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Richard Stallman on ChatGPT

• jdthedisciple 11mo ago
  OK take for Nov 2022. Mundane for Dec 2025.
  • imiric 11mo ago
    It's not a "take", but an accurate assessment of "AI" tools.
  • kakacik 11mo ago
    Fine by me even in 2025. If you have a narrow use case that works for you great (and not actively and uncritically promoting it all over internet and HN like many others do). Its a mistake to expect too much from it now though or treat it as some sort of financial cost-cutting panacea. And its a mistake being played right now by millions, spending trillions that may end up in financial crash when reality checks back that will make 2008 crisis look like a children's game.
• haunter 11mo ago
  I use ChatGPT for CLI app commands and it's perfect for that!
  • luqtas 11mo ago
    do you mean something like running a full blown and expensive GPU or relying to have your prompts parsed into a server that often times is draining the water, as well causing power shortages of nearby places (sometimes residential areas) trained on copyright violated data to do something like: "hey chat cd my Downloads folder" instead of "cd Downloads"? or any alias for $often used $stuff?
• eatitraw 11mo ago
  > I call it a "bullshit generator" because it generates output "with indifference to the truth". Seems unnecessary harsh. ChatGPT is a useful tool even if limited. GNU grep also generates output ”with indifference to the truth”. Should I call grep a “bullshit generator” too?
  • csmantle 11mo ago
    > GNU grep also generates output ”with indifference to the truth”. GNU grep respects user arguments and input files to the dot. It is not probabilistic.
    • kubafu 11mo ago
      Also GNU grep doesn't claim to be intelligent.
      • xorcist 11mo ago
        Now you tell me!
  • Rygian 11mo ago
    GNU grep operates an algorithm, and provides output which is truthful to that algorithm (if not, it's a bug). An LLM operates a probabilistic process, and provides output which is statistically aligned with a model. Given an input sufficiently different from the training samples, the output is going to be wildly off of any intended result. There is no algorithm.
    • deleted 11mo ago
      [deleted]
    • oulipo2 11mo ago
      It is an algorithm... just a probabilistic one. And that's widely used in many domains (communications, scientific research, etc)
    • IanCal 11mo ago
      Of course there's an algorithm! What nonsense is this that we're saying things with probability used somewhere inside them are no longer algorithms?
      • Rygian 11mo ago
• gwd 11mo ago
  > ChatGPT is not "intelligence", so please don't call it "AI". I define "intelligence" as being capable of knowing or understanding, at least within some domain. Great -- another "submarines can't swim" person. [EDIT2: Apparently this is not his position, although it's only clear in a different page he links to. See below.] By this definition nothing is AI. Quite an ignorant stance for someone who used to work at an AI laboratory. ETA: > Please join me in spreading the word that people should not trust systems that mindlessly play with words to be correct in what those words mean. Please join me in spreading the counterargument to this: The best way to predict a physical system is to have an accurate model of a physical system; the best way to predict what a human would write next is to have a model of the human mind. "They work by predicting the next word" does not prove that they are not thinking. EDIT2, con't: So, he clarifies his stance elsewhere [1]. His position appears to be: 1. Systems -- including both "classical AI" systems like chess and machine learning / deep learning systems -- can be said to have semantic understanding, even if they are not 100% correct, if there has been some effort to "validate" the output: to correlate it to reality. 2. ChatGPT and other LLMs have had no effort to validate their output 3. Therefore, ChatGPT and other LLMs have no semantic understanding. #2 is not stated so explicitly. However, he actually goes into quite a bit of detail to emphasize the validation part in #1, going so far as to describe completely inaccurate systems still count as "attempted artificial intelligence" because they "purport to understand". So the only way #3 makes any sense is for #2 to be presented as stated. And, #2 is simply and clearly false. All the AI labs go to great lengths to increase the correlation between the output of their AI and the truth ("reduce hallucination"); and have been making steady progress. So to state it forwards: 1. According to [1], a system's output can reflect "real knowledge" and a "semantic understanding" -- and thus qualify as "AI" -- if someone "validate[s] the system by comparing its judgment against [ground truth]". 2. ChatGPT, Claude, and others have had significant effort put into them to validate them against ground truth. 3. So, ChatGPT has semantic understanding, and is thus AI. [1] https://www.gnu.org/philosophy/words-to-avoid.html#ArtificialIntelligence https://www.gnu.org/philosophy/words-to-avoid.html#Artificia...
  • conartist6 11mo ago
    > Can submarines swim? There at least is not a large contingent of people going around trying to say there is no such thing as swimming beyond what submarines can do...
• j-pb 11mo ago
  Old man yells at cloud.
  • croes 11mo ago
    Ageism
    • criddell 11mo ago
      And nephophobic.
  • zaphirplane 11mo ago
    That dismissal hardly buys credibility
    • yxhuvud 11mo ago
      Neither did the dismissal of AI in the article. I'd classify it as "not even wrong" in that the factual parts are true, but the conclusions are utter nonsense as ChatGPT can be extremely useful regardless of the claims being true.
  • fooker 11mo ago
    > The only way to use it is by talking to a server which keeps users at arm's length. Old man yells at cloud _computing_
  • Synaesthesia 11mo ago
    Cloud computing, that is
• Synaesthesia 11mo ago
  He's not wrong. It's not intelligence. It's a simulacrum of intelligence. It can be useful but ought to not be trusted completely. And it's certainly not a boon for freedom and openness.
  • tigrezno 11mo ago
    So can we consider "intelligence" when that simulacrum is orders of magnitude stronger?
    • Synaesthesia 11mo ago
      It's brilliant at recapitulating the daya it's trained on. It can be extremely useful. But it's still nowhere close the capability of the human brain, not that I expect it to be. Don't get me wrong I think they are remarkable but I still prefer to call it LLM rather than AI.
      • criddell 11mo ago
        Is intelligence a binary thing? A dog's cognitive capabilities are nowhere near human level. Is a dog intelligent?
        • Aloisius 11mo ago
          Some of the things we consider prerequisites of general intelligence (what we usually mean by when we talk about intelligence in these contexts) - like creativity or actual reasoning, are not present at all in LLMs. An LLM is a very clever implementation of autocomplete. The truly vast amount of information we've fed it provides a wealth of material to search against, the language abstraction allows for autocompleting at a semantic level and we've add enough randomness to allow some variation in responses, but it is still autocomplete. Anyone who has used an LLM enough in an uncommon domain they are very familiar with has no doubt seen evidence of the machine behind the curtain from faulty "reasoning" where it sometimes just plays madlibs to a complete lack of actual creativity.
• am17an 11mo ago
  All a LLM does is hallucinate, some hallucinations are useful. -someone on the internet
  • JKCalhoun 11mo ago
    Agree. I confess to having hallucinated through a good portion of my life though (not medicinally, mind you).
  • fluidcruft 11mo ago
    Boy are we going to have egg on our faces when we finally all agree that consciousness and qialia are nothing but hallucinations.
• zoobab 11mo ago
  He is right, once again.
• dkyc 11mo ago
  Absolutely hilarious that he has a "What's bad about" section as a main navigation, very self-aware.
  • fooker 11mo ago
    Self aware would be be having the "What's bad about ->" {Richard Stallman, GPL, GNU, Emacs} entries.
  • poisonborz 11mo ago
    "Posting on Reddit requires running nonfree JavaScript code." I have much respect for him but this is at the level of old-man-shouting-at. Criticism should be more targeted and not just rehashing the same arguments, even if true.
    • mvid 11mo ago
      Well, in the Reddit case, they used to have APIs you could build free, OSS clients against, and specifically removed it
      • mvdtnz 11mo ago
        Even with a perfectly free client you still need to perform computation on a remote machine that's outside of your control to post on (or read) reddit. Which is the same violation he moans about in this article, > Doing your own computing via software running on someone else's server inherently trashes your computing freedom. As always with Stallman he is dogmatic well past the point of reasonableness.
        • computably 11mo ago
          reddit was also open source at one point, so at least in theory anybody could run their own copy. I agree Stallman is far from reasonable but AFAIK he's consistent with his unreasonable standards.
        • saltwatercowboy 11mo ago
• rckt 11mo ago
  That's what I'm thinking every time I hear or have to use the term "AI". It is not intelligent, but everyone is so used to call it so. LLM is much better.
• IanCal 11mo ago
  Extremely lazy take. > ChatGPT is not "intelligence", so please don't call it "AI". Totally ignoring the history of the field. > ChatGPT cannot know or understand anything Ignoring large and varied debates as to what these words mean. From the link about bullshit generators > There are systems which use machine learning to recognize specific important patterns in data. Their output can reflect real knowledge (even if not with perfect accuracy)—for instance, whether an image of tissue from an organism shows a certain medical condition, whether an insect is a bee-eating Asian hornet, whether a toddler may be at risk of becoming autistic, or how well a certain art work matches some artist's style and habits. Scientists validate the system by comparing its judgment against experimental tests. That justifies referring to these systems as “artificial intelligence.” Feels absurd to say LLMs don't learn patterns in data and that the output of them hasn't been compared experimentally. We've seen this take a thousand times and it doesn't get more interesting to hear it again.
  • bjourne 11mo ago
    > Totally ignoring the history of the field. What does that mean? "Others have called such tools AI" is argumentum ad populum and a fallacious argument. > Ignoring large and varied debates as to what these words mean. Lacking evidence of ChatGPT knowing or understanding things, that is the null hypothesis.
  • saltwatercowboy 11mo ago
    > Extremely lazy take. He's famously a curmudgeon, not lazy. How would you expect him to respond? > Totally ignoring the history of the field. This criticism is so vague it becomes meaningless. No-one can respond to it because we don't know what you're citing exactly, but you're obviously right that the field is broad, older than most realise, and well-developed philosophically. > Ignoring large and varied debates as to what these words mean. Stallman's wider point (and I think it's safe to say this, considering it's one that he's been making for 40+ years) would be that debating the epistemology of closed-source flagship models is fruitless because... they're closed source. Whether or not he's correct on the epistemology of LLMs is another discussion. I agree with him. They're language models, explicitly, and embracing them without skepticism in your work is more or less a form of gambling. Their undeniable usefulness in some scenarios is more an indictment of the drudgery and simplicity of many people's work in a service economy than conclusive evidence of 'reasoning' ability. We are the only categorically self-aware & sapient intelligence, insofar as we can prove that we think and reason (and I don't think I need to cite this).
• brainless 11mo ago
  I prefer using LLM. But many people will ask what is an LLM and then I use AI and they get it. Unfortunate. At the same time, LLMs are not a bullshit generator. They do not know the meaning of what they generate but the output is important to us. It is like saying a cooker knows the egg is being boiled. I care about the egg, cooker can do its job without knowing what an egg is. Still very valuable. Totally agree with the platform approach. More models should be available to be run own own hardware. At least 3rd party cloud provider hardware. But Chinese models have dominated this now. ChatGPT may not last long unless they figure out something, given the "code red" situation is already in their company.
  • mort96 11mo ago
    Frankly, bullshit is the perfect term for it because ChatGPT doesn't know that it's wrong. A bullshit artist isn't someone whose primary goal is to lie. A bullshit artist is someone whose primary goal is to achieve something (a sale, impressing someone, appearing knowledgable, whatever) without regard for the truth. The act of bullshitting isn't the same as the act of lying. You can e.g bullshit your way through a conversation on a technical topic you know nothing about and be correct by happenstance.
    • card_zero 11mo ago
      I guess it's a fair point that slop has its own unique flavor, like eggs.
    • lifthrasiir 11mo ago
      That interpretation is too generous, the word "bullshit" is generally a value judgement and implies that you are almost always wrong, even though you might be correct from time to time. Current LLMs are way past that threshold, making them much more dangerous for a certain group of people.
    • rvz 11mo ago
      Before someone replies and does a fallacious comparison along the lines like: "But humans also do 'bullshitting' as well, humans also 'hallucinate' just like LLMs do". Except that LLMs have no mechanism for transparent reasoning and also have no idea about what they don't know and will go to great lengths to generate fake citations to convince you that it is correct.
• woolion 11mo ago
  I have most sympathy for the ideals of free software, but I don't think prominently displaying "What's bad about:", include ChatGPT, and not make a modicum of effort to sketch out a basic argument, is making any service to anyone. It's barely worth a tweet, which would excuse it as a random blurb of barely coherent thought spurred by the moment. There are a number of serious problems with LLMs; the very poor analogies with neurobiology and anthropomorphization do poison the public discourse to a point where most arguments don't even mean anything. The article itself present LLMs as bullshitters, which is clearly another anthropomorphization, so I don't see how this really addresses these problems. Whats bad about: RMS Not making a decent argument make your position look unserious The objection that is generally made to RMS is that he is 'radically' pro-freedom rather than be willing to compromise to get 'better results'. This is something that makes sense, and that he is a beacon for. It seems such argument weaken even this perspective.
• kelzier15 11mo ago
  It simplifies a lot of analysis and critical thinking job for me so say what you want I call it "intelligence".
  • necovek 11mo ago
    There is a huge risk with that type of usage: for some percent of cases (unknown how much, though), it will get things seriously wrong. You'll be accountable for the decision though. This is what RMS is flagging, though not very substantiated.
• necovek 11mo ago
  This seems to be a complaint against general use of "Artificial Intelligence" term: none of it is "real intelligence" as we don't really have a definition for that.
  • IanCal 11mo ago
    No, he calls other much simpler things AI.
• yxhuvud 11mo ago
  The assertive jump from it not understanding to it being not worth using is pretty big. Things can also be useful without having trust in them.
• swatson741 11mo ago
  I never really considered this too deeply, because I've never studied "Agentic AI" before (except for natural language processing). Stallman is making a really good point. ChatGPT doesn't solve the intelligence problem. If ChatGPT was actually able to do that it would be able to make ChatGPT 2.0 on request.
  • TheDong 11mo ago
    I guess that proves that there are zero intelligent beings on the planet since if humans were intelligent, they would be able to make ChatGPT 2.0 on request. What you're talking about is "The Singularity", where a computer is so powerful it can self-advance unassisted until the entire planet is paperclips. There is no one claiming that ChatGPT has reached or surpassed that point. Human-like intelligence is a much lower bar. It's easy to find arguments that ChatGPT doesn't show it (mainly it being incapable of learning actively, and with there being many ways to show it doesn't really understand what it's saying either), but a Human cannot create ChatGPT 2.0 on request, so it follows to reason a human-like intelligence doesn't necessarily have to be able to do so either.
  • IanCal 11mo ago
    You're assigning something different to his argument. Here's from the linked page > There are systems which use machine learning to recognize specific important patterns in data. Their output can reflect real knowledge (even if not with perfect accuracy)—for instance, whether an image of tissue from an organism shows a certain medical condition, whether an insect is a bee-eating Asian hornet, whether a toddler may be at risk of becoming autistic, or how well a certain art work matches some artist's style and habits. Scientists validate the system by comparing its judgment against experimental tests. That justifies referring to these systems as “artificial intelligence.” This is nowhere near arguing that it should be able to make new versions of itself.
• fooker 11mo ago
  > ChatGPT cannot know or understand anything, so it is not intelligence. It does not know what its output means. It has no idea that words can mean anything. This argument does a great job anthropomorphizing ChatGPT while trying to discredit it. The part of this rant I agree with is "Doing your own computing via software running on someone else's server inherently trashes your computing freedom." It's sad that these AI advancements are being largely made on software you can not easily run or develop on your own.
• zetacrushagent 11mo ago
  At ZetaCrush (zetacrush.com) we have seen benchmark results that align with Richard's view. For many of our benchmark tests, all leading models score 0/100
  • deleted 11mo ago
    [deleted]
• thw_9a83c 11mo ago
  I think we can call LLMs artificial intelligence. They don't represent real intelligence. LLMs lack real-life experience, and so they cannot verify any information or claim by experiencing it with their own senses. However, "artificial intelligence" is a good name. Just as artificial grass is not real grass, it still makes sense to include "grass" in its name.
  • foxrider 11mo ago
    I'm going to get pedantic. That's why I've been using this exact term "LLM" instead of "AI" for the last few years. The "artificial" implies that it has man-made origins, yet serves the *same* purpose. Richard here argues that it doesn't fit because it does not, in fact, serve the same purpose. You can argue that for certain people it can serve the same purpose, like if you're a CEO who replaces low-level support staff with chatbots - but then it's an artificial support staff, not artificial intelligence.
    • thw_9a83c 11mo ago
      > ...it doesn't fit because it does not, in fact, serve the same purpose. For many people and purposes, it does indeed serve the same purpose. I use it all the time for coding, which is still very tricky, and for writing emails. For writing emails in particular, it is already a life-changing technology. I have always wanted my own secretary to dictate and finalize various letters. But, for some reason, companies don't provide secretaries anymore. Now, I can finally have an LLM instead. I guess there's no discussion that a good secretary must have always been quite intelligent.
  • gaganyaan 11mo ago
    LLMs lack real-life experience in the same way that humans lack real-quark experience. Does only direct sensory input count, or does experience mediated through tools count? How much do you or I really know about the Oort Cloud?
• benrapscallion 11mo ago
  This should be a badge of honor, a rite of passage for companies: when they become big and important enough for humanity, RMS will write a negative <company>.html page on his website.
• deleted 11mo ago
  [deleted]
• cheschire 11mo ago
  What the world calls an LLM is just a call-and-response architecture. In the labs they’ve surely just turned them on full time to see what would happen. It must have looked like intelligence when it was allowed to run unbounded. Separate the product from the technology and the tech starts to get a lot closer to looking intelligent.
• yread 11mo ago
  LLM is a model. So, it fits under "all models are wrong, some are useful". Of course, it can produce wrong results. But it can also help with mechanistic tasks. And you can run some models locally. What does he think of open-weight models - there is no source code to be published. Closest thing - the training data - needs so many resources to turn into weights that it's next to useless.
• acituan 11mo ago
  Unfortunate that he starts with the thinking argument because it will be nitpicked to death, while bullshit and computing freedom arguments are much stronger and to me personally irrefutably true. For those who will take “bullshit” as an argument of taste I strongly suggest taking a look at the referenced work and ultimately Frankfurt’s, to see that this is actually a pretty technical one. It is not merely the systems’ own disregard to truth but also its making the user care about the truthiness less, in the name of rhetoric and information ergonomics. It is akin to the sophists, except in this case chatbots couldn’t be non-sophists even they “wanted” to because they can only mimic relevance, and the political goal they seem to “care” about is merely making other use them more - for the time being. Computing freedom argument likewise feels deceptively about taste but I believe harsh material consequences are yet to be experienced widely. For example I was experiencing a regression I can swear to be deliberate on gemini-3 coding capabilities after an initial launch boost, but I realized if someone went “citation needed” there is absolutely no way for me to prove this. It is not even a matter of having versioning information or output non-determinism, it could even degrade its own performance deterministically based on input - benchmark tests vs a tech reporter’s account vs its own slop from a week past from a nobody-like-me’s account - there is absolutely no way for me to know it nor make it known. It is a right I waived away the moment I clicked “AI can be wrong” TOS. Regardless of how much money I invest I can’t even buy a guarantee on the degree of average aggregate wrongness it will keep performing at, or even knowledge thereof, while being fully accountable for the consequences. Regression to depending on closed-everything mainframes is not a computing model I want to be in yet cannot seem to escape due to competitive or organizational pressures.
  • duskdozer 11mo ago
    >For example I was experiencing a regression I can swear to be deliberate on gemini-3 coding capabilities after an initial launch boost Can you describe what you mean by this more? Like you think there was some kind of canned override put in to add a regression to its response to whatever your input was? genuine question
    • acituan 11mo ago
      It is a black box. We don’t know what happens on the other side of the RPC call; good and bad, therefore it could be any number of knobs. User has two knobs called the thinking level and the model. So we know there are definitely per call knobs. Who can tell if thinking-high actually has a server side fork into eg thinking-high-sports-mode versus thinking-high-eco-mode for example. Or if there were two slightly different instantiations of pro models, one with cheaper inference due to whatever hyperparameter versus full on expensive inference. There are infinite ways to implement this. Zero ways to be proven by the end user.
• torginus 11mo ago
  If we define intelligence as the ability to understand an unfamiliar phenomenon, create a mental model of it, these models are not intelligent (at least at inference time), as they cannot update their own weights. I'm not sure if these models are trained using unsupervised learning and are capable of training themselves to some degree, but even if so, the learning process of gradient descent is very inefficient, so by the commonly understood definition of intelligence (the ability to figure out and unfamiliar situation), the intelligence of an inference only model is zero. Models that do test time training might be intelligent to some degree, but I wager their current intelligence is marginal at best.
  • IanCal 11mo ago
    Intelligence and learning really seem distinct. Does someone who only has a short term memory no longer count as intelligent? But also he does count much simpler systems as AI, so it's not about learning on the fly or being anything like human intelligence.
    • hulitu 11mo ago
      > Intelligence and learning really seem distinct. Yeah. Some people are intelligent, but never learn. /s
    • torginus 11mo ago
      An AI/person that can solve novel problems (either by teaching it or not) is a more general kind of intelligence than one that can not. It's a a qualitatively better intelligence. An intelligence that can solve problems that fall into its training set better is quantitatively better. Likewise, an intelligence that learns faster is quantitatively better. To give a concrete and simple example, take a simple network trained to recognized digits. The network can be of arbitrary quality, it can be robust or not, fast or slow, but it can't do more than digits. Another NN that can learn to recognize more symbols is a more general kind of AI, which again introduces another set of qualitative measures, namely how much training does it need to learn a new symbol robustly. 'Intelligence' is a somewhat vague term as any of the previous measures I've defined could be called intelligence (training accuracy, learning speed, inference speed etc., coverage of training set). You could claim a narrower kind of intelligence that exists without learning (which is what ChatGPT is and what you gave as example with the person that only has a short-term memory) is still intelligence, but then we are arguing semantics. Inference only LLMs are clearly missing something and are lacking in generality.
• TheOtherHobbes 11mo ago
  The quality and usefulness of the service across different domains, the way it's being rolled out by management, the strategy of building many data centres when this makes questionable sense, the broader social and psychological effects, the stock market precarity around it, the support among LLMs for open source code and weights, and the applicability of the word "intelligence" are all different questions. This reads like more a petulant rant than a cogent and insightful analysis of those issues.
  • horsawlarway 11mo ago
    While I mostly agree with you. I still think he's pretty spot on about the risks of depending on a tool you can't run locally.
• torginus 11mo ago
  Considering Stallman worked in the MIT AI lab in the era of symbolic AI, and has written GCC (an optimizing compiler is a kind of symbolic reasoner imo), I think he has a deeper understanding of the question than most famous people in tech.
  • pupppet 11mo ago
    Or he’s just shaking his fist at the clouds again.
    • spongebobism 11mo ago
      why not both?
  • german_dong 11mo ago
    No, modern approaches have zero overlap with Chomsky's deterministic methodology..
    • keybored 11mo ago
      What does Chomsky’s work have to do with AI?
      • xdavidliu 11mo ago
        yeah, your parent comment appears to be just nonsensical name-dropping. happens a lot here. A different type of comment is the "X is just Y" comment that is kind of annoying, like "all of AI is just curve fitting", which the commenter wants readers to think is some kind of profound insight.
      • keeda 11mo ago
        My understanding is prior work in NLP and symbolic AI was strongly influenced by Chomsky's philosophies, e.g. https://chomsky.info/20121101/ https://chomsky.info/20121101/ and https://norvig.com/chomsky.html https://norvig.com/chomsky.html
      • 11mo ago
• HPsquared 11mo ago
  Artificial grass is not grass, but we can use it for similar things.
• zkmon 11mo ago
  > being capable of knowing or understanding. The definitions of "knowing" and "understanding" are being challenged. Also, it's no longer possible to not have a dependency on other opaque softwares.
• classified 11mo ago
  > ChatGPT is not "intelligence", so please don't call it "AI". I've been saying the same thing for years, but it's utterly hopeless by now. Even the critics use that ludicrous "AI" moniker.
• german_dong 11mo ago
  Why do we continue to give increased weight to a guy whose computer science knowledge ends in 1980? Dude became an icon by preaching a rationalization to an all-too-receptive generation of cash poor college students: that Microsoft should work pro bono. Odd that he didn't extrapolate that principle to other "secret sauce" enterprises like Coca Cola or KFC.
  • Gud 11mo ago
    When did he say Microsoft should work pro bono? I would say his main argument is that you should not use closed source software. I am not a cash poor college student, I share his philosophy, hence why I try to use Free Software when I have the choice. Unlike Stallman, I am not a zealot.
    • burnt-resistor 11mo ago
      > I would say his main argument is that you should not use closed source software. His argument/s go beyond that and {wa,i}s actively hostile towards uses and interoperability if code, other libraries, systems, services, and users aren't pure enough. That was the progression from GPL2 -(TiVo)-> GPL3 -(Mandriva )-> AGPL with ever more extremism and less choice and usability. There's no need for anyone to mirror his entire philosophy or much of it, but that some bits can be replaced by the choice of the user/developer without being forced by others into another walled garden (licensing tainting) that claims to be "free". YMMV. Take with a grain of salt. Ask your doctor if licensing is right for you. :peace:
  • armchairhacker 11mo ago
    Physical goods like Coca Cola require raw material. Software costs effort to make, but then practically nothing to copy, hence making it “free” is more feasible. We just have to figure out how to pay the creators before it’s released, and ensure they have a decent path to continued funding after (e.g. other projects to create).
    • german_dong 11mo ago
• morgengold 11mo ago
  What's so difficult to understand about LLMs? The meaning of intelligence is irrelevant. I like to call LLMs Associators. No they don't think and they don't understand. But it turns out by finding patterns and associate them on the basis of symbolic language, there comes up some useful stuff in some areas.
  • JeremyNT 11mo ago
    I really like this framing. A lot of people mysticise them or want to quibble about intelligence, and then we end up out in the "no true Scottsman" weeds. We know what they are, how they work, what they generate. Let them be their own thing, do not anthropomorphize them. The thing we don't understand so well is humans, really.
• dada216 11mo ago
  The question about what sort of "cognition" is going on in the simulated neural network of the current LLMs is an open one. Saying "it's not intelligence" it's the wrong framing. also, there are fully open LLM, including the training data. He was right on a number of things, very important ones, but he's loosing it with old age, as we all will.
• yomismoaqui 11mo ago
  > I call it a "bullshit generator" because it generates output "with indifference to the truth". Sure we humans don't do this... right?
• spongebobism 11mo ago
  There are some semantic debates going on in this thread about the term "bullshit". But there is a clear definition. The paper Stallman links to uses bullshit in the Frankfurtian sense, that is, talk without care for the truth: > The liar, Frankfurt holds, knows and cares about the truth, but deliberately sets out to mislead instead of telling the truth. The "bullshitter", on the other hand, does not care about the truth and is only seeking "to manipulate the opinions and the attitudes of those to whom they speak"[0] [0] https://en.wikipedia.org/wiki/Bullshit#Harry_Frankfurt's_concept https://en.wikipedia.org/wiki/Bullshit#Harry_Frankfurt's_con...
  • gaganyaan 11mo ago
    What is truth, but defined by our sensory input? LLMs care about truth insofar as truth exists in their sensory input. They also can be massaged for financial incentives when controlled by private corporations, which can result in prioritizing things other than truth, much like humans.
• davidguetta 11mo ago
  This is an idiotic take "I call it a "bullshit generator" because it generates output "with indifference to the truth"." yeah, no. the point of post-training with RL is precisely to get the truth on many tasks. Many of the answers in post training are judged on whether they are true or not, not just "RLHF / human preference". Also it's not like human are perfectly commited to truth always itself and we don't question their overall innate "intelligence" sense. Stallman just doesn't know what post-training is
• mabedan 11mo ago
  It’s a little like saying calculators cannot do math because they don’t really understand numbers or arithmetic and they just do bit operations. I understand the sentiment, but reality is that it does with words pretty much what you’d expect a person to do. It lacks some fundamentals like creativity and that’s why it’s not doing real problem solving tasks, but it’s well capable of doing mundane tasks that the average person gets paid to do. And when it comes to trust and accuracy, if I ask it a question about German tax system, it will look up sources and may give an answer with an inaccuracy or two but it will for sure be more accurate than whatever I will be able to do after two hours of research.
  • m463 11mo ago
    > calculators cannot do math because they don’t really understand numbers I don't think that's an appropriate analogy at all. He's not saying that AI is not useful. He's saying that it doesn't understand or reason, so it does not generate the truth. So you can ask chatgpt a question about the german tax system, but it would be a mistake to have it do your taxes. in the same way, a calculator could help with your taxes, because it has been engineered to give precise answers about some math operations, but it cannot do your taxes.
    • mabedan 11mo ago
      > so it does not generate the truth. It’s equally true for humans, benchmarks of intelligence. Most shortcomings in our working life is from miscommunications and misunderstanding requirements, and then simply by incompetence and making trivial mistakes.
• engintl 11mo ago
  https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room
• pks016 11mo ago
  To me LLMs are glorified google search. I use them like that.
• deleted 11mo ago
  [deleted]
• bcoughlan 11mo ago
  Perhaps we are putting human intelligence on a pedestal. I have often caught myself having a supposedly original idea only to find I inherited it from something I read months before. We are mostly autocomplete with a mild capacity to synthesise new ideas. It’s the network effect of communicating that creates the feedback loops which amplify our collective intelligence. Also if you think intelligent life has to have a regard for truth, not just a regard for self, tune in to any news channel.