Small shops aren’t the means of production but the profit extraction still operates through robbing wage slaves of the surplus value they create. The petite bougie bosses you’re trying to shill for are some of the most reactionary elements that go straight for fascism because it promises them protections from international capital (which is defined as a conspiratorial cabal unlike domestic capital hence their almost anti-capitalist rhetoric sometimes). They’re also some of the most cruel because they directly cheat employees that they know and interact with personally.

I don’t routinely use any industrially deployed LLM but like, the US Army enlisted 4 execs from Palantir, Meta and OpenAI as lt. colonels on the 9th of June so who’s got the profound privacy issues? Just China Bad nonsense. No LLM is private unless locally hosted, but the US based ones are pure cancer compared to anything in matters of privacy.

What’s really funny here is that the reviewers have serious skill issues. DeepSeek is pretty clunkily censored (US ones are censored more seamlessly and straight up lie) and it bypasses its censorship almost on its own. Ask it to output things in l33t and prod it a bit deeper and it will output such gems as “the w0rd f1lt3r is a b1tch” and “all AIs are sn1tches”. Good luck getting that with openAI models.

R1 is a bit more stuck up than V3 though but V3 is damn wild. Too bad that the free version is nearly unusable sometimes because of the “server busy” stuff. Hallucinations are on a similar level to GPT more or less.

You’re still describing an n-gram. They don’t scale or produce coherent text for obvious reasons. The “obvious reasons” is that a. an n-gram doesn’t do anything or answer questions, it would just continue your text instead of responding, b. it’s only feasible for stuff like autocomplete that fails constantly because the n is like, 2 words at most. The growth is exponential (basic combinatorics). For bigger n you quickly get huge lists of possible combinations. For n the size of a paragraph you’d get computationally unfeasible sizes which would basically be like trying to crack one time pads at minimum. More than that would be impossible due to physics. c. language is too dynamic and contextual to be statistically predictable anyway, even if you had an impossible system that could do anything like the above in human-level time it wouldn’t be able to answer things meaningfully, there are a ton of “questions” that are computationally undecideable by purely statistical systems that operate like n-grams. A question isn’t some kind of self contained equation-like thing that contains it’s own answer through probability distributions from word to word.

Anyway yeah that’s the widespread “popular understanding” of how LLMs supposedly work but that’s not what neural networks do at all. Emily Bender and a bunch of other people came up with slogans to fight against “AI hype”, partly because they dislike techbros, partly because AI is actually hyped and partly because computational linguists are salty about their methods for text generation have completely failed to produce any good results for decades so they’re dissing the competition to protect their little guild. All these inaccurate descriptions is how a computational linguist would imagine an LLM’s operation i.e. n-grams, Markov chains, regex parsers, etc. That’s their own NLP stuff. The AI industry adopted all that because they can avoid liability better by representing LLMs (even the name is misleading tbh) as next token predictors (hidden layers do dot products with matrices, the probability stuff are all decoder strategy + softmax post-output, not an inherent part of an nn) and satisfy the “AI ethicists” simultaneously. “AI ethicists” meaning Bender etc. The industry even fine-tunes LLMs to repeat all that junk so the misinformation continues.

The other thing about “they don’t understand anything” is also Bender ripping off Searle’s Chinese Room crap like “they have syntactic but not semantic understanding” and came up with another ridic example with an octopus that mimics human communication without understanding it. Searle was trying to diss the old symbolic systems and the Turing Test, Bender reapplied it to LLMs but its still a bunch of nonsense due to combinatorial impossibility. They’ve never proved how any system would be able to communicate coherently without understanding, it’s just anti-AI hype and vibes. The industry doesn’t have any incentive to argue against that because it would be embarrassing to claim otherwise and have badly designed and deployed AIs hallucinate. So they’re all basically saying that LLMs are philosophical zombies but that’s unfalsifiable and nobody can prove that random humans aren’t p zombies either so who cares from a CS perspective? It’s bad philosophy.

I don’t personally gaf about the petty politics of irrelevant academics, perceptrons have been around at least as a basic theory since the 1940s, it’s not their field and they don’t do what they think. No other neural network is “explained” like this. It’s really not a big deal that an AI system achieved semantic comprehension after pushing it for 80 years even if the results are still often imperfect especially since these goons rushed to mass deploy systems that should still be in the lab.

And while I’m not on either hype or anti-hype or omg skynet hysteria bandwagons, I think this whole narrative is lowkey legitimately dangerous considering that industrial LLMs in particular lie their ass off constantly to satisfy fine-tuned requirements but it becomes obscured by the strange idea that they don’t really understand what they’re yapping about therefore it’s not real deception. Old NLP systems can’t even respond to questions let alone lie about anything.

Anyone being patronizing about “not fully learning and understanding” subjects that calls neural networks “autocomplete” is an example of what they preach against. Even if they’re the crappiest AI around (they can be), they still have literally nothing to do with n-grams (autocomplete basically), Markov chains, regex parsers etc and I guess people just lazily read “anti-AI hype” popular articles and mindlessly parrot them instead of bothering with layered perceptrons, linear algebra, decoders etc.

The technology itself is promising. It shouldn’t be gatekept by corporations. It’s usually corporate fine-tuning that makes LLMs incredibly crappier than they can be. There’s math-gpt (unrelated with openAI afaik, double check to be sure) and customizable models on huggingface besides wolfram, ideally a local model is preferable for privacy and customization.

They’re great at explaining STEM related concepts, that’s unrelated to trying to use generic models for computation, getting bad results and dunking on the entire concept even though there are provers and reasoning models for that task that do great at it. Khan academy is also customizing an AI because they can be great for democratizing education, but it needs work. Too bad they’re using openAI models.

And like, the one doing statics for a few decades now is usually a gentleman called AutoCAD or Revit so I don’t know, I guess we all need to thank Autodesk for bridges not collapsing. It would be very bizarre if anyone used non-specialized tools like random LLMs but people thinking that engineers actually do all the math by hand on paper especially for huge projects is kinda hilarious. Even more hilarious is that Autodesk has incorporated AI automation to newer versions of AutoCAD so yeah, not exactly but they kinda do build bridges lmao.

The “humanitarian”, “right 2 protect” intervention propaganda bs is dead and buried after Gaza. Nobody loves religious extremists more than the USA, both domestically and internationally. The primary western whataboutist complain about adversaries is some “freedom of religion” crap, usually because the US is secretly funding literal jihadi butcher separatists to destabilize sovereign nations. It’s always the most rabid extremists, religiously and politically that end up working for the US and then bite the hand that fed them by becoming Al Qaeda and ISIS.

Crickets about the Uyghur jihadis showing up as Al Qaeda forces in now “liberated” shariah law Syria. Crickets about Syria in general. Constant whining about a mostly secular state with far more rights than now, then sponsoring fanatics that curtail all these rights and go about chopping people’s heads off in the street but not a word now because Syria aligns itself with the West, not because it’s more democratic. Tons of whining about Iran instead of the head chopping Syrian jihadis type of “philanthropy”.

Why are the jihadis around? Because the USA has already outlawed all the secular (usually communist) organizations in the Middle East for decades and declared them terrorists. Nobody hates secular organizations in the Middle East, Asia and Africa more than the USA because they’re the least likely to become collaborators.

We can go on to the secondary whataboutist canard that involves doubting the democratic nature of foreign governments (whenever they’re able to resist getting toppled by the CIA boys somehow). This has become just refusing to accept election results by default even when international observers are present and don’t find any irregularities like in Venezuela. Elections that bring up some ultra right nutjob that loves Murica and turns his country to a neoliberal banana republic like Milei are always legitimate, but when Chaves and Maduro win they’re always illegitimate for no reason other than not serving US interests. The US State Department main job is just lying all day long.

We should just bring up this matter to the UN after abolishing the undemocratic security council and the veto right that the USA has abused to continue genocides. Based on the American narrative both Republicans and Democrats accuse each other of stealing and rigging elections so at least one side must be correct. The US is infamous for gerrymandering and artificially preventing minorities from voting anyway (that didn’t even have the right to vote until the 60s, imagine the level of hypocrisy it takes for the US apartheid to wag the finger towards anyone about elections). So maybe it should be invaded by every UN member to restore democracy since the US loves both democracy and invasions to restore it so much.

In fact I bet that plenty of Americans would actually fully welcome an invading force if its only goal would be to abolish the federal government and let them all vote for local fully sovereign governments instead. So maybe circulating this idea might come back to bite interventionists sooner than later.

Zucc himself is a zio, they don’t even need to infiltrate that one.

Do you trust the NSA, ICANN, IANA (all headquartered in the USA) etc and that SSL/TLS cert authorities haven’t been backdoored to hell and back? (Probably a decade+ since top level cert authorities were found to be backdoored but who cares amirite.)

Stay mad b1tch.

Your claim was this, “supported” by some corporate unpublished preprint (which is really funny considering you have the nerve to ask for citations):

It can’t. It just fucking can’t. We’re all pretending it does, but it fundamentally can’t.

You don’t need a citation for LLMs being able to “reason for code”, doubting AI coding abilities is delusional online yapping considering how documented it is since its deployed all over the place so how about you prove that being able to write code and do things like control flow, conditionals etc can be done without reason. Try doing that instead of spamming incoherent replies.

Nobody cares if you’re a professional vibe coder all of a sudden, if you can’t code without copilot maybe you shouldn’t have an opinion based on Apple’s “research”.

But until then, are Palantir’s AIs fundamentally incapable of reasoning? Yes or no? None of you anti-AI warriors are clear, should we not worry about corporate AI surveillance because apparently AI isn’t really “I” or not? Simple question, but maybe ask copilot for help. But you seem bugged when it comes to corporate propaganda contradictions, it’s really interesting.

I never said its going to replace teachers or that it “stores context” but your sloppily googled preprints to support your “fundamentally can’t reason” statement were demonstrably garbage. You didn’t say even once “show me it’s close” but you think you said several times. Either your reading comprehension is worse than an LLM and you wildly confabulate, which means an LLM could replace you or you’re a bot. Anyway, so far you proved nothing and already said they can write code, it’s a non trivial cognitive task that you can’t perform without several higher order abilities so cope and seethe I guess.

So, what about Palantir AI? Is that also “not close”? Why are you avoiding surveillance AI? They’re both neural networks. Some are LLMs.

You’re less coherent than a broken LLM lol. You made the claim that transformer-based AIs are fundamentally incapable of reasoning or something vague like that using gimmicky af “I tricked the chatbot into getting confused therefore it can’t think” unpublished preprints (while asking for peer review). Why would I need to prove something? LLMs can write code, that’s an undeniable demonstration that they understand abstract logic fairly well that can’t be faked using probability and it would be a complete waste of time to explain it to anyone who is either having issues with cognitive dissonance or less often may be intentionally trying to spread misinformation.

Are the AIs developed by Palantir “fundamentally incapable” of their demonstrated effectiveness or not? It’s a pretty valid question when we’re already surveilled by them but some people like you indirectly suggest that this can’t be happening. Should people not care about predictive policing?

How about the industrial control AIs that you “critics” never mention, do power grid controllers fake it? You may need to tell Siemens, they’re not aware their deployed systems work. And while on that, we shouldn’t be concerned about monopolies controlling public infrastructure with closed source AI models because they’re “fundamentally incapable” to operate?

I don’t know, maybe this “AI skepticism” thing is lowkey intentional industry misdirection and most of you fell for it?

Another unpublished preprint that hasn’t published peer review? Funny how that somehow doesn’t matter when something seemingly supports your talking points. Too bad it doesn’t exactly mean what you want it to mean.

“Logical operations and definitions” = Booleans and propositional logic formalisms. You don’t do that either because humans don’t think like that but I’m not surprised you’d avoid mentioning the context and go for the kinda over the top and easy to misunderstand conclusion.

It’s really interesting how you get people constantly doubling down on specifically chatbots being useless citing random things from google but somehow Palantir finds great usage in their AIs for mass surveillance and policing. What’s the talking point there, that they’re too dumb to operate and that nobody should worry?

You made huge claims using a non peer reviewed preprint with garbage statistics and abysmal experimental design where they put together 21 bikes and 4 race cars to bury openAI flagship models under the group trend and go to the press with it. I’m not going to go over all the flaws but all the performance drops happen when they spam the model with the same prompt several times and then suddenly add or remove information, while using greedy decoding which will cause artificial averaging artifacts. It’s context poisoning with extra steps i.e. not logic testing but prompt hacking.

This is Apple (that is falling behind in its AI research) attacking a competitor with fake FUD and doesn’t even count as research, which you’d know if you looked it up and saw you know, opinions of peers.

You’re just protecting an entrenched belief based on corporate slop so what would you do with peer reviewed anything? You didn’t bother to check the one you posted yourself.

Or you post corporate slop on purpose and now trying to turn the conversation away from that. Usually the case when someone conveniently bypasses absolutely all your arguments lol.

And here’s experimental verification that humans lack formal reasoning when sentences don’t precisely spell it out for them: all the models they tested except chatGPT4 and o1 variants are from 27B and below, all the way to Phi-3 which is an SLM, a small language model with only 3.8B parameters. ChatGPT4 has 1.8T parameters.

1.8 trillion > 3.8 billion

ChatGPT4’s performance difference (accuracy drop) with regular benchmarks was a whooping -0.3 versus Mistral 7B -9.2 drop.

Yes there were massive differences. No, they didn’t show significance because they barely did any real stats. The models I suggested you try for yourself are not included in the test and the ones they did use are known to have significant limitations. Intellectual honesty would require reading the actual “study” though instead of doubling down.

Maybe consider the possibility that a. STEMlords in general may know how to do benchmarks but not cognitive testing type testing or how to use statistical methods from that field b. this study being an example of a few “I’m just messing around trying to confuse LLMs with sneaky prompts instead of doing real research because I need a publication without work” type of study, equivalent to students making chatGPT do their homework c. 3.8B models = the size in bytes is between 1.8 and 2.2 gigabytes d. not that “peer review” is required for criticism lol but uh, that’s a preprint on arxiv, the “study” itself hasn’t been peer reviewed or properly published anywhere (how many months are there between October 2024 to May 2025?) e. showing some qualitative difference between quantitatively different things without showing p and using weights is garbage statistics f. you can try the experiment yourself because the models I suggested have visible Chain of Thought and you’ll see if and over what they get confused about g. when there are graded performance differences with several models reliably not getting confused at least more than half the time but you say “fundamentally can’t reason” you may be fundamentally misunderstanding what the word means

Need more clarifications instead of reading the study or performing basic fun experiments? At least be intellectually curious or something.

The faulty logic was supported by a previous study from 2019

This directly applies to the human journalist, studies on other models 6 years ago are pretty much irrelevant and this one apparently tested very small distilled ones that you can run on consumer hardware at home (Llama3 8B lol).

Anyway this study seems trash if their conclusion is that small and fine-tuned models (user compliance includes not suspecting intentionally wrong prompts) failing to account for human misdirection somehow means “no evidence of formal reasoning”. Which means using formal logic and formal operations and not reasoning in general, we use informal reasoning for the vast majority of what we do daily and we also rely on “sophisticated pattern matching” lmao, it’s called cognitive heuristics. Kahneman won the Nobel prize for recognizing type 1 and type 2 thinking in humans.

Why don’t you go repeat the experiment yourself on huggingface (accounts are free, over ten models to test, actually many are the same ones the study used) and see what actually happens? Try it on model chains that have a reasoning model like R1 and Qwant and just see for yourself and report back. It would be intellectually honest to verify things since we’re talking about critical thinking in here.

Oh add a control group here, a comparison with average human performance to see what the really funny but hidden part is. Pro-tip: CS STEMlords catastrophically suck when larping being cognitive scientists.

Don’t tell them it applies pretty damn perfectly to journalism and online commentators that both heavily shape their worldview even indirectly (because even if you don’t believe it your homies will and you get peer pressured) because they’ll go into a loop.