linguistic input to the images produced, doing well on some prompts and poorly on others.
No horizon Evidence as of
Limitation: The strongest experimental evidence covers DALL-E 2.
Source: Form, function, and the giant gulf between drawing a picture and understanding the world
Assessment: UnresolvedSaid
Observation
“Nine more tries, and only one very marginal success (top right corner):”
Our reading: Stable Diffusion produced only one very marginal success in nine further attempts at a part-function prompt.
No horizon Evidence as of
Limitation: Success grading includes a subjective boundary.
Source: Form, function, and the giant gulf between drawing a picture and understanding the world
Assessment: UnresolvedSaid
Observation
“Even “draw a white bicycle with green wheels”, which focuses purely on part-whole relationships without function or complex syntax, is problematic:”
Our reading: Stable Diffusion has trouble even with simple part-whole prompts such as a white bicycle with green wheels.
No horizon Evidence as of
Limitation: Failure to locate independent records does not show that Marcus's displayed results were inaccurate.
Source: Form, function, and the giant gulf between drawing a picture and understanding the world
Assessment: UnresolvedSaid
Observation
“Negation is, as ever, a problem.”
Our reading: Text-to-image systems like Stable Diffusion continue to have problems with negation in prompts.
No horizon Evidence as of
Limitation: This is an evidence gap, not a finding that negation worked reliably.
Source: Form, function, and the giant gulf between drawing a picture and understanding the world
Assessment: UnresolvedSaid
Observation
“Google cloaks its current work the trappings of science (article preprints with citations and graphs and so forth) but when it comes to community inspection, they’ve systematically opted out.”
Our reading: Google has systematically refused to let the scientific community inspect models such as Imagen and LaMDA.
No horizon Evidence as of
Limitation: The evidence distinguishes public availability from scientific access but does not fully document the latter.
Source: Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?