[HN Gopher] A Man Out to Prove How Dumb AI Still Is
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A Man Out to Prove How Dumb AI Still Is
Author : fortran77
Score : 39 points
Date : 2025-04-04 21:09 UTC (1 hours ago)
(HTM) web link (www.theatlantic.com)
(TXT) w3m dump (www.theatlantic.com)
| echelon wrote:
| > When I spoke with him earlier this year, Chollet told me that
| AI companies have long been "intellectually lazy"
|
| s/intellectually lazy/hype maxing for fundraising/
| refulgentis wrote:
| I think it's fascinating that his impossible benchmark got
| defeated, but because the Keras guy doesn't like LLMs, it is
| possible to mishear algorithmic distaste as saying people
| shipping this are "lazy" and "hype maxing."
| artificialprint wrote:
| Arc agi 1 that "got defeated" was published even before first
| mainstream llms and still stood the test of time
| refulgentis wrote:
| "LLMs won't lead to AGI - $1,000,000 Prize to find true
| solution" - https://www.dwarkesh.com/p/francois-chollet -
| 1.5 hours with the gent and Dwarkesh (June 2024, and by
| December, well...we can all agree there's an ARC AGI 2 now)
| whiplash451 wrote:
| Francois never said he dislike LLMs. In fact, he said he
| expected them to be part of the solution to ARC.
|
| I don't know where this persistent myth comes from, but it
| has to go.
| refulgentis wrote:
| > _part of_ the solution to ARC. I don't know where this
| persistent myth comes from,
|
| Part of, explicitly, not _the_ , quite 100% explicitly. The
| TL;DR is "LLMs can't do it alone, program synthesis
| leveraging LLMs is my bet". Not "Maybe not LLMs but they'll
| certainly help us get there!", quite the opposite! Hence:
| well, TFA. And the intellectually lazy quote we are
| explicitly discussing. And anything Chollet has said on the
| subject. [^1]
|
| [^1]"LLMs won't lead to AGI - $1,000,000 Prize to find true
| solution" - https://www.dwarkesh.com/p/francois-chollet -
| 1.5 hours with the gent
| andersco wrote:
| https://archive.is/7PL2a
| j_bum wrote:
| > To hit 87 percent on the original ARC-AGI test, o3 spent
| roughly 14 minutes per puzzle and, by my calculations, may have
| required hundreds of thousands of dollars in computing and
| electricity
|
| > the bot came up with more than 1,000 possible answers per grid
| before selecting a final submission.
|
| Yeah, AGI is right around the corner... /s
| HenryBemis wrote:
| Let's all hope two things: 1) AGI is not near 2) Nobody ever
| allows it to have physical presence 2b) should that happen,
| expect the one idiot ask it to make "as many paperclips as
| possible" :)
| i_am_proteus wrote:
| >Chollet, a French computer scientist and one of the industry's
| sharpest skeptics
|
| I feel like this description really buries the lede on Chollet's
| expertise. (For those who don't know, he's the creator of and
| lead contributor[0] to Keras)
|
| [0]https://github.com/keras-team/keras/graphs/contributors
| some_random wrote:
| Calling Francois Chollet just "A Man" in the title (or "The Man"
| in the actual article as of writing) is crazy work, he's been
| deeply involved in ML for ages including creating Keras.
| noosphr wrote:
| >Last week, the ARC Prize team released an updated test, called
| ARC-AGI-2, and it appears to have sent the AIs back to the
| drawing board. The full o3 model has not yet been tested, but a
| version of o1 dropped from 32 percent on the original puzzles to
| just 3 percent on the new version, and a "mini" version of o3
| currently available to the public dropped from roughly 30 percent
| to below 2 percent. (An OpenAI spokesperson declined to say
| whether the company plans to run the benchmark with o3.) Other
| flagship models from OpenAI, Anthropic, and Google have achieved
| roughly 1 percent, if not lower. Human testers average about 60
| percent.
|
| Arc AGI is the main reason why I don't trust static bench marks.
|
| If you don't have an essentially infinite set to draw your
| validation data from then a large enough model will memorize it
| as part of its developer teams KPIs.
|
| Forget all these fancy benchmarks. If you want to saturate any
| model today give it a string and a grammar and ask it to generate
| the string from the grammar. I've had _every_ model fail this on
| regular grammars with strings of more than 4 characters long.
|
| LLMs are the solution to natural language, which is a huge deal.
| They aren't the solution to reasoning which is still best solved
| with what used to be called symbolic AI before it started
| working, e.g. sat solvers.
| mdp2021 wrote:
| > _I 've had _every_ model fail this_
|
| That seems to be because LLMs don't seem to be able to follow
| procedures (e.g. reliably counting).
| whiplash451 wrote:
| Show me the results of your symbolic AI on ARC 2.
| gambiting wrote:
| >> If you want to saturate any model today give it a string and
| a grammar and ask it to generate the string from the grammar.
|
| I'm not sure I understand what that means - could you explain
| please?
| elpocko wrote:
| I'm not GP, but here goes:
|
| LLMs are token-based, which are words or word fragments; they
| have limited ability to work on a letter-by-letter basis.
| They can't reliably count letters in a sentence, for example.
| "give it a string and a grammar and ask it to generate the
| string from the grammar" can't be done by inference alone
| because of this: they would generate tokens that don't match
| the grammar.
|
| But you can use a grammar-based sampler and it'll generate
| valid strings just fine. llama.cpp can easily do this if you
| provide an EBNF grammar specification.
| janalsncm wrote:
| It means applying specific rules about how text can be
| generated. For example, generating valid json reliably.
| Currently we use constrained decoding to accomplish this
| (e.g. the next token _must_ be one of three valid options).
|
| Now you can imagine giving an LLM arbitrary validity rules
| for generating text. I think that's what they mean by
| "grammar".
| janalsncm wrote:
| > best solved with what used to be called symbolic AI before it
| started working
|
| Right, the current paradigm of requiring an LLM to do arbitrary
| digit multiplication will not work and we shouldn't need to. If
| your task is "do X" and it can be _reliably_ accomplished with
| "write a python program to do X" that's good enough as far as
| I'm concerned. It's preferable, in fact.
|
| Btw Chollet has said basically as much. He calls them "stored
| programs" I think.
|
| I think he is onto something. The right atomic to approach
| these problems is probably not the token, at least at first.
| Higher level abstraction should be refined to specific
| components, similar to the concept of diffusion.
| Ologn wrote:
| Most human ten year olds in school can add two large numbers
| together. If a connectionist network is supposed to model the
| human brain, it should be able to do that. Maybe LLMs can do
| a lot of things, but if they can't do that, then they're an
| incomplete model of the human brain.
| SpicyLemonZest wrote:
| No LLM or other modern AI architecture I'm aware of is
| supposed to model the human brain. Even if they were, LLMs
| can add large numbers with the level of skill I'd expect
| from a 10 year old:
|
| ----
|
| What's 494547645908151+7640745309351279642?
|
| ChatGPT said: The sum of 494,547,645,908,151 and
| 7,640,745,309,351,279,642 is:
|
| 7,641,239,857,997,187,793
|
| ----
|
| (7,641,239,856,997,187,793 is the correct answer)
| michaelmarkell wrote:
| If I were to guess, most (adult) humans could not add two 3
| digit numbers together with 100% accuracy. Maybe 99%?
| Computers can already do 100%, so we should probably be
| trying to figure out how to use language to extract the
| numbers from stuff and send them off to computers to do the
| calculations. Especially because in the real world most
| numbers that matter are not just two digits addition
| janalsncm wrote:
| Artificial neural nets are pretty far from brains. We don't
| use them because they are like brains, we use them because
| they can approximate arbitrary functions given sufficient
| data. In other words, they work.
| mdp2021 wrote:
| > _that's good enough as far as I'm concerned_
|
| But in that case, why an LLM. If we want Question-Answer
| machines to be reliable, they must have the skills which
| include "counting" just as a basic example.
| janalsncm wrote:
| The purpose of the LLM would be to translate natural
| language into computer language, not to do the calculation
| itself.
| mikestew wrote:
| Not to dismiss Chollet's work, but I'm starting to think he need
| prove nothing to even the muggles. For example, nearly any
| endurance athlete stands a good chance of being a Strava user. If
| you run in those circles, have you heard a _single_ person with
| anything good to say about Strava's "Athletic Intelligence"?
| Garmin is rolling out a beta right now that includes "AI
| Insights" or summat. Same deal: useless summaries like "you ran 5
| miles today, which contributes to your aerobic base". I could do
| better with a database and some _if /else_ statements. And Garmin
| wants a subscription for this. (It's included in Strava's
| subscription, but I suppose you're still paying for it.) And so
| now the memes tend toward "dumb AI insight of the day" on many
| online forums.
|
| Seems to me that a lot of folks are enjoying having an LLM
| rewrite their email or whatever, but I wonder how many are
| actually buying the rest of it? The companies themselves sure
| aren't helping.
| anthomtb wrote:
| I use Strava for mountain biking and the Athletic Intelligence
| is just comical.
|
| "This ride was longer and harder than usual" - no sh*t, the
| map, elevation profile and my legs have already informed me.
|
| "You set 3 new PRs" - I can see that with one flick of the
| thumb thank you.
|
| "Consider a rest day" - consider? None of my job, spouse,
| equipment or body is too keen on doing that again for a while.
| mdp2021 wrote:
| > _In 2019, Chollet created the Abstraction and Reasoning Corpus
| for Artificial General Intelligence, or ARC-AGI--an exam designed
| to show the gulf between AI models' memorized answers and the
| "fluid intelligence" that people have_
|
| There are a number of skill signals we demand from an
| intelligence.
|
| Mind you: some of them are achieved - like the ability to
| interpret pronouns (Hinton's "the trophy will not enter the case:
| it's too big" vs "the trophy will not enter the case: it's too
| small").
|
| Others, we meet occasionally when we are not researching said
| requirements systematically: one example is that detective game
| described at https://news.ycombinator.com/item?id=43284420 - a
| simple game of logic that intelligences are required to be able
| to solve (...and yet, again some rebutted that humans would fail
| etc.).
|
| It remains important though that those working modules are not
| clustered (solving specific tasks and remaining unused
| otherwise): they must be intellectual keys adapted into use in
| the most general cases they can be be helpful in. That's
| important in intelligence. So, even the ability to solve
| "revealing" tasks is not enough - the way in which the ability
| works is crucial.
| HenryBemis wrote:
| 1a) it's not AI, it's LLM. The companies who create/train/operate
| them may (wink-wink) pitch them as "AI" with half-truths, but we
| (here) know it's LLMs "all the way down" 1b) just like I
| disliked the "autopilot" in Teslas because it was never
| autopilot. 2) I know that I wanted to write some software
| tools, and I have been successful at this for the past many
| months, and I got top-shelve tools, that work, do their tasks,
| send alerts, etc. etc. And I am not the only one. So if the
| purpose is to "show it's a stupid AI".. well.. it's not AI.. so
| yeah. If the purpose is "it is not perfect", yes, because it
| draws a hand with 10 fingers. What else is new?
|
| LLMs are a tool, still under development, still early in the
| curve, they can do A-B-C well but not X-Y-Z well (or at all).
| Congratulations :)
| whiplash451 wrote:
| Francois Chollet and his work deserve a better title than this
| stupid headline.
|
| Francois is out to push the boundaries of science and help create
| models that are truly more intelligent.
| mdp2021 wrote:
| > _A person who scores 30 percent on ARC-AGI-2 is in no sense
| inferior to someone who scores 90 percent_
|
| "News just in: journalist for the Atlantic stops reasoning and
| drifts in a world of feelings after neural hijacking, as he
| perceives abilities as some kind of threat".
|
| > _Human cognitive diversity [...] when that diversity is already
| so abundant, do you really want to?_
|
| We definitely need intelligence.
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