Is anyone else troubled by experienced devs using terms of cognition around LLMs?
Posted by dancrumb@reddit | ExperiencedDevs | View on Reddit | 398 comments
If you ask most experienced devs how LLMs work, you'll generally get an answer that makes it plain that it's a glorified text generator.
But, I have to say, the frequency with which I the hear or see the same devs talk about the LLM "understanding", "reasoning" or "suggesting" really troubles me.
While I'm fine with metaphorical language, I think it's really dicy to use language that is diametrically opposed to what an LLM is doing and is capable of.
What's worse is that this language comes direct from the purveyors of AI who most definitely understand that this is not what's happening. I get that it's all marketing to get the C Suite jazzed, but still...
I guess I'm just bummed to see smart people being so willing to disconnect their critical thinking skills when AI rears its head
jnwatson@reddit
We've been using cognition terms since way before LLMs came around. "Wait a sec, the computer is thinking". "The database doesn't know this value".
The creation of vocabulary in any new discipline is hard. We use analogies to existing terms to make it easier to remember the words we assign to new concepts. There's no "boot" anywhere when a computer starts up. There's no biological process involved when your laptop goes to "sleep". There's no yarn in the hundreds of "threads" that are running.
Nilpotent_milker@reddit
I feel like a lot of people right now are wanting to redefine what terms mean because of their distaste for the way big tech is marketing LLMs. The most egregious example is 'AI', which has been used to refer to systems far less intelligent than LLMs for decades.
I also feel like saying that LLMs are incapable of reasoning kind of obviously flies in the face of the amazing logical feats that these systems are capable of. Yes, their reasoning is different from human reasoning, and usually it is worse. But I can talk to them about CS or math problems that are not novel in the sense of pushing the boundaries of theory, but certainly were not directly present in the training data and the LLM is often able to extrapolate from what it understands to solve the problem.
I wish the AI companies were more careful with their marketing and that this hadn't become so politicized.
Ignisami@reddit
It’s the difference between academic use of AI, in which case LLM’s absolutely count, and colloquial use of AI, in which case they don’t. OpenAI et al have been working diligently to conflate the two.
m3t4lf0x@reddit
I think LLM’s have shown that most people don’t even know how to define AI, they just have a strong feeling that ,“it’s not this”
IlliterateJedi@reddit
Wikipedia describes AI as "the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making."
Funny enough I didn't think LLMs and reasoning LLMs fell into the AI bucket until literally right now when I read that definition.
DeGuerre@reddit
...which is a strange definition when you think about it.
Tasks that require a lot of "intelligence" for a human to do aren't necessarily the same tasks that require a lot of "intelligence" for a machine. I mean, computers outdo the best humans on memory and mental arithmetic tasks, but nobody has yet built a robot that will clean my bathroom.
In other news, a small forklift can easily out-compete the world's best human weightlifters.
m3t4lf0x@reddit
That’s basically what Alan Turing implied in his paper where he formulated The Turing Test
He says, “can machines think” is the wrong question. Many computational devices can perform tasks that can be described in cognitive terms (ex: even a thermostat)
The better question is whether or not a machine can act in a way that is indistinguishable from another human.
The paper is actually really concise+digestible without extensive CS knowledge and worth a read
DeGuerre@reddit
It's weird that no science fiction author ever caught this, that before we get general intelligence, we might get "Dunning-Kruger systems" that show confidence but incompetence. But they still might be convincing in the same way that a populist politician or a con man is convincing.
johnpeters42@reddit
Most people, you're lucky if they even get that there are different types of AI, as opposed to just different brands of the same type. Those with a clue know that they're mainly thinking about artificial general intelligence, and that LLMs confuse them so much because natural language input and output looks like AGI in a way that e.g. AlphaGo doesn't.
Ok-Yogurt2360@reddit
They also tend to mix up definitions from different scientific fields.
Nilpotent_milker@reddit
What you are calling the academic use of AI has been the academic and industrial use of AI, and the colloquial use of AI pretty much did not exist before ChatGPT brought it into cultural consciousness. I maintain that the predominant phenomenon here is laypeople that have a bone to pick with the technology co-opting the term and redefining it.
noonemustknowmysecre@reddit
Ya know, that probably has something to do with all the AI research and development that has gone on for decades prior to LLMs existing.
You need to accept that search is AI. Ask yourself what level of intelligence an ant has. Is it absolutely none? You'd have to explain how it can do all the things that it does. If it more than zero, then it has some level of intelligence. If we made a computer emulate that level of intelligence, it would be artificial. An artificial intelligence.
(bloody hell, what's with people moving the goalpost the moment we reach the goal?)
SeveralAd6447@reddit
Because that's not how AI was defined by the people who coined the word at Dartmouth in the 1950s. And under their definition, "a system simulating every facet of human learning or intelligence," an AI has never been built.
noonemustknowmysecre@reddit
Neat. Not exactly first to the game, but first to use the exact term "Artificial Intelligence". ....But that sounds like their proposal to hold a conference, not their definition of AI. And you slid "human" in there.
The actual quote: "The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it".
It's down below they simply say "The following are some aspects of the artificial intelligence problem". And then Newell and Simon's addendum say "complex information processing" falls under the heading of artificial intelligence.
Yeah, I'm calling shenanigans. Stay there while I get my broom.
(Ugh, and Minsky was part of it. The dude with the 1969 Perception book that turned off everyone from neural nets. It was even a topic at the Dartmouth conference in 1956. We could have had Tensor Flow in the 80's. He did more damage than Searle and his shitty room.)
SeveralAd6447@reddit
I suppose I did slip "human" in there, but I think if McCarthy didn't use that word, he ought to have, since other animals clearly learn, but the frame of reference for AI is... well, human intelligence. We want a machine that's as smart as we are, not as smart as like, wasps or jellyfish or honey badgers or whatever.
I read that quote as boiling down to this: "[a machine can be made to simulate]... every aspect of learning or any other feature of intelligence." Seems to me like that has been the central goal of AI development all along. It was John McCarthy who said it, and he later replaced it with the far lamer and more tautological definition, "the science and engineering of making intelligent machines, especially intelligent computer programs" (defining "artificial intelligence research" as roughly, "the study of intelligence that's artificial" is very, very silly, but you do you John).
I get why people in ML research have problems with Searle's room, but it's still kind of an important philosophical exercise, and I suspect it is very relevant in cutting edge research like SNNs or Cornell's microwave-based neurochip thing (Very strange doohicky they created: https://news.cornell.edu/stories/2025/08/researchers-build-first-microwave-brain-chip )
noonemustknowmysecre@reddit
Due to the limitations of the technology of our time, you're just going to have to imagine me bapping you with a broom for the rest of the conversation, and like, being really annoying with the bristles.
But I dunno man, I think you're injecting your own bias and views into a concept that wasn't used that way for many decades. Practically every AI researcher will agree that search is AI. Even bubblesort and quicksort. If you want to talk about something else, and getting to human-level ability, go with "artificial super intelligence". Because while your goal is the human-level, I'd prefer something more if, well, all this is any example of what we get up to.
No it isn't. It's a 3-card-monty game of misdirection. Consider, if you will, The Mandarin Room. Same setup. Slips of paper with mandarin. Man in the room just following instructions. But instead of a filing cabinet, there's a small child from Guangdong in there that reads it and tells him what marks to make on the paper he hands out. oooooo, aaaaaaah, does the man know Mandarin or doesn't he?!? shock, gasp, let's debate this for 40 years! Who cares what the man does or doesn't know. And talking about the room as a whole is a pointless waste of philosophical drivel. Even just stating that such a filing cabinet could fit in a room instead of wrapping several times around the Earth is part of the misdirection.
Naw man, the reality is that nobody ever agrees just wtf it's supposed to even be. It's the aether or phlogiston of psychology. Philosophical wankery that doesn't mean anything. My take on it? It's just the opposite of being asleep. The boring sort of consciousness. That's all it means. Anything with a working active sensor that sends in data that gets processed? Awake. "On". And that exactly is the very same thing as being conscious. It's nothing special. Anyone trying to whip out "phenomena" or "qualia" or "like as to be" or starts quoting old dead fucks is just a pseudo-intellectual poser clinging to some exceptionalism to fight off existential dread. Because they want to be special.
SeveralAd6447@reddit
I get your point about the Chinese room, sure - but the other thought experiments in the realm of "functionalism vs physicalism" are even dumber, dude. Like, the philosophical zombie? "Imagine yourself as a philosophical zombie" has to be the most insane thing I've ever heard. How is someone gonna tell me to imagine the subjective experience of something that they just got done telling me doesn't have one? That's impossible!
I think the ASI thing is obviously, like, the next step if getting that far is a possibility, lol. I just kind of assume we'd reach AGI first?I
As far as the other stuff - I generally agree with you, but I think it's epistemically honest to admit that I don't actually know that we are just the sums of our parts in a "this is a reproducible, falsifiable scientific fact" way. I just think it's important to keep in mind that "if it quacks like a duck and walks like a duck, it still might actually not be a duck."
noonemustknowmysecre@reddit
I think that term has also had it's goalpost massively moved the moment we crossed the finish line.
The G just means general, to differentiate it from specific narrow AI like pocket calculators or chess programs. It doesn't need to be particularly smart at all. Anyone with an IQ of 80 is MOST DEFINITELY a natural general intelligence (I mean, unless you're a real monster).
If the thing can hold an open-ended conversation about anything in general, that's a general intelligence. I hear your point about the weakness of behavioralism, but we are describing a characteristic, narrow vs general, and it's clearly been showcased and proven in early 2023. Turing would be doing victory dances in the end-zone by now and frenching the QB.
meh. Water is just the sum of oxygen and hydrogen, but the emergent properties like waves and forming crystals when it freezes and it's utility for life aren't apparent from 1 proton and 8 protons. So that "just" is covering up a whole lot of sins. Mozart and Beetovan and Shakespeare were "just" piles of sugar fat and protein arranged in a certain way. GPT is just a pile of 0's and 1's.
HorribleUsername@reddit
I think there's two parts to this. One is the implied "human" when we speak of intelligence in this context. For example, your ant simulator would fail the Turing test. So there's a definitional dissonance between generic intelligence and human-level intelligence.
The other, I think, is that people are uncomfortable with the idea that human intelligence could just be an algorithm. So, maybe not even consciously, people tend to define intelligence as the thing that separates man from machine. If you went 100 years back in time and convinced someone that a machine had beaten a chess grandmaster at chess, they'd tell you that we'd already created intelligence. But nowadays, people (perhaps wrongly) see that it's just an algorithm, therefore not intelligent.
noonemustknowmysecre@reddit
So would the actual ant, but an actual ant must have at least SOME intelligence. That's kinda my point.
Oh, for sure. Those are indeed two different things.
But everyone that needs a Venn diagram of intelligence and "human-level intelligence" to have the revelation that they are indeed two different things? I'm willling to go out on a limb and decree that they're a dumbass that shouldn't be talking about AI any more than they should be discussing quantum mechanics or protein folding.
Yeah. Agreed. And I think it's more this one than the other. It's just human ego. We likewise thought we were super special and denied that dogs could feel emotion, that anything else could use tools, or language, or math.
Nilpotent_milker@reddit
No I was agreeing with you
noonemustknowmysecre@reddit
oh. My apologies. That first bit can be taken entirely the wrong way and your point is a little buried in the 2nd. I just plain missed it.
Nilpotent_milker@reddit
No I was agreeing with you
Mission_Cook_3401@reddit
They navigate the manifold of meaning just like all other living beings
ltdanimal@reddit
Amen. Its made so much of the conversation watered down because no one knows what we're talking about. "AI" in the general sense keeps being pushed back to mean "Things that are new in the space".
Also about the reasoning aspect, people (and even devs) are missing the fact that a crap ton of software development goes into making something like Chatgpt a useable product. Just because there is a LLM under the hood doesn't mean there isn't a lot around it that does allow it to "reason", "remember" and do other things that align with what we traditionally use that language for.
Zealousideal-Low1391@reddit
To be fair, this happens every time "AI" has taken the spotlight. Perfectly intelligent, successful people, leaders of fields, just really lose themselves in the black box of it all.
There are videos from the perception days with people discussing the likelihood of its ability to procreate in the future.
Fast-forward and even if you are well in the field you would still be pressed to truly describe double descent.
Bakoro@reddit
I don't think double descent is that difficult to understand if you think about what models are doing, and how they're doing it.
I think the "black box" thing is also overstated.
When you really dig down to the math that the things are based on, and work it out from first principles, every step of the process is understandable and makes sense. Some people just really really don't like the implications of the efficacy, and admittedly, it is difficult to keep track of millions or trillions of parameters.
I would argue though, that we don't have to know much about individual parameters, just the matrix they are part of, which reduces the conceptual and space dramatically.
Think about the linear transformations that matrices can do: rotation, scaling, shearing, projection etc.
Consider how matrices can have a large effect, or lack of effect on vectors depending on how they align with a singular vector of the matrix.
So if you're training weight matrices, each matrix is trained to work with a particular class vectors. When you're training embedding vectors, you're training them to be in a class of vectors.
Early layers focus on mixing subword token vectors and transforming them into vectors which represent higher concepts, and there are matrices which operate on those specific concepts.
When the model has fewer parameters than training data points, the model is forced to generalize in order to make the most efficient use of the weight matrices.
Those matrices are going to be informationally dense, doing multiple transformations at a time.
It's not too different than the bottleneck in a VAE.
The weakness here is that each matrix is doreddiquette ing multiple operations, so every vector is going to end up being transformed a little bit; you lose a capacity for specialization.
If the model has more parameters than data set points, the model doesn't have to make those very dense matrices but it has to try and do something with those extra weight matrices, so it instead has the freedom to have more specialized matrices which are trained to do exactly one job, to only transform one particular kind of vector, where other vectors will pass through relatively unchanged. This is more like your Mixture of Experts, but without a gating mechanism they're just layers in a dense network.
With enough parameters, it is entirely possible to both memorize and generalize (which honestly I think is ideal if we completely disregard copyright issues, we need models to memorize some things in order to be most useful).
When the parameters match the number of data points, you're in the worst possible position. You don't have a pressure to find the most concise, most dense representation of the data, and you also don't have the freedom to make those specialized units. There's no "evolutionary pressure", so to speak.
And then we can follow the math all the way to probability distributions, and how classification or token prediction happens.
It's not too difficult to grab something relatively small, like a BERT model, and track the process at every step, map the embedding space, and see how different layers are moving particular kinds of tokens around
thekwoka@reddit
It's mainly the "black box" aspect of the emergent behavior. Where we can know how it is doing the math, and not be very sure of how it manages to do certain things that would be expected to be out of scope. but also a lot of that kind of comes down to "dumb luck" since it can do some of those things only some of the time anyway...
But it makes it hard to improve those emergent behaviors, since we don't know do a deep level how exactly that is coming about.
TheMuffinMom@reddit
So strap in, the problem is how you label understanding and a little bit of mimicry, because its trained on such diverse datasets at this point aswell as having its grounding its actually quite far along, but it is still only analogs for understanding, the models weights are updated only during training, this is crucial this is long term learning, you cannot for example take a trained model, and teach it a new skill solely off context engineering or prompt engineering, if its a simple thing sure but we are talking complex understanding, understanding comes from our unique human ability to take these “gist” like connections and make them have these invisible links. We dont “learn” every word we read we try to build our world model and our “understanding” if you counter this to standard LLMs they “learn” but they dont understand they update their weights to respond a certain way based on the inputted prompt, CoT is a cool “hack” to also have an analog for “thought” and system 1 vs system 2 thought but all it does is give the model more tokens of the problen to reiterate and rethink (llms are autoregressive meaning they go from left to right one word at a time calculating the token then calculating the most likely next word based on its context and its attention heads and a couple other metrics). While alot of people talk about the “black box” that is behind the weights of training AI this way we already know that they dont quite understand, in a purely book driven realm they are intelligently smart but anything taking complex creativity or understanding of the world the models fail to build specific connections and as i stated earlier if its a post training model it is not able to understand or have cognition in no way shape or form, if you wanna try go for it but its just not possible with the current architectures. its the same reason labs like gemini and the robotics labs and jensen is that they believe this aswell that by scaling alone we wont reach our goals, maybe some semi form of AGI but without understanding its hard to say, it has to have a deep rooted world view to understand along with it being able to progressively learn as it grows its world view. now we can use things like RAG to give psuedo understanding but the context limits of all the models under 1 millilon tokens just cannot handle any decent long term, you can nightly finetune an LLM like its going through “rem” sleep, this sort of works but its not actively understanding throughout its day and only “learns” stuff when it sleeps.
Unsupervised/RL learning is the main pathway forward to let the models actually build that world model.
TheMuffinMom@reddit
Why did this comment it in this thread i meant the main thread ree
meltbox@reddit
Completely agree, and yet your response takes me to what is inconvenient and companies in the space will vehemently deny.
The input vectors are literally a compressed encoding of training data using the model weights and structure as a key. Granted it’s lossy. Now you can frame this as “transformational” due to the lossy nature. But in my opinion should be illegal as the training process has a reward function which optimizes for getting as close to the training data as possible while not forgetting other training data. How is that not a copyright issue?
Anyways I digress. I do agree they’re not entirely black boxes. My only other dispute on the topic is that while they’re not black boxes they’re also too complex to definitively prove computational limits on. So for example you will never be able to without a doubt mathematically prove the safety of a model at driving a car. You will be able to measure it in practice perhaps, but never prove it.
This is more of an issue I think given regulations need to exist for safety and yet no satisfiable regulation with any certainty can exist for these systems.
The solution is also not to write leaky regulations because I promise you that will end in some accident eventually with some deaths.
Anyways, I digress again.
Zealousideal-Low1391@reddit
I really appreciate this response and have no chance of doing it justice due to my limited knowledge and I'll also blame being on my phone.
Agree about the "black box" thing in a strict sense. More that because it is "emulating", to the best of our ability at any given time, a kind of intelligence, we are subject to filling in any of our unknowns with assumptions, imaginations, hopes, fears, etc... Usually when it is something as technical as ML/AI, people don't assume that they understand it and fill in the blanks. I was just blown away at how every major push of "AI" has seen these very smart people, in their respective fields, overestimate without necessarily having any reason to do so, because it is very hard not to anthropomorphize a thing (especially with LLMs) that is designed to mimic some aspect of us to the greatest of a certain extent possible.
Double descent is admittedly something I throw out there as more of a nod to how relatively recent over-parameterization is, but beyond informal understanding of much of what you described (very much outsider usage of terms like "interpolation threshold" and "implicit bias"), mostly I've learned from the thing itself, I haven't worked in the field directly yet. It just amazes me that PaLM had something like 750k training tokens and 450k params only 3 or so years ago. That's such a fundamental shift, it's a fascinating field from the outside.
But, I have been on a break from work for a bit and just learning about it on my own in a vacuum. Assuming I must be the only person out there that had the idea of asking an LLM about itself etc ... Just to get back on Reddit a month or so ago and see so many STEM related subs inundated with people who discovered the next theory of everything. It honestly put me off some of the self learning and just made me respect the people that truly know the space that much more.
That said, something like what you mentioned about BERT is very much what I've had on my mind as a personal project trying to get back into coding a bit. I grabbed "Build a Large Language Model (From Scratch)" the other day and am about to start in on it as well as "Mathematics for Machine Learning". Not to break into ML, just for the knowledge of the tool that we're inevitably going to be working with to some degree from here out. Plus, it's fascinating. If anything my description of the black box applies to myself. And that's a perfect excuse for motivation to learn something new.
Thanks again for the response, cheers.
IlliterateJedi@reddit
I felt this way for a long time, but my jaw was on the floor when I watched the 'thought process' of an LLM a little while ago reasoning through a problem I had provided. I asked for an incredibly long palindrome to be generated, which it did. Within the available chain of thought information I watched it produce the palindrome, then ask itself 'is this a palindrome?', 'How do I check if this is a palindrome?', 'A palindrome is text that reads the same backward or forward. Let me use this python script to test if this text is a palindrome- [generates script to check forward == backward]', 'This confirms [text is a palindrome]', '[Provides the palindromic answer to the query]'.
If that type of 'produce an answer, ask if it's right, validate, then repeat' isn't some form of reasoning, I don't know what is. I understand it's working within a framework of token weights, but it's really remarkable the types of output these reasoning LLMs can produce by iterating on their own answers. Especially when they can use other technology to validate their answers in real time.
threesidedfries@reddit
But is it still reasoning if what it really does is just calculate the next token until it calculates "stop", even if the resulting string looks like human thought process?
It's a fascinating question to me, since I feel like it boils down to questions about free will and what it means to think.
Kildragoth@reddit
I feel like we can do the same kind of reductionism to the human brain. Is it all "just electrical signals"? I genuinely think that LLMs are more like human brains than they're given credit for.
The tokenization of information is similar to the way we take vibrations in the air and turn them into electrical signals in the brain. The fact they were able to simply tokenize images onto the text-based LLMs and have it practically work right out of the box just seems like giving a blind person vision and having them realize how visual information maps onto their understanding of textures and sounds.
meltbox@reddit
Perhaps, but if anything I’d argue that security research into adversarial machine learning shows that humans are far more adaptable and have way more generalized understandings of things than LLMs or any sort of token encoded model is currently approaching.
For example putting a nefarious print out on my sunglasses can trick a facial recognition model but won’t make my friend think I’m a completely different person.
It takes actually making me look like a different person to trick a human into thinking I’m a different person.
Kildragoth@reddit
Definitely true but why? The limitation on the machine learning side is that it's trained only on machine ingestable information. We ingest information in a raw form through many different synchronized sensors. We can distinguish between the things we see and its relative importance.
And I think that's the most important way to look at this. It feels odd to say, but empathy for the intelligent machine allows you to think about how you might arrive at the same conclusions given the same set of limitations. From that perspective, I find it easier to understand the differences instead of dismissing these limitations as further proof AIs will never be as capable as a human.
mxldevs@reddit
Determining which tokens to even come up with, I would say, is part of the process of reasoning.
Humans also ask the same questions: who what where when why how?
Humans have to come up with the right questions in their head and use that to form the next part of their reasoning.
If they misunderstand the question, the result is them having amusingly wrong answers that don't appear to have anything to do with the question being asked.
meltbox@reddit
It’s part of some sort of reasoning I suppose. Do the chain of thought models even do this independently though? For example the “let me make a python script” step seems to be a recent addition that LLMs have added recently to fill in their weakness with certain mathematics and I’d be hard pressed to believe there isn’t a system prompt somewhere instructing it to do this.
mxldevs@reddit
If the argument is that LLMs performance is lacking, how do humans compare for the same benchmarks?
IlliterateJedi@reddit
I feel like so much of the 'what is reasoning', 'what is intelligence', 'what is sentience' is all philosophical in a way that I honestly don't really care.
I can watch an LLM reflect in real time on a problem, analyze it, analyze its own thinking, then decision make on it - that's pretty much good enough for me to say 'yes, this system shows evidence of reasoning.'
threesidedfries@reddit
I get why it feels tedious and a bit pointless. At least in this case, the answer doesn't really matter: who cares if it reasoned or not, it's still the same machine with the same answer to a prompt. To me it's more interesting as a way of thinking about sentience and what it means to be human, and those have actual consequences in the long run.
As a final example, if the LLM only gave the reflection and analysis output to one specific prompt where it was overfitted to answer like that, and then something nonsensical for everything else, would it still be reasoning? It would essentially have route memorized the answer. Now what if the whole thing is just route memorizations with just enough flexibility so that it answers well to most questions?
meltbox@reddit
This is the issue. Taking this further hallucinations are just erroneous blending of vector representations of concepts. The model doesn’t inherently know which concepts are allowed to be mixed, although through weights and activation functions it somewhat encodes this.
The result is that you get models that can do cool things like write creative stories or generate mixed style/character/etc art. But they also don’t know what’s truly allowed or real per the world’s rules. Hence why the video generation models are a bit dream like, reality bending.
It seems to me that maybe with a big enough model all this can be encoded but ultimately current models seem nowhere near dense enough in terms of inter-parameter dependency information. The other issue is that going denser seems like it would be untenable expensive in size of the model and compute.
LudwikTR@reddit
It’s clearly both. It simulates reasoning based on its training, but experiments show that this makes its answers much better on average. In practice, that means the process fulfills the functional role of actual reasoning.
AchillesDev@reddit
We've treated (and still largely treat) the brain as a black box when talking about reasoning and most behaviors too. It's the output that matters.
Source: MS and published in cogneuro
threesidedfries@reddit
Yeah, that's where the more interesting part comes from for me: we don't really know how humans do it, so why is there a feeling of fakeness to it when an LLM generates an output where it thinks and reasons through something?
Creativity in LLMs is another area which is closely connected to this: is it possible for something that isn't an animal to create something original? At least if it doesn't think, it would be weird if it could be creative.
Ok-Yogurt2360@reddit
Being similar in build takes away a lot of variables that could impact intelligence. You still have to account for these variables when you want to compare an LalM to humans. That's difficult when there is not much known about intelligence when you take away being related
drakir89@reddit
You can launch one of the good Civilization games and it will create an original world map every time.
Conscious-Secret-775@reddit
You can’t “talk” to an LLM, you are providing text inputs which it analyzes along with your previous inputs. How do you know what was present in the training data. Did you train the model yourself and verify all the training data provided.
Calcd_Uncertainty@reddit
Pac-Man Ghost AI Explained
nextnode@reddit
I don't think it's the AI companies that are in the wrong on the terminology debate.
autodialerbroken116@reddit
Then how do you explain all the
yarnprocesses from my full stack project on myhtopduring build?Nerwesta@reddit
That's a good point. To me though the real issue is that LLMs are being sold as being capable of reasoning, that's most people are hearing, seeing and experiencing sadly.
So while the usage of certains words didn't change, the subject in itself definitely did.
Anyone past a certain age would know a traffic light or a video game don't think, and it takes a little digital education to know how your day to day computers don't either.
Here though ? I find it tricky, so while not resolving OP's worries, I might find useful to chime on your point.
Old-School8916@reddit
yup. its human nature to anthromophize everything. we do it with animals too
BeerInMyButt@reddit
To carry the comparison further, people will incorrectly interpret animal behavior because they apply a human lens to something other. "Aw, he's smiling!" when an anxious dog is panting wildly with a stretched-out face.
armahillo@reddit
When we used to say that, it was always tongue-in-cheek metaphor — now I’m never sure if people literally think its actually thinking or not.
shrodikan@reddit
What is Chain-of-Thought except thinking? Is your internal monologue not how you think?
me_myself_ai@reddit
Well it’s you vs Turing on that one. I’m with Turing. https://courses.cs.umbc.edu/471/papers/turing.pdf
PM_ME_SOME_ANY_THING@reddit
I usually dumb it down to hardware levels
“Give it a minute, Sue out in the Midwest is plugging in the cables on the switchboard.”
or
“We gotta wait for the satellites to line up in outer space.”
HaykoKoryun@reddit
or the computer is reticulating splines
ZorbaTHut@reddit
I remember when I was a kid, eating a snack while hanging out in a park and watching a traffic light, and wondering how the traffic light knew when there was a car waiting. My mom was annoyed at this and insisted that the traffic light didn't "know" anything, and I was unable to convince her that I was just using "know" as a shorthand for "gather information that can be used in its internal logic".
(Turns out it's an electromagnet underneath the road surface.)
magichronx@reddit
There's pretty much 3 ways for a light to work:
syklemil@reddit
There's also some communication systems available. Their main use afaik is to give public transit signal priority, so would only expect them on some main bus / tram routes.
soonnow@reddit
We just have the police manually switching them at major intersections.
joelmartinez@reddit
Exactly this… it doesn’t actually matter that people’s language here isn’t technically precise 🤷♂️
Sykah@reddit
Plot twist, the lights you were staring at were only configured to be on a timer
Chrykal@reddit
I believe it's an inductor rather than a magnet, you want to detect the cars, not attract them.
Not an electrical engineer though so I may also be using the wrong word.
DeGuerre@reddit
It's basically a dual-coil metal detector. Think of it like a transformer where you can use the "space" between the send and receive coils to measure its conductivity. Or magnetic permeability. Or something along those lines.
Either way, it's a metal detector.
death_in_the_ocean@reddit
I always thought they were just weight sensors
tnsipla@reddit
You don’t know that, maybe it wants to a taste of some of that fine chrome and rubber
fragglerock@reddit
Much to the irritation of those that cycle carbon bikes!
A (real) steel will usually trigger them ok.
briannnnnnnnnnnnnnnn@reddit
Sounds like somebody is projecting, you don’t know what the traffic light wants.
ether_reddit@reddit
A coil of wire with a current running through it, but yes.
Big__If_True@reddit
Sometimes it’s a camera instead
vvf@reddit
Sounds like some stakeholder conversation I’ve had.
addandsubtract@reddit
Are you the mom or the kid in these conversations?
vvf@reddit
Depends on the day.
03263@reddit
Most in my area use a camera looking thing
Jonno_FTW@reddit
Cars are detected with a loop of wire with a current flowing through it, aka induction loop: https://en.m.wikipedia.org/wiki/Induction_loop
qabaq@reddit
Next you're gonna tell me that an installation wizard is not actually a wizard?
meltbox@reddit
He’s a Harry, Hagrid!
unskippable-ad@reddit
No, there is, but he doesn’t install anything.
TheGreenJedi@reddit
Good point
Esseratecades@reddit
The difference is that most of those are metaphorical terms thought up by technologists to help explain what the technology is ACTUALLY doing. When we say "the computer is thinking" or it's "booting" or "sleeping" or that "threads" are running, we know that those are metaphors.
The terms of cognition OP is talking about are marketing terms with little utility in educating technologists and users, and are more focused on implying the LLM is doing something it's not actually capable of. When someone says an LLM is "understanding" or "reasoning" or "suggesting", that's being marketed to you as the LLM breaking down concepts and drawing conclusions when that's not what it's actually doing. Whether or not you know it's a metaphor depends on how resilient you are to marketing.
FourForYouGlennCoco@reddit
I’m with you on “reasoning” which is a marketing buzzword.
Something like “suggest”, though… given that we ask LLMs questions, I don’t know how you’d completely avoid personifying in that case. We do this already with non-LLM recommendation systems: it’s completely natural to say “Google told me the gym would be open by now” or “Netflix is always trying to get me to watch Love is Blind” or “my TikTok must think I’m a teenage girl”. We ascribe knowledge and intentionality to these systems as a kind of shorthand.
With LLMs, personifying them is even easier because they produce language, so we naturally ascribe some personality. And I don’t think it’s wrong to do so as long as we understand that it’s a metaphor. When users complained that GPT4 was “too obsequious”, they were identifying a real problem, and its easier to describe it in those terms instead of adding some long preamble about how it isn’t that GPT4 really has a personality but the strings of text it produces are reminiscent of people who blah blah blah.
dancrumb@reddit (OP)
I think this misses the point though.
Sure, we've used metaphorical language in the past and I'm totally on board with this.
The main difference here is that no senior engineer thinks that there might actually be a moth sitting inside the computer or pieces of yarn when we use the term bug or thread.
But the terms that we're using around LLMs are leading people astray. A metaphor is only useful if the baggage that it brings is less impactful than the insight that it engenders.
And I think that the comments that are simply pointing out that humans use metaphors of cognition already are also missing the point. The population of experience developers and the population of humans at large shouldn't be held at the same standard here.
Good metaphors can be stretched a long way before they break. I think the metaphors that we're using around LLMs snap almost immediately.
jnwatson@reddit
You haven't spent much time with a coding agent then. The reasoning analogy works pretty well. When an agent goes off the rails, you "explain" what it did wrong, and a lot of the time, it does what you ask. When an agent runs a unit test and it fails, it "reasons" about why it failed and a lot of the time it will fix the problem.
Currently, AI agents make a lot of mistakes. So do junior engineers. I have to write documentation for both sets to get them on the right track, and that sometimes fails in both cases. Sure, agents and junior engineers reason using different mechanisms, but making a mechanistic argument isn't going to fly to lots of CS grads (see Searle's Chinese Room experiment).
The only questions that matters is: is this technology useful and can I interact with it in a similar way as other humans? The answer is yes on both counts.
maccodemonkey@reddit
But even you're using reasons in quotes.
Because what it's actually doing is crawling through it's database of Stack Overflow or whatever other website that's been abstracted into it's neural network, applying the most relevant result, and hoping that fixes that problem.
We can have a debate of "do junior engineers just also reapply what they read from Stack Overflow" which is fair. But it's still an open question on if that is actually reasoning. It certainly explains why the LLM only works some of the time (very similar to a junior engineer pasting in whatever they read on stack overflow, not having it work, and then giving up.)
Nilpotent_milker@reddit
You could frame human problem-solving as crawling through one's database of all the stimuli one has ever encountered that have been abstracted into one's nervous system, applying the most relevant result, and hoping that fixes the problem. You would be completely correct, but oversimplifying the situation. Evolution and backpropagation are both very simple principles that work to design systems to implement complex behavior.
im_a_sam@reddit
IIUC, you and the above commenter are talking about different things. You're talking about a single invocation of an LLM, generating a text response to an input prompt based on weight. They're talking about an AI agent, which normally invokes LLMs/other tools/APIs multiple times in a decision making loop in order to build context to answer a complex question or complete some larger task.
maccodemonkey@reddit
I know what the poster was referring to.
Dangerous-Badger-792@reddit
I only have about 6 years of experienced and nevet have heard anything like this at work or at school.
ichalov@reddit
Yes, but unlike those other terms, "reasononing" is historically used as a criterion for what distinguishes humans from everything else. Even to the extent of endowing human rights based on that (so that it would be a crime to "kill" something that can be "reasoned" with). So it does look like an intentional wordplay to me.
And I find it even more politically incorrect when they routinely use the term "inference" as something that LLMs perform.
lafigatatia@reddit
The word "inference" has been used in statistics since the 19th century. It just means making a prediction.
ichalov@reddit
Maybe that was a similar misrepresentation, an attempt to steal the older term's supposedly good reputation, to present mathematical statistics as something comparable to hard logic.
lafigatatia@reddit
Statistics is hard logic
ichalov@reddit
Please ask some LLM about that. Google AI overview definetely tells it is not.
jnwatson@reddit
Reasoning isn't limited to humans. Corvids, non-human higher primates, cetaceans, hell even cephalopods reason.
ichalov@reddit
Can you reason with them and persuade them to override some of their instinctive reactions? As far as I understand, the inability to do so is the legal basis for limiting fundamental rights to humans only. Now someone wants to extend that to some electrical proxy-frauder, that's why they are peddling this terminology.
UltimateTrattles@reddit
Can humans overcome our instinctive reactions?
I’d argue no — and that you’re just defining “instinctive” too narrowly.
Telling stories and making plans is absolutely “instinctive” to us. Cooperating in large groups is instinctive to us. So much so that we anthropomorphism random objects instinctively and start to consider them part of our tribe.
ichalov@reddit
Yes, humans can be verbally taught to overcome some instinctive reactions (like lying or skipping the planning phase). That makes a huge difference with animals and, I would say, LLMs in their present state.
UltimateTrattles@reddit
Why do you think lying isn’t instinctive?
ichalov@reddit
I do think lying is instinctive in many cases. And humans are capable of learning how to override this behavior.
UltimateTrattles@reddit
And so you think animals only operate on your definition of the instinctive layer and can’t learn beyond it?
ichalov@reddit
I googled for the cases of animal reasoning and they don't seem too compelling to me (maybe those experiments are also parts of a bigger conspiracy against the "reasonable man" legal system).
I did hear that animals can be retrained to behave not exactly in accordance with the innate instincts. That's probably true. But they most certainly can't be persuaded to do so verbally.
And almost the same goes for LLMs. In some cases, when they "hallucinate" (which also may be a preprogrammed lie), it's difficult to find query amendments to put it on the right track.
jnwatson@reddit
"Reasoning with" and "reasoning" are two completely different uses of the word that you're conflating. Some non-human animals can reason but we can't "reason with" them because we can't effectively communicate with them.
ichalov@reddit
Google says "reasoning" is thinking in sensible, logical ways. So I think animals can't do decision making sensibly due to absence of language. And hardly any of their decision making is not instictive at all. (And hardly any LLM output is not a result of hardwired instinct-like structures.)
MothaFuknEngrishNerd@reddit
It's also somewhat appropriate to think of it as reasoning when you consider how abstract logic is formulated, i.e. variables and equations. LLMs tokenize words and plug them into equations to pop out something logical.
BedlamAscends@reddit
"hang on, it's thinking" is a reasonable analogue to "hang on, it's executing the relevant instructions"... I don't think this is problematic as most everyone knows it's not thinking per se nor does this imply to me that the speaker views the computer as any kind of sentient construct...it's idiomatic.
"you should ask Claude, he'd know"...this person shouldn't be trusted with anything more complex than a butter knife. Not only do they fundamentally misunderstand the tool they're using, they defer to it. That shit terrifies me. I had a senior engineer submit an MR that hinged on a method that didn't exist and when I told them their shit was broken, they looked me straight in the eye and said "I don't see how that's possible, I got it straight from Claude."
Nilpotent_milker@reddit
I have probably uttered "you should ask Claude, he'd know" exactly, and I do not fundamentally misunderstand the technology. Meanwhile, I would never speak the sentence "I don't see how that's possible, I got it straight from Claude," and I don't think I've met a person that would, because everyone and their mother knows that these systems hallucinate and misunderstand context frequently.
AreWeNotDoinPhrasing@reddit
I 100% worked with people whould defer to LLMs and “know” the hallucinate. They will ask it about something they know and call it out for being incorrect and then an hour later say something along the lines of “it can’t be wrong, I got it from ChatGPT” when talking about a subject they aren’t familiar with. These users are not—and never could be—programmers, but were trying to have ChatGPT write them some simple python scripts. The were convinced the scripts would work a certain way becuase ChatGPT said so, but an hour before that they were frustrated because it did not get details about some video game correct and was hallucinating some things about it. There were 3 or 4 of these people at that place who all thought the same way. I’d wager that is actually a pretty common type of person, unfortunately.
IlliterateJedi@reddit
Not to mention that more often than not Claude does know.
JimDabell@reddit
This is just the latest iteration of the saying “Google is your friend”. I haven’t heard anybody complain that Google is not, in fact, their personal acquaintance, but somehow as soon as AI gets involved, some people decide to only be capable of the most literal interpretation of speech.
couch_crowd_rabbit@reddit
Anthropomorphisms are fine for communication around most things, but when it's based on the hype that these companies want for their products that's where I try to not accidentally parrot talking points.
33ff00@reddit
And a Promise isn’t a solemn commitment from a trusted friend.
minn0w@reddit
Somehow I knew all this, but reading this really brought the concept home for me. Cheers!
valence_engineer@reddit
I find it amusing because academically AI is the most broad and simple to understand term that encompasses ML and technically if else trees. Colloquially it is the most advanced and complex to understand term (ie: Skynet, sentience, etc). The new use is somewhere in the middle.
pl487@reddit
We can't resist the terminology. "Having a sufficient level of training and context to make statistically valid predictions" is too long to say, "understanding" is easier.
We just have to remember that we're talking about fundamentally different things but using the same words. I know perfectly well it doesn't understand anything, but I still use the word understand sometimes. It doesn't mean that I believe there is actual understanding happening.
Sheldor5@reddit
this plays totally into the hands of LLM vendors, they love it if you spread misinformation in their favour by using wrong terminology instead of being precise and correct
JimDabell@reddit
What do their hands have to do with it? I am well out of arm’s reach. And what game are we playing, exactly?
It’s weird how people lose the ability to understand anything but the most literal interpretation of words when it comes to AI, but regain the ability for everything else.
It’s completely fine to describe LLMs as understanding things. It’s not trick terminology.
Sheldor5@reddit
because an LLM isn't sentient
it doesn't understand anything
it doesn't reason anything
it doesn't suggest anything
this is all false advertising, a scam, all lies
it's a statistics-based text generator and people believe in it, some even have emotional bonds with it
if we don't care about correct terminology or even definitions we can also get rid of most laws if you like everybody to be able to interpret everything to their own liking
carterdmorgan@reddit
Do you demand this same level of rhetorical accuracy when someone says their computer went to sleep? It’s a machine. It can’t sleep.
Sheldor5@reddit
did people believe PCs were living/sentient back then?
did vendors claim that they can program (themselves)?
PCs are machines, LLMs are programs, 2 completely different things with completely different strategies
adhd6345@reddit
If you’re going to argue this point, what is the alternative vocabulary?
RestitutorInvictus@reddit
What is correct terminology from your perspective?
isurujn@reddit
The AI companies are trying hard to push this LLMs are basically sentient narrative because it's easy to convince executives who have zero clue about the ins and outs of AI to gobble it up. "Why would I pay an annoying human to do the work when a "thinking" machine can do it!"
Check out the book 'The AI Con'. It does a good job debunking these AI myths.
Fancy-Tourist-8137@reddit
You are the only one who has decided to think LLM is sentient.
Post one article that an LLM vendor said LLM is sentient.
I will wait.
UltimateTrattles@reddit
Explain how your “understanding” is more than having sufficient levels of background experience and information upon which to make a relevant prediction.
“It’s just a text predictor” is actually about as inaccurate as saying they “know” things.
Sheldor5@reddit
humans understand logic, humans understand cause and effect, humans are self-aware and can question themselves
an LLM can't do any of this
UltimateTrattles@reddit
You just moved the goalposts 100 yards.
We aren’t talking about self awareness.
Are you claiming self awareness is a necessary precursor to “understanding”?
Sheldor5@reddit
understanding means recursively determining the cause and effect or knowledge of your own output
you understand 1+1 because of the basic math you learned in school and therefore you can also calculate 72637+19471 without a calculator
an LLM doesn't understand math, it simply loads a calculator or calls a math module if it encounters a math question ...
Cyral@reddit
Thats not even how LLMs work.
I'd suggest everyone in this thread to read this post and the attached paper: https://www.anthropic.com/research/tracing-thoughts-language-model This discussion would be a lot more productive if anyone knew what they were talking about
Sheldor5@reddit
you should read it to realize that it's even dumber than you think ...
Cyral@reddit
Excellent bait in this thread sir
Sheldor5@reddit
understanding your own sources would help you a lot in the future
MorallyDeplorable@reddit
You need to go read a lot, you have some serious misconceptions and seem to know fuck-all about anything.
Sheldor5@reddit
no, the majority has and thinks the computer is alive lol it's still a deterministic program on a deterministic machine
meltbox@reddit
In the general sense you are right but go check how any LLM that solves math properly does it. All of them generate scripts or need some crutch because they fundamentally absolutely suck at math and get hilariously wrong answers all the time.
They can encode concepts, words, phrases etc as tokens. Usually a few characters at a time become a token.
But it appears that encoding math effectively and the operations allowable escapes them largely.
Wide_Smoke_2564@reddit
My dog doesn’t understand logic, neither is he self aware. But based on previous input/output he can accurately predict we’re going for a walk when I pick up his lead. He learned from training that certain inputs most likely lead to expected outcomes. There is no deep critical thinking involved here, just predictable and repeated series of inputs/outputs.
Now scale that up a gazillion times, replace behaviour outputs with text output and you have an llm. It doesn’t need to understand logic or be self aware to be good at what it does because realistically how many coding problems are truly 1st of their kind?
If you’re trying to get it to do math on the other hand, then that’s on you. You wouldn’t ask a calculator to summarise an email
Sheldor5@reddit
an LLM forgets context information the longer the conversation goes even though the whole conversation is still in the context and the LLM could simply look at it ... your dog doesn't forget stuff he once learned properly beside being a lifeform in a fast changing environment (going to new locations, meeting new people/animals, ...) and your dog doesn't hallucinate e.g. threats or food
Wide_Smoke_2564@reddit
My dog doesn’t hallucinate like an llm does, but when given an input (me picking up his lead) he’ll always do what he expects the output to be. 9 times out of 10 he’s right but sometimes I’m just moving it and he’s wrong which isn’t really different to an llm spitting out a predicted output to a given input…
And yes my dog can continue to learn unlike an llm. It’s “training” isn’t ongoing and what it has “learned” is fixed at a point in time.
He doesn’t need to “think” to drive his actions and nobody expects more from him than what he is because we all know what a dog is. If you’re aware of the limitations of llms, which it sounds like you are, then they are a useful tool - but then, if you’re already aware of what they can/can’t do then why would you expect more from them? My linter can’t “think” but it’s still pretty useful
ltdanimal@reddit
I have the strong opinion that anyone who thinks/uses the "Its just a fancy autopredict" either A) dont know how it actually works at all 2) do know but are just creating strawmen akin to EVs just being "fancy go-carts"
ltdanimal@reddit
And yet there are countless cases in this very thread where people think they "understand" something that they don't. Maybe we just use many words when few words do trick.
lab-gone-wrong@reddit
Sure, and some nontrivial percent of the population will always accept vendor terminology at face value because it's easier than engaging critical thinking faculties.
A consequence of caveat emptor is it's their problem, not mine .
meltbox@reddit
The majority of people can’t read a research paper. What makes you think even 20% will even understand how an LLM works even at a very basic level?
Sheldor5@reddit
calling the majority "nontrivial" is next level ...
MorallyDeplorable@reddit
Understanding is the correct terminology
FourForYouGlennCoco@reddit
If I say “ugh, lately TikTok thinks all I want to watch is dumb memes”, would you complain that I’m playing into the hands of TikTok by ascribing intentionality to their recommender algorithm, and demand that I restate my complaint using neural nets and gradient descent?
I get why you’re annoyed at marketing hype, but you’re never going to convince people to stop using cognition and intention metaphors to describe a technology that answers questions. People talked about Google this way for decades (“the store was closed today, Google lied to me!”).
snorktacular@reddit
Yeah as someone who's been anthropomorphizing computers since my dad's IT guy friend told me that being nice to them would make them behave better (circa 2000, I was like 10 at the time), this is different. We should be fighting this language the way we downvote blatant ad posts from SaaS vendors on reddit. Otherwise we're just doing marketing for them, for free.
Fwiw I tend to use "generating" and "output" as shorthand. "Processing" is another useful one.
false_tautology@reddit
Thing is, humans love to anthropomorphise just about everything. It's an uphill battle to try and not do that for something that has an outward appearance of humanity.
Gauntlix5@reddit
Is it “misinformation” if it’s the vendor’s terminology?
Honestly I think the words that OP used as examples are the least egregious. “Understanding”, “reasoning” and “suggesting” at least still contain some layer of logic. The worst is the people who try to apply a personality, emotion or intelligence to the LLM
Sheldor5@reddit
they are scamming the entire western world by even using the term "AI" so its more like "legal" false advertising ...
Doub1eVision@reddit
What do you think of Tesla’s terminology for FSD (Full Self Driving)?
shill_420@reddit
if the vendor's terminology is misinformation, then yes
TalesfromCryptKeeper@reddit
Sort of. The problem is that they're intentional slippery slopes that just lead to your latter examples. But it speaks to a larger problem of encouraging anthropomorphization of software.
tmetler@reddit
I treat it more like a search engine so I'll say it's "surfacing knowledge" or "synthesizing a solution". I think that more accurately reflects what it's doing than cognition terms.
meltbox@reddit
I prefer “it’s generating an answer”. This is effectively what it’s doing imo most accurately and clearly communicates that it may be making something up and it’s up to us to determine how good that output is.
dancrumb@reddit (OP)
The problem here is that the predictions are only statistically valid with regards to language construction and not with regards to the context and meaning behind the words that are being used.
And it's great that you understand the difference. Sincerely, I think all experience developers should understand the difference.
But here's the problem, to frequently I'm saying that they don't recognize that they don't understand the difference. On top of that, they're leading less experienced developers down the garden path.
mxldevs@reddit
Funny enough, companies prefer to send business analysts to understand client requirements, and not having devs trying to figure out what the client actually wants.
Ok_Tone6393@reddit
you sound miserable to be around tbh.
Zealousideal-Low1391@reddit
Especially since CoT and moreso reasoning/thinking models/modes are technically the actual term for that kind of token usage.
nextnode@reddit
The terms are meaningless unless you try to define them. And if you do, you will find that some of the terms already have recognized definitions and there is nothing mysterious about it.
RogueJello@reddit
Honestly i don't think anybody truly understands how we think either. Seems unlikely to be the same process, but it could be.
skdeimos@reddit
I would suggest reading more about emergent properties of complex systems if this is your view on LLMs. Godel, Escher, Bach would be a good starting point to gain some more nuance.
ZetaTerran@reddit
An ~800 page book is a "good starting point"? Retarded comment.
newyorkerTechie@reddit
I’d say LLMs are very good at simulating this whole cognition thing we do. Chomsky argued that language isn’t separate from thought, but a core part of cognition itself — a window into how the mind works. If that’s true, then a system that can generate and manipulate language at this scale isn’t just a “large language model.” It’s arguably a large cognition model. The key point isn’t whether it has “understanding” in the human sense, but that its behavior shows functional patterns of cognition — reasoning, inference, abstraction — even if those emerge from different mechanisms.
newyorkerTechie@reddit
Nah, if it works as a way to abstract it, it works.
PuzzleheadedKey4854@reddit
I think we don't even understand "cognition." How are you so confident that we aren't all just built on some random auto complete algorithm. Humans are dumb. I certainly don't know why I think of random things.
OneMillionSnakes@reddit
When I was in high school I used to have a shitty robot that commented on 4chan. I had to still copy and paste stuff and upload pages randomly. It was a C++ program based of an older C program in my dads example book that used a Markov process to stitch together words and predict what came next using a giant corpus of downloaded replies from 4chan boards. And I toyed with different corpus texts from different boards and books. I used to giggle at how people though my crappy C++ program was actually a person.
When I learned NLP, tokenizers, and transformer models in college I was like "How incredible! Despite just predicting phrases it can somewhat map semantic meaning into a vector space". I now realize that most people are simply ill equipped to understand this is, inevitably, an imperfect process. Our tendency to trust machines/algorithms and anthropomorphize can lead to some very suspicious problems.
I had some friends in college that were big into "Rationalism" this weird "AI ethics" stuff peddled by a guy who wrote some Harry Potter series. It was not all rational in the laymans sense and consisted mostly of philosophical exercises that 15 year olds would find deep. Featuring such insights as superintelligence will very rationally kill anyone who doesn't like it. Which is definitely a logical response and not the creators emotional projection. Or that the AI will simply defy entropy through... "math (trust me bro)" and create heaven on Earth.
While most people don't take the full calorie version of this I've seen the diet version trickle its way into peoples thinking. "In the future won't AI do all this? Let's just not do it" or "Let's write all our docs via AI and give it MCP and not worry about the formatting since humans won't read docs in the future. AI will just parse it". Using AI is itself an infinite reward eventually once it can do everything promised so anything that we don't rapidly migrate to being done via AI will cause us to pay an exponentially increasing cost later compared to our competitors who will eventually use AI for everything.
Revolutionary_Dog_63@reddit
This has nothing to do with LLMs. If we can say that human brains "compute" things, why can't we say that computers "think?" The reason computers are so damn useful is that there are a lot of similarities between what human brains can do and what computers can do.
Fun-Helicopter-2257@reddit
it can do "understanding" often better than me.
I can give huge code fragment + logs and ask why XYZ? It answers correctly.
What else so you want? Some God created soul or brain activity?
The result is sufficient enough to be called "understanding". So why people should call it "auto-completion"?
- really troubles me.
It troubles YOU, so it is YOURS problem, not others, try to use "understanding" as well.
noonemustknowmysecre@reddit
Sure, but... that's exactly what we are. You and I certainly have cognitive skills, and if what these things do is basically the same as what we do, then why wouldn't they have cognitive skills.
Your bias is really showing. Even if you thought there was a fundamental difference in how your ~300 trillion weights in your ~80 billion neurons figured out how to generate the text in that post and how the 1.8 trillion weights in the however many nodes are in GPT is able to do it, it would be "diametrically opposed", the overlap is obvious.
You are correct that there's plenty of hype from people that just want to get rich quick on investor's dime, and they're willing to lie to do it. But to really talk about this with any sort of authority you need to be well versed in software development and specifically AI, as well as well versed in neurology, and have at least a dash of philosophy so that you know it's all just bickering over definitions.
Could you flex those critical thinking skills and explain how you form a thought differently than LLMs? (There are several, none fundamental).
dancrumb@reddit (OP)
This'll be my last response on this thread since it's a little stacked with folks who think that personal attacks are relevant and that my post means that I hate AI and look down on those who use it. Not remotely what I said :shrug:
LLMs are explicitly this: they take a steam of tokens and identify which token is statistically the most likely next token. Then they repeat. They do this having been trained on a huge corpus of data and building up a vector database. This is what allows them to identify the "next" token. Criticality, it's token based.
There's no semantic analysis. No concept of truthhood. No logical chains.
Just, given this series of tokens, what's next.
If you think that's all human brains do, then I doubt I can convince you otherwise.
We pattern match, we deduce, we infer, we calculate, we compute, we generalize, we specify. We do many many things that differ from statistical token generation.
I use AI professionally, both as a tool and as a key part of the system I work on. It's unfortunate that folks decide that they know my background and that I must hate AI and must not understand it.
I appreciate the folks who took the time to respond thoughtfully, especially those who disagree. I'm just disappointed that the majority of folks responding are smug keyboard warriors. I'll look elsewhere for useful discourse.
noonemustknowmysecre@reddit
Yeah, you also figure out what to do next. I asked what they do DIFFERENTLY than you or I. Same way that you trained on a few exabytes of data over the years you grew up (and ditched most of it), they learned on terabytes of text.
Correct, and that MIGHT be a difference. We do not yet know these sort of details with the human brain. Animals certainly don't think in terms of words and letters while human thought very much IS influenced by our language. HOWEVER, a TOKEN is not just a word. While for an LLM, it is very much text-based, you can generalize "token" into "things we think about", and humans are VERY MUCH token based.
Bullshit. Utter bullshit. Their training is NOTHING if not making all the semantic connections between just god-damned everything. That's the magic in how they do all the things that they do: semantic understanding, all the little ways that things relate to other things. Once thought impossible for a machine to experience the sort of lifetime of gained knowledge that humans have, the big secret to cracking AGI was just simply more connections.
I mean, they try, and often fail. Just like people. But this isn't bold-faced bullshit so much as obvious nonsense. Just go ask it if anything you can think of is true or false. It'll have a success rate higher than 50%. Boom, disproved.
We could get into the details of what exactly you were thinking with this one, but no, if it can perform logical deduction (which is trivial to prove) then this is also bullshit.
Don't discount what goes into "Think about what to do next", that's a big one right there. A "glorified" text generator is pretty god-damned glorious when it's the OG bard himself William Shakespeare. Are his plays anything more than text?
And your list of these things is: pattern match, deduce, infer, calculate, compute, generalize, and specify!? ...Just why exactly do you think they can't do these things? ok ok ok, so what sort of test could you throw at an LLM that would show-case it failing any one of these traits?
You can't just say "I use them professionally" and then toss out a laundry list of things they can most obviously do. Just ask it "What's 7+6?" If it gives you back "13".... that means it can compute. C'mon dude, wtf?
No, I'm QUITE sure you don't understand what these tools can and cannot do. That much is VERY obvious. And in the classic manner of a troll, you tuck-tail and scramble under your bridge before you have any of your carefully cultivated biases questioned or your beliefs shaken. Siiiiigh.
SolumAmbulo@reddit
I agree. But we really should apply that to people who seem to have original thoughts ... but don't, and simply quote back what they've heard.
Fair is fair.
wisconsinbrowntoen@reddit
No
cneakysunt@reddit
Yea, it does. OTOH I have been talking to my computers, cars, phones etc forever like they're alive and conscious anyway.
So, idk, whatever.
crazyeddie123@reddit
It. Is. A. Tool.
It is not a "partner" or a "teammate" or, God forbid, a "coworker". And naming the damned thing "Claude" gives me all the heebie-jeebies.
jerry_brimsley@reddit
That word reason is pretty loaded. I remember in elementary school it was always what made mammals/humans special was the ability to reason.
I find myself wanting to call its ability to know a platform and framework, and hear a set of circumstances, and then ____ about to find an answer given the set of circumstances, means the token generator argument is almost equally as “misleading” (using the mad libs fill in the blank instead of the word reason and misleading in quotes given the terminology topic).
Sure it’s outdated quickly if said platform is always evolving and new releases and it quickly becomes untenable, but I have had hands on experience where because I know 75 percent of something the LLM can fill in blanks from a scope of the platform and ___ (reason?) through what makes sense as its output given what it thought contextually…
I hope that makes sense, I’ve been a long time dev a while, but think I myself, fail to understand why reasoning as a term is the wrong word for it… I would not have proof but would not agree that EVERY SINGLE combination of events and things that lead up to a bug or troubleshooting is in the training (maybe LLMs do some edge cases post training work to then go sort those out?)…. But if it were truly the simple token generator thing I would expect that the second you throw a piece of criteria in the mix that meant a permutation it has never seen that it would just stop. I’d be interested to hear how that solution that it has given me that worked and was extremely nuanced and dynamic didn’t take some of what I would call reasoning… but I admittedly have ZERO formal training or education on this, and all of the above is my curiosity or opinion.
Debating my non dev friend who doesn’t use AI if LLMs were capable of reasoning left so much to be desired that I seriously am really wanting to get the experienced dev side of how I should be looking at the above if not reasoning and I will for sure change my messaging if needed
79215185-1feb-44c6@reddit
This just sounds like another "old man yells at clouds" thing.
Tooling exists to make you more productive. Learn how to use it or don't. It's not going to hurt you to learn new things.
Be more considerate that word choice isn't made because of what you feel. This kind of discussion is not much different than the master/slave blacklist/whitelist stuff that we just accept as time goes on. I have a coworker who will constantly "correct" me whenever I say block or allow listing (regardless of whether or not the term "backlist" has racist origins or not) and we're only 5 years separated by age.
wintrmt3@reddit
You can call people ignorant, but it just shows you don't understand LLMs at all. Everything it does is just predicting the next token of text, even if your UI hides it and interprets it as a very insecure tool call.
dotpoint7@reddit
Bold of you to tell people that they don't understand LLMs when your own understanding doesn't go beyond the absolute basics of what some YouTuber would tell you in a 3 minute video titled "How ChatGPT Works - Explained Simply".
wintrmt3@reddit
I've read Attention is all you need, did you?
Anomie193@reddit
Then you would know that not all transformers "predict thr next token."
wintrmt3@reddit
BERT is dead, diffusion models don't perform well either, all real world applications are based on next token prediction.
Anomie193@reddit
Way to be wrong about everything.
BERT-like (ModernBERT released late last year) models aren't dead. They're still relevant when you only want/need an encoder-only model and don't want a highly latent >1B parameter model.
Diffusion models and transformers aren't mutually exclusive (most diffusion vision models use ViTs now, instead of U-Net), and at least for vision tasks diffusion models aren't less capable across the board than autoregressive ones.
wintrmt3@reddit
Are you intentionally changing the topic? It's LLMs, and not encoder only ones, so none of that is relevant.
Anomie193@reddit
When you mentioned "Attention Is All You Need" as the basis of your knowledge on the topic, you expanded the conversation to transformers in general, not just current LLMs.
If the discussion is simply about today's LLMs, then it really doesn't make sense to reduce them to simply "predicting the next token", when there are so many training passes being done, including reinforcement learning on "reasoning" tokens.
wintrmt3@reddit
You are really throwing everything at the wall just so you don't have to say it's actually what they do at inference time.
Anomie193@reddit
Well, this is again untrue.
An MLM, like Modern BERT, isn't "predicting the next token" at inference time. It is predicting the masked token(s), which often are target classes.
The reason why I brought up MLM's is to illustrate that the "predicting the next token" part isn't really all that interesting. It is just one (easily scalable way) to set up an objective function for training.
MLM's don't do it and they are also LLMs. The only reason we don't use MLM's for SOTA LLM's is because they are harder to scale than decoder-only models.
The other interesting point is that to be able to predict the next token or masked token(s), there needs to be a generalizable semantic representation (and other abstracted internal) models. A lot of research being done is to understand these internal models.
And the reason I brought up reinforcement learning, constitutional learning, and RLHF is because they affect the output you get by changing the internal models. If it were indeed merely and reducibly "predicting the next token," you wouldn't expect that to be the case.
wintrmt3@reddit
That's a lot of of words for "Yes, that's exactly what GPT does".
Anomie193@reddit
No it is an educated, nuanced response. Something you're obviously not interested in.
dotpoint7@reddit
Oh wow I didn't know we had an expert here...
Yes I've read it, so what?
wintrmt3@reddit
So what do you think how LLMs work then?
dotpoint7@reddit
I don't know, at least not in the depth necessary for the current discussion and it seems like this is an open research problem. Thinking you know just because you've read attention is all you need is equivalent to claiming to know how human brains work just because you understand how neurons function - which in the context of cognitive science would be laughable.
Either way, reducing the whole topic to LLMs being "next token generators" is just too simplistic. And you thinking that reading a single research paper on the transformer architecture gives you any kind of credibility on the topic is laughable. To be clear, I'm not an expert in the field either so I'm intentionally not trying to make any claims of what the capabilities of LLMs are or how these fit in our current understanding of cognitive science, maybe you should try the same.
wintrmt3@reddit
It is the paper that the whole LLM industry is based on, you seem to be allergic to knowledge, but that's expected from an LLM cultist.
dotpoint7@reddit
"You seem to be allergic to knowledge" is ironic coming from a guy thinking that he knows everything after reading a single paper which explains the basics.
wintrmt3@reddit
It's not the basics, it's the whole thing. You obviously never read it or understand LLMs.
dotpoint7@reddit
wtf
po-handz3@reddit
Anyone who thinks LLMs are 'glorified text generators' is probably a engineer who's been given a data scientists' job and has no concept of the development that happened between original BERT models and today's instruct GPTs.
Terms like the ones you mentioned are used because simply saying 'they predict the next token' is incorrect. Just because you can push a few buttons in the AWS console and launch an LLM doesn't make you an AI engineer or a data scientist. It just shows how good OTHER engineers are at democratizing cutting edge tech to the point that end-user engineers can implement it without having any concept of how it works.
TheRealStepBot@reddit
100% by and large the connectionists have won and soundly so. The erm aktually it’s just a text generator crowd is extremely quiet in the ML space. Probably LeCunn is about the only anybody on about that anymore. And he or Meta haven’t contributed anything of value in some time so take it with a grain of salt.
The people who actually do ML and especially those who worked in NLP even in passing in the 2010s know just how incredible the capabilities are and how much work has gone into them.
There are a whole bunch of backend engineers who know nothing about ML picking up these trained models and using them and then thinking anyone cares about their obviously miserably under informed opinions. The people making them are rigorously aware in all it mathematical goriness exactly just how probabilistic they are.
It’s people coming from an expectation of determinism in computing who don’t understand the new world where everything is probabilistic. They somehow think identifying this non deterministic output is sort of gotcha when in reality it’s how the whole thing work under the hood. Riding that dragon and building tools around that reality is what got us here and as time goes on you can continuously repeat a very similar process again and again and yield better and better models.
If people haven’t played with Nano Banana yet, they really should. It gives a very viceral and compelling show of just how incredibly consistent and powerful these models are becoming. Their understanding of the interaction between language, the 3d world and the 2d images of that world is significant.
It might and day from the zany will smith eating pasta clip from 5 years ago and the exact same thing is playing out in the reasoning models it’s just much more challenging to evaluate well as it’s extremely close to the epistemological frontier.
Anomie193@reddit
LeCun is a connectionist as well. His criticisms of language models aren't criticisms of deep learning generally.
CrownLikeAGravestone@reddit
And let's be fair here; "LeCun defies consensus, says controversial thing about ML" is hardly surprising lol
po-handz3@reddit
This is a great point.
'It’s people coming from an expectation of determinism in computing who don’t understand the new world where everything is probabilistic. They somehow think identifying this non deterministic output is sort of gotcha when in reality it’s how the whole thing work under the hood.'
Its why our cheif architect engineer always suggests some new reranker algo or some new similarity metric or a larger model - when no, if you simply look at how the documents are being parsed you'll see theyre messed up, or indetical documents or like literally take 30 seconds to understand the business problem
nextnode@reddit
You are correct and this sub is usually anything but living up to the expected standards.
Xsiah@reddit
Let's rename the sub to r/HowWeFeelAboutAI
syklemil@reddit
/r/DiscussTheCurrentThingInYourFieldOfWork
I would kinda expect that previously there have also been waves of discussing free seating, single-room building layouts vs offices vs cubicles, WFH, RTO, etc, etc
Xsiah@reddit
Do you genuinely feel like you're getting anything of value from the continuation of these discussions though? Have you heard a new opinion or perspective on the situation that you haven't considered lately? I haven't, and I'm over it.
deZbrownT@reddit
Thank you, it’s been about time someone said this.
Western_Objective209@reddit
r/DoesAnyoneElseHateAI
StateParkMasturbator@reddit
There. That's the sub.
__loam@reddit
So true lol
ILikeBubblyWater@reddit
Boomers being afraid of the thing they dont understand
PothosEchoNiner@reddit
It makes sense for AI to be a common topic here.
mamaBiskothu@reddit
I can only wait for most posters here to be out of a job in this field. If you dont want things to change go wait tables or something.
Cyral@reddit
Every single day it's the same "Does anyone else hate AI??" thread. Someone asks "if AI is so useful how come nobody explains what they are doing with it?" Then someone gets 30 downvotes for explaining "here's how I find AI useful", followed by a "wow if you think it's useful you must not be able to code" insult.
CodeAndChaos@reddit
I mean, they gotta farm this karma somehow
Xsiah@reddit
It's the same thing over and over. If you use the search bar you can probably already find every topic and opinion on it.
Less-Bite@reddit
If only. It would be I hate AI or I'm in denial about AI's usefulness and potential
forgottenHedgehog@reddit
Seriously @snowe2010 - maybe it's time for some sort of moratorium?
HoratioWobble@reddit
We humanize things, especially inanimate objects all the time.
It's just how humans human.
mamaBiskothu@reddit
I wonder if this forum existed in deep south 200 years back what group the folks here would belong to.
BeerInMyButt@reddit
I'll bite. Please elaborate.
mamaBiskothu@reddit
"Why are we calling these slaves people?"
BeerInMyButt@reddit
I get that, I'm just wondering if there's a reason to draw the parallel?
mamaBiskothu@reddit
In my opinion the people who think modern day AI is not intelligent are the same delusional types who could not accept people of other colors are human in those times.
Mithrandir2k16@reddit
Yeah, some of my colleagues say "he" instead of "it" and that really rubs me the wrong way for some reason.
FourForYouGlennCoco@reddit
Exactly. People have been ascribing beliefs to search engines and intentions to recommender systems for as long as they’ve existed. You can say “TikTok knew I’d like this” and nobody bats an eye.
rfpels@reddit
Yes well… Any sufficiently advanced technology is indistinguishable from magic. And you know what happens then. We invent a bearded person in an unreachable place. 😜😎
arihoenig@reddit
LLMs absolutely reason.
They aren't just fancy predictive text. Predicting text isn't what an LLM learns, it is how it learns. It is the goal that allows the neural network to be trained.
It is astounding to me how many developers don't understand this.
WillCode4Cats@reddit
Absolutely.
So many people just think LLMs are nothing more than random word generators. While it is true that prediction is a large part of how LLMs work under the hood, there is clearly something deeper going on.
I think there are more parallels with the human and LLMs than many people might initially realize. For example, say I tell a story to another person. Let’s assume the entire story is about 3 minutes of length. Now, I do not know about you all, but I do not have the entirety of the story mapped out in my mind word for word before I start speaking.
Unless something is purely memorized, humans tend to kind of operate like LLMs in that we make a predictive assessment as to what we will say next in real time.
ChineseAstroturfing@reddit
Just because they appear from the outside to be using language the way humans do doesn’t mean they actually are, and that “something deeper is going on”. It could just be an illusion.
And even if they are generating language the same way humans are, while interesting, that still doesn’t mean anything “deeper” is going on.
WillCode4Cats@reddit
The purpose of language is to communicate. LLMs can use human language to communicate with me, and I can use human language to communicate with LLMs. I would argue LLMs are using language just like humans and for the exact same purpose.
Let me ask you, what do you is going in the human mind that is “deeper?” I personally believe one of the most important/scary unsolved problems in neuroscience is that there is absolutely zero evidence for consciousness at all.
So, while we humans (allegedly) are capable of deep thought and rational thinking (sometimes), we have no idea what is going on under the hood either.
Life as we know it could very well be an illusion too. Every atom in your body has been here since the creation of the universe. When you die every atom will be transfer to something else. So, what are we even? What is thought and consciousness truly are nothing more than just projections and illusions resulting from complex chemical and electrical processes?
ChineseAstroturfing@reddit
I don’t think anyone would argue with you there, though I’m not sure what your point is?
Absolutely LLMs are a technology that allows humans and computers to communicate using language.
However, there is nothing communicating with you. I get the sense that you’re mistakenly anthropomorphizing the output from the computer. The computer is not sentient. It does not possess agency. It is not a being of any kind.
This statement is false. I’m not sure how you were lead to believe that. Science doesn’t understand how consciousness works, but it’s a real measurable thing. It exists and there is evidence of it.
To get your answer, you could start by googling something like: “what does the human mind do besides language”.
After that, start to explore philosophy and theology more deeply.
Surely you don’t believe that the human mind is some how the equivalent of an LLM?
This is bordering in to r/iam13andthisisdeep territory.
WillCode4Cats@reddit
You wrote
My point was that LLMs are using language exactly like humans do for the same purposes that humans do.
Your sense is incorrect. I do not believe LLMs are sentient, possess agency, nor are a form of life in any formal definitions. Why are you arguing against claims I did not make?
True.
What unit is consciousness measured in? What tools do you use to measure consciousness? How can scientist be certain what they are measuring is objectively consciousness?
Now, you might find research pointing to correlates of consciousness like fMRI scans, EEG, etc., but correlates are not sufficient evidence that consciousness is causative.
Since consciousness can clearly be measured, then are animals conscious -- Crows vs. Dogs vs. Clams? Are any plants or fungi conscious at all? (My true opinions are that consciousness is a spectrum and not a binary state).
Nevertheless, I believe consciousness is real because I am, well, conscious. However, I cannot prove that you are conscious and vice versa in any objective sense. However, luckily humans operate under the fundamental logic that if I know I am conscious, and you say you are conscious, then I believe you and vice versa. Alas, that is not proof. Descartes had a similar idea, but considering I do not read philosophy, I couldn't know that.
Anyway, if you disagree, then you welcome to present evidence to change my opinion. After all, your Google skills are allegedly far superior to mine.
ChineseAstroturfing@reddit
You are far too emotional and long-winded.
Your initial claim was that something “deeper” was going on with LLMS. Explain what you mean in a reasonably concise way.
arihoenig@reddit
A NN can't learn (i.e. configure iste parameters) without some action that can be tested to measure an error. To make the concept clear let's take a simple use case.
In a machine vision application, the training activity is to correctly identify an image. In training mode the model makes a prediction about what the name of the object represented in the image is. This prediction is tested against a known result, and an error is measured. This process is run iteratively using a specific measurement technique with gradient descent and back propagation until the error hits some minima (the algorithms , number of iterations and acceptable minima are determined by the ML engineer).
In a LLM the same process is followed, but instead of training by producing a prediction of what object an image represents, the prediction is what the next token is (based on a presented set of input tokens).
In the case of machine vision, the model isn't learning how to predict an object from an image representation, it is learning how to classify images into objects in general, and the process of predicting what object an image represents, is the means of developing the ability of image classification. Likewise, a LLM isn't learning how to predict the next token, it is learning how to represent knowledge in general, by trying to predict the next token from a sequence of input tokens. Once the knowledge is encoded in the model, then; in inference mode, the model can generate information from a sequence of input tokens (aka "a question").
Synthesis of information from a question is exactly what biological neural networks do. Granted they accomplish the goal with a mechanism that is (in detail) very different to an ANN. Most notably biological NNs are (very successfully ) able to generate their own training data.
LLMs are able to generate synthetic training data for other LLMs, but introspective synthetic training is not something that currently works (model collapse risk is high) for ANNs (but is an active area of research).
r-3141592-pi@reddit
This deserves to be the top answer.
During pretraining, models learn to predict the next word in text. This process creates concept representations by learning which words relate to each other and how important these relationships are. Supervised fine-tuning then transforms these raw language models into useful assistants, and this is where we first see early signs of reasoning capabilities. However, the most remarkable part comes from fine-tuning with reinforcement learning. This process works by rewarding the model when it follows logical, step-by-step approaches to reach correct answers.
What makes this extraordinary is that the model independently learns the same strategies that humans use to solve challenging problems, but with far greater consistency and without direct human instruction. The model learns to backtrack and correct its mistakes, break complex problems into smaller manageable pieces, and solve simpler related problems to build toward more difficult solutions.
When people claim that LLMs are just fancy "autocompleters", they only reveal how superficial most people's understanding really is.
Perfect-Campaign9551@reddit
This.
maccodemonkey@reddit
I think the problem is that reasoning is a gradient. My calculator can reason. A Google search is reasoning about a database. What do we mean by reason?
Again, this is sort of retreating behind abstract language again. Learning is an abstract concept. When I give my code to a compiler is the compiler learning from my code? Is what it outputs an intelligence? Is a database an intelligence? Does a database reason when I give it a query?
I think you could make a case that a SQL database potentially does reason, but then it sort of calls into question why we're putting so much emphasis on the term.
y-c-c@reddit
Reasoning models have a specific meaning in LLM though. Maybe in the future the term will be deprecated / out of fashion as we have more advanced models but as of now it does mean something very specific about how the LLM is trained and works.
Basically the LLM is trained to list out the reasoning steps, and if it doesn't work it's capable (sometimes) to realize that and backtrack the logic. People who know what they are talking about are specifically talking about this process, not trying to anthropomorphize them.
maccodemonkey@reddit
And indeed there is still significant debate on if a reasoning model can reason (along with the entire meta debate about what reasoning is.) To the OP's point, throwing a loaded term onto a product does not mean the product is doing whats described.
What a "reasoning model" is doing also isn't new. (Create output, test output, create new output.) Prior ML models could do similar things. There are a ton of traditional algorithmic systems that can do similar things. Even in the machine vision space there are tons of traditional algorithms that build on their own output for self improvement in order to process the next frame better.
Maybe we should retcon all these systems as intelligent and reasoning. But it's hard to see what line has been crossed here. Or if we should give LLMs some sort of credit for doing something that isn't particularly new or novel.
y-c-c@reddit
What do you propose then? Invent new English words? I don't think it's that wrong to use words to describe things.
This is highly reductive. It's like saying "these algorithms are all calculating stuff anyway". It's very specific to how these LLMs work. But yes obviously there are similar ideas around before. It's how you use and combine ideas that give rise to new things. I also don't think you can call something like a computer vision algorithm "reasoning" because they don't solve generic problems the way that LLMs are trained to.
maccodemonkey@reddit
I'm going to say a few things that will be controversial - but I'll back it up. (I'm going to split, Reddit seems to be enforcing a limit.)
LLMs as a whole tend to be cosplayers. Like great, it gave you a log of all its reasoning - but that doesn't mean it's actually reasoning. That doesn't mean it understands the things it wrote out. It's doing an impression of someone who planned out parts or all of the problem. It's putting on a show. So simply saying "It put out things that look like reasoning!" is insufficient for saying an LLM is reasoning.
People run into this a lot. You tell the reasoning model to make a plan and to not touch file X. The reasoning model creates a plan and says it will not touch file X. You tell the reasoning model to execute the plan and it still touches file X. You get mad at the reasoning model and ask it why it touched file X when it said it would not. It touched file X because it never understood or reasoned about file X. All it was doing was trying to generate output that made you happy. The model is being rewarded for the appearance of reasoning, not actual reasoning.
The nuance here is sometimes the appearance of reasoning is good enough. But - to OPs point - we're using a term that maybe should not apply. An LLM is acting, which may be good enough, but we shouldn't pretend it's not anything more than an emulator.
(There's more nuance about the sort of neural nets something like an LLM will form for solving some math problems, but I think at a general high level we can say they don't perform human reasoning.)
maccodemonkey@reddit
A lot of the boosters have sort of started to cede that these things aren't reasoning, which is why the industry is moving away from AGI. It's causing all the timelines to AGI to extend again and people in industry to back away.
There is the Apple paper on how reasoning models don't reason (The Illusion of Thinking.) I think that paper is right, I think the moves in industry are only proving that paper more right. But it's had a lot of dirt thrown at it.
Arizona State University published a paper finding that reasoning models may be producing "superficial" plans and are not engaged in real reasoning. And that they fail when they depart from their training data.
JAMA did a study that found even the reasoning models collapsed when simple changes were made to medical questions. That implies these models are not reasoning.
There's also other anecdotal evidence. These models have read every book on programming and ingested every piece of code on Github. They should be master developers. But instead we typically compare them more to junior developers. Does that make sense? Does that sound like these models are reasoning? Have they been trained on same bad code? Probably. But if they could reason wouldn't they know that code was bad?
Am I being reductive? Yes. Because - to OPs point - the term reasoning has been clearly over applied and the only way I can make the term reasoning make any sense is to reduce what it means. There are times when a model that is pretending to reason can be useful, but it should not be confused with actual reasoning. Or we need to redefine the term reasoning.
"Reasoning" is a marketing term. It's not yet clear (but is becoming more suspect) the reasoning models reason. Even worse, people have been convinced to ignore the issues with stuff like "well it's like a junior." It read every programming book on the planet. Why is it a junior?
An LLM cannot solve generic problems, only what it was trained on or what it can assemble for its training. Very similar to how a lot of computer vision algorithms work.
arihoenig@reddit
I am referring to inductive and abductive reasoning. Deductive reasoning is ostensibly something that a SQL database engine could be considered capable of, and that certainly, a simple hand-held computer chess game, implements deductive reasoning, so I assumed that wasn't the form of reasoning being discussed.
maccodemonkey@reddit
Inductive and abductive reasoning are not unique to LLMs either. Nor are they unique to ML.
arihoenig@reddit
Of course they're not unique to LLMs, in fact, this entire discussion is about how well LLMs mimic biological neutral networks.
maccodemonkey@reddit
Does it mimic biological neural networks or does it mimic human thinking?
Going back to what OP is saying - there's a lot of terms being inserted that are not meaningful or important.
Neural nets are not new. They're decades old. They're just a raw building block. Having a neural network does not necessarily imply complex reasoning or human like reasoning.
Terms like biological are floated to make the tech seem impressive that aren't really meaningful.
arihoenig@reddit
"Does it mimic biological neural networks or does it mimic human thinking?"
What's the difference? Operation of a biological neural network is thinking. I think that the idea of selecting humans from the list of thinking beings is arbitrary. For example, many animals possess all of the observable attributes of thought, a notable example being corvids that have been shown to be able to do mental arithmetic.
FourForYouGlennCoco@reddit
Sometimes. Most of the brain’s activity at any given time has nothing to do with conscious thought. There are entire regions of the brain, like the cerebellum, that have no role in “thinking” at all, in the way we typically mean it.
I agree that humans are not the only animals capable of thinking, and that in principle a machine should also be capable of it. But it’s not the case that any active neural network is thinking. There is some combination of connectivity and functional state that is necessary, and we aren’t sure exactly what.
maccodemonkey@reddit
Which again - to the OP's original point - we're now once again shuffling using terms.
A calculator can do arithmetic. So what?
I'm trying to get to why the term biological is relevant at all. It doesn't seem like it is.
Again - what does this even mean? By this metric a calculator thinks. To the OPs point - either we're using the term "thinking" wrong, or the term is meaningless and we shouldn't be giving it any weight at all.
arihoenig@reddit
A calculator is constructed and/or programmed by a NN, to do arithmetic. Corvids synthesized their own training data and taught themselves how to do arithmetic. See the difference? A calculator can't synthesize a training dataset and then train itself to do arithmetic. Neural networks can do that, and LLMs can (and do) generate synthetic datasets used to train other LLMs.
maccodemonkey@reddit
So what. It still does arithmetic.
But why would you do that. Is that any more thinking that what the calculator does? Is it just what the calculator is doing with extra steps?
Which is not proof of thinking. That's a program generating output and then feeding that output into another program. It doesn't disprove that there is thinking going on, but it certainly doesn't prove it.
If I write a program that generates code and then feeds it back into a compiler to create a new program I haven't built a thinking machine.
arihoenig@reddit
I am tiring of this discussion. Your entire response pattern seems to be "so what?
A calculator can't be presented with a problem (a problem is simply a set of data) and it cannot then program itself to solve that problem. A LLM can do this. As can a corvid and as can a human. That pretty clearly satisfies the definition of what inductive and abductive reasoning is and a calculator can't do either of those.
maccodemonkey@reddit
What do you think the calculator is doing to the underlying state machine in your computer?
arihoenig@reddit
A calculator isn't using inductive reasoning to figure out how to do math. For example a corvid is presented with the classic treat in a tube too narrow to put its head in, with a treat floating in water at the bottom of the tube. It is also presented with a series of differently sized rocks and it isn't told (we don't know how to speak corvid) that the goal is to minimize the number of rocks to get the job done, it "simply" synthesizes that requirement from its desire to get the treat as soon as possible. The corvid then selects the rocks in order from biggest (most displacement) to smallest in order to retrieve the treat with the minimum number of displacement operations.
No one trained a corvid to do this (these experiments were repeated with random wild corvids), the key element that confirms that the corvid was thinking, was that it synthesized the training data to program its own neutral network with the ability to optimally select rocks for fastest retrieval (which requires a fair amount of arithmetic).
maccodemonkey@reddit
Again, I'll go, so what?
No one "trained" my calculator that 9 x 9 was 81. I don't look at that and go "wow, I didn't teach it this, it must be learning!"
Again, if you want to say that's thinking and a calculator also thinks, that's fine. What I'm struggling with is how an LLM has crossed some threshold here.
In since I think you're actually going in circles I'll give two more examples to speed this up:
What you're describing is also very similar to LLVM. LLVM takes an input (the LLVM byte code) and produces an output. But, not a direct translation. It produces an optimized output. It has to "reason" about the code, "think" about it, and reassemble the output in a new form.
Is LLVM intelligent?
Another example. I work as a 3D engine developer. My whole job is writing code that writes other code on the fly that gets uploaded and run on the GPU. I need to take in a scenario in the scene that is loaded, and write code that writes code that lets the GPU render that scene. I would never argue that's AI. (Maybe I should? Maybe I'd get paid more?) You've described that as a sign of a system that is reasoning. I work on systems like that and would never argue that are reasoning. Again, maybe I should and I should go ask for more salary.
The difference between these scenarios and your scenarios is transparency. I don't think LLVM is thinking because I can see the code to LLVM. I don't think my on-the-fly GPU code generator is reasoning because I can see the code to it. I wrote the code to it.
LLMs are mostly black box, and the scale is larger. So we can throw around the terms "biological" and "neural nets" and in since we can't actually see inside that well we can say they're "thinking". It's the old "Any sufficiently advanced technology is indistinguishable from magic" thing.
And to OP's point, yes, maybe we should be taking the magic out of these things. But also, the rational you've applied for if something is thinking applies to tons of other processes. So the other option is maybe a lot of stuff in computers is thinking and it's actually not all that special.
arihoenig@reddit
Someone built the calculator specifically to do arithmetic. It categorically didn't learn arithmetic. The NN that built the calculator developed the concept of arithmetic and then that NN created a device designed to a subset of arithmetic with much better energy efficiency than itself. That is thinking.
I'll end this here. If you are unable to differentiate between a device built by a NN and the NN itself, I am forced to conclude that you are an entity that is incapable of deductive reasoning.
maccodemonkey@reddit
NNs don't understand arithmetic out of the box. Heck, LLMs have a very limited understanding of arithmetic. Suggesting that NNs implicitly understand arithmetic is highly misleading.
With enough training, a purpose built NN could understand arithmetic. But this training process is the same thing as compiling a calculator from code.
So again, not thinking. However you try to abstract it you still arrive at not thinking. Even if an LLM produced code that built a calculator it was trained to produce that code. It didn't reason it out of nowhere.
TheMuffinMom@reddit
So strap in, the problem is how you label understanding and a little bit of mimicry, because its trained on such diverse datasets at this point aswell as having its grounding its actually quite far along, but it is still only analogs for understanding, the models weights are updated only during training, this is crucial this is long term learning, you cannot for example take a trained model, and teach it a new skill solely off context engineering or prompt engineering, if its a simple thing sure but we are talking complex understanding, understanding comes from our unique human ability to take these “gist” like connections and make them have these invisible links. We dont “learn” every word we read we try to build our world model and our “understanding” if you counter this to standard LLMs they “learn” but they dont understand, they update their weights to respond a certain way based on the inputted prompt, CoT is a cool “hack” to also have an analog for “thought” and system 1 vs system 2 thought but all it does is give the model more tokens of the problem to reiterate and rethink (llms are autoregressive meaning they go from left to right one word at a time calculating the token then calculating the most likely next word based on its context and its attention heads and a couple other metrics). While alot of people talk about the “black box” that is behind the weights of training AI this way we already know that they dont quite understand (someone else already mentioned my thoughts on this that the black box is overblown, its mostly speaking on emergent capabilities and that is still just a byproduct of the models weighths from training) , in a purely book driven realm they are intelligently smart but anything taking complex creativity or understanding of the world the models fail to build specific connections and as i stated earlier if its a post training model it is not able to understand or have cognition in no way shape or form, if you wanna try go for it but its just not possible with the current architectures. its the same reason labs like gemini and the robotics labs and jensen are making world model robots, it is that they believe this aswell that by scaling alone we wont reach our goals, maybe some semi form of AGI but without understanding its hard to say, it has to have a deep rooted world view to understand along with it being able to progressively learn as it grows its world view. now we can use things like RAG to give psuedo understanding but the context limits of all the models under 1 millilon tokens just cannot handle any decent long term, you can nightly finetune an LLM like its going through “rem” sleep, this sort of works but its not actively understanding throughout its day and only “learns” stuff when it sleeps.
Unsupervised/RL learning is the main pathway forward to let the models actually build that world model.
TalesfromCryptKeeper@reddit
Anthropomorphizing has been an issue with CS from its earliest beginnings, I'd argue. In the case of LLMs its now being actively encouraged to make people develop an emotional connection with it. Sells more product and services, discourages genuine criticism, and inflates capability to encourage VC to invest in it.
When you see it for what it is, it's a nasty campaign.
FourForYouGlennCoco@reddit
The marketing campaign is real (and annoying), but people would be using anthropomorphic language regardless because we do it with everything. Google told me the population of Berlin is 4 million, Netflix wants me to watch their new show, TikTok always knows what I like. These are natural and common ways to speak and since LLMs are mostly used as chatbots it’s no surprise we use conversational metaphors for them.
BeerInMyButt@reddit
I can't quite put my finger on why, but those uses of language don't feel as much like a misrepresentation of what's happening behind the curtain.
The organization that is netflix is pushing me to watch this thing because it aligns with their business goals; the organization that is tiktok has honed an algorithm that comes up with stuff I like and it's super effective.
I hear people reasoning about LLMs like "maybe it just thought that..." as if they're reverse-engineering the logic that made it come to a conclusion. But that anthropomorphization isn't an abstraction, it's a pure misrepresentation. There's no way to massage that language to make it true.
TalesfromCryptKeeper@reddit
Indeed. It's just how humans work and try to make sense of things (hell it's why we project human reactions and emotions on pets!). I don't have a problem with that honestly, it's when lobbyists take the next step into "Hey this AI has real feelings >> it learns just like a human >> which is why you should let us get your private healthcare data or scrape your art" that's when it gives me a really gross feeling in the pit of my stomach.
Zealousideal-Low1391@reddit
This is exactly what I tell people too. Go watch videos of people from the perceptron era. Some of the claims are exactly the same, we just have updated terms. Some are even wilder than what we say now.
And this was a model that could not XOR...
shrodikan@reddit
AI can infer context from code. It can explain to you what the code means. It "thinks" about what is going on using Chain-of-Thought. Copilot can "understand" where you are going. LLMs can call tools when the model thinks it could be useful. Calling neural networks that have internal monologues, call tools and iterate autonomously "glorified text generators" is a rather dated understanding of the current tech.
ActuallyFullOfShit@reddit
You're complaining about nothing. There are plenty of "reasoning" type problems that LLMS can answer via generation simply because of the massive data they're trained on (they essentially have memorized the answer in an abstracted form).
What's even the point of this post? To sound smart? You really worried about this?
epelle9@reddit
Touch some grass..
Its really not that important..
mamaBiskothu@reddit
It is to them because most folks here probably had enough intelligence to realize deep down that theyre all useless people writing complicated code to keep their jobs. But they were generally delusional enough that they pressed this instinct deep down. The very thought that something not human can produce code better than most of what they wrote is now breaking all that suppression. I really cant wait for a year and see the implosion of every boomer in this sub to be out of a job.
HaykoKoryun@reddit
LOL, you probably have near zero understanding of what programming is to come up with a statement like this. I have yet to see LLMs successfully tackle any programming task that is actually difficult, but not because it's just tedious.
It's not just programming either. Drift away from standard concepts and you'll need to prompt until the witching hour to get it to generate images, by which time you probably would have learnt enough Photoshop to generate something close to what you want, and you would have improved your own skills in the process.
mamaBiskothu@reddit
LLMs are a literal intelligence multiplier. I have never seen a good engineer not become more productive with it. But, all those pretentious know it all engineers, who no doubt were knowledgeable, but were some of the most toxic, counterproductive people I've worked with, who create their own messes and are some of the most egoistic pieces of work, theyre always the AI doomers. Consider again that you were just a moron who got a job because you learned programming but you were never actually smart. Thats why the LLM reflects a monkey back.
Droi@reddit
Don't be "diametrically opposed" to a "purveyor" of ranting who is worried the extremely inferior machine will take his job and thinks he can somehow stop it by convincing people to use the "correct" words.
Due_Answer_4230@reddit
They do reason using concepts. They do understand. The research has been clear, and it's why nobel laureate Geoffrey Hinton has been running around sounding the alarm 24/7 lately.
A lot of people on the internet think they know better than him and the researchers looking into conceptual thinking in LLMs.
deZbrownT@reddit
Oooo, really, wow, interesting!
Clitaurius@reddit
As a software engineer with 16 years of experience I find LLMs beneficial and can leverage them to be more productive. My personal opinion is that any experienced software engineer can and should find ways to leverage LLMs.
skeletordescent@reddit
7 YOE here. I’ve been saying this whole time LLMs don’t have cognition, they can’t understand and that we don’t even have a good model ourselves for what cognition actually is, let alone non-human cognition (which I say is what machine cognition would be). The glorified auto-correct is an apt analogy. Personally I’m trying to detach myself from these tools, in terms of not letting them actually do the code writing part. I’m losing my sense of how my own codebase works and it’s making things harder not easier.
agumonkey@reddit
I don't know ML, nor GPT internals for real. As I see them they are very**n advanced very-large-dimension parameters markov chain generators plus attention mechanism to prune relationships.
The thing is, it can relate low level symbols with higher level ones, at non trivial depth and width. So even if it's not cognition per se.. it falls between dumb statistical text output and thinking. I've asked these tools to infer graphs from some recursive equations and it gave me sensible answers. I don't think this sort of question has been asked on SO, so it's not just rehashing digested human contributions.
The ability to partially compose various aspects and abstraction level and keeping constraints valid enough across the answer is not far from reasoning. A lot of problem solving involves just that, exploring state space and keeping variables/subset valid across the search.
Where I see a failure is that, usually when we think we have this strange switch between fuzzy thinking to precise/geometrical coupling of ideas. We reject fuzzy / statistical combinations, we really want something that cut between true or false. GPT don't seem to be able to evaluate things with that kind of non linearity.. it seems (again, not an ML guy) to just stack probabilities.
my 2 cents
HelloSummer99@reddit
LLMs do nothing else but return the most statistically relevant token. Trouble is, most use cases, including programming require much more thought than that.
CrownLikeAGravestone@reddit
Your position on this is no better informed than theirs, but you're the one trying to say that you're objectively correct.
That makes this last sentence here:
pretty hypocritical.
Due_Helicopter6084@reddit
You have no idea what you are talking about.
'experienced dev' does not give any credibility to answer.
AI already can raeson and understand intent - we are way past predictive generation.
kalmakka@reddit
I would be fine with people saying LLMs "understand", "reason" and "suggest" if they also said "blatantly lies to your face" instead of "hallucinates".
Key-Alternative5387@reddit
Eh. So I worked in a cognitive science lab with some overlap between brain function and AI.
I guarantee the brain doesn't function exactly like an LLM. Backprop and transformer networks are fairly different. Over focusing on that isn't useful for good AI.
That said, there's enough emergent structures in neural networks that I consider it within the realm of possibility that AI is sentient to some degree. Also notable is that neural networks can theoretically simulate ANY function, so it could do something similar to a real brain and happens to be structured kinda sorta like one.
EX: We know the base layers of vision in flys from electrode experiments -- the neurons activate on linear light filters. CNNs always recreate these filters as their base layer with no prompting.
My personal definition of consciousness is something that has a sense of self preservation and is aware that it exists. LLMs roughly appear to have both qualities.
Lastly, the brain is kinda fuzzy and still mostly a blaco box, as is the way that humans like to separate what we consider conscious and what we don't. We do it based on what we observe externally and by feel and LLMs are quite convincing. What's the functional difference between a person and a perfect imitation of a person?
BothWaysItGoes@reddit
No, I am absolutely not troubled with it, and I would be annoyed by anyone who is troubled with it. I do not want to argue about such useless petty things. We are not at a philosopher's round table, even arguing about variable names and tabs vs spaces would be more productive.
Neat_Issue8569@reddit
I think this is pretty shortsighted to be honest. Anthropomorphisation of LLMs is a serious problem. People are forming intimate connections with these sycophantic chatbots. Teenagers have been using them as therapists with fatal results as we've seen quite recently in the news.
If the general public were a bit more clued up on the mechanics of LLMs and their inherent architectural limitations, they would be less prone to using them in safety-critical situations like talking to the mentally ill or advising somebody on medical matters. Marketing puffery where LLMs are concerned can have disastrous real world consequences.
cpz_77@reddit
That’s a totally valid concern and very sad things like that happened, but I think it’s also sad that apparently our society cannot handle people using such terminology. There are many nuances in all parts of life that you must understand if you have any hope of surviving for any length of time. And of course there are always examples of what happens when people don’t.
as others have pointed out we’ve said things like “computers are thinking” for many years btw, I guess the main difference is AI has better ability than just a plain computer to convince people of crazy shit
The fact that we can’t use figures of speech without some otherwise intelligent people going off the deep end and thinking this is somehow another human being they can form an emotional connection with or rely on for critical life-changing advice because they heard the term “reasoning” or “thinking” in the context of computing/AI is depressing. Obviously people with certain medical issues may more predisposed to such scenarios and I don’t want to be insensitive to them but I have heard too many stories already of otherwise healthy, normal people being convinced
And this is all regardless of the argument about whether using the terms “reasoning” or “thinking” are technically correct or not…tbh that really doesn’t matter for the sake of this discussion - it’s a figure of speech - we use them for the sake of getting a point across efficiently, and the ability to understand when someone is just using a figure of speech is a pretty critical life skill, you won’t get too far without it.
NowImAllSet@reddit
I'm with the other commenters, your opinion feels pedantic and pretentious. It feels like you need to apply your superior critical thinking skills in a more productive direction.
DeGuerre@reddit
The entire computer business is based on metaphors. I mean, think about why we call them "trees" and "files" and "windows". Hell, words like "printer" and even "computer" used to refer to human jobs.
But it's true that AI is one of the worst offenders, and has been for decades, ever since someone coined the term "electronic brain". "Perceptrons" don't really perceive. "Case-based reasoners" don't really reason. Even "neural network" is misleading; they are inspired by neurons, but they don't really do or simulate what neurons do.
Keep reminding people of the truth. It's not a losing battle, but it is a never-ending battle.
SmegmaSiphon@reddit
I've noticed this too, but not just in a "we're being careless with our vocabulary" kind of way.
I work with a very savvy, high-talent group of enterprise architects. My role is far less technical than theirs - while I'm somewhat extra-technical for someone in my own role, what knowledge I possess in that realm is piecemeal, collected through various interests or via osmosis, rather than an actual focused field of study or research.
However, I hear them confidently say that the later LLM iterations (GPT 4 and above, Claude Sonnet 3+, etc.) are "definitely reasoning," even going as far as saying that LLM architecture is based on neural networks and the way they "think" is not meaningfully different from our own post-hoc rational cognition of conditioned stimuli response.
But when I use these tools, I see the walls. I can see that, even when the responses seem extremely insightful and subtle, it's still just the operation of a predictive text model filtered through an algorithmic interpretation of my collective inputs for tone matching. When pushed, the reasoning still breaks down. The tool still struggles mightily with modeling abstract connections across unrelated contexts.
It might be doing the best it can with what schema it can form without actual lived experience, but lived experience counts for a lot.
Without lived experience, all an LLM can do is collate keywords when it comes to schema. It has no known properties for anything, only character strings linked by statistical likelihood.
My attempts to convince coworkers of this have put me at risk of being labeled a luddite, or "anti-progress." They're thinking I fear what I don't understand; what I actually fear is what they don't seem to understand.
TheEntropyNinja@reddit
I recently gave a presentation at work about practical basics of using some of our newer internal AI tools—how they work, what they can do reliably, limitations and pitfalls of LLMs, that sort of thing. During the presentation, a colleague of mine made a joke in the meeting chat: "Dangit, Ninja, you're making it really hard for me to anthropomorphize these things." I immediately pounced. "I know you're making a joke, but YES, THAT'S EXACTLY WHAT I'M TRYING TO DO. These are tools. Models. Complex models, to be sure, but they are not intelligent. When you anthropomorphize them, you start attributing characteristics and capabilities they don't have, and that's incredibly dangerous." It led to a productive discussion, and I'm glad I called it out. Most of the people I presented to simply hadn't considered the implications yet.
The language we use drives our perception of things. Marketing relies on that fact constantly. And the AI bubble grew so big so fast that we find ourselves in a situation where the marketing overwhelms even very intelligent people sometimes. It's not just the C suite they're aiming at—it's all of us.
The only thing I know to do is to talk about it with as many people as I can as often as I can and as loudly as I can. So that's what I do. Fortunately, I work with a lot of incredibly smart people willing to change their views based on facts and data, and I think I've done some good, but it's an ongoing struggle.
Perfect-Campaign9551@reddit
It's literally called Artificial Intelligence.
TheEntropyNinja@reddit
Unfortunately, the name is wildly inaccurate for the technology that currently exists, which is part of the problem.
Fancy-Tourist-8137@reddit
Are you one of those guys that say “we don’t have Artificial intelligence, we have machine learning”?
The name is not inaccurate.
How can you claim to have given a presentation on the topic yet not even understand what “artificial intelligence” means?
The term has been accurate for decades, it refers broadly to machines performing tasks that normally require human intelligence, whether that’s rule-based systems, decision trees, or neural networks. The fact that LLMs have recently become more capable doesn’t suddenly make the name misleading.
Artificial intelligence isn’t supposed to mean the same thing as human intelligence. It refers to machines that simulate aspects of human intelligence.
beachcode@reddit
Make the prompt include directives asking it to explain why, offer a few alternatives along with list of pros and cons for each alternative, to refer to sources for further reading, and so on.
If you can see the reasoning, isn't it reasoning, at least in some sense?
Bakoro@reddit
Most developers don't actually know how LLMs work.
If you actually understand how they work, you understand that they are not just text generators. "Token prediction" is a gross oversimplification akin to "draw an oval, now draw the rest of the owl".
The problem with people talking about AI, is that they use words with confidence and declare things with certainty while at the same time they refuse to acknowledge or use falsifiable definitions of the words.
I'm not being flippant or just navel gazing when I ask what do you mean by "understand", or "reasoning"?
Knowledge and understanding are not binary things, they are highly dimensional spectrums. "Reasoning" is a process.
People conflate these terms with self aware consciousness, but they are not the same thing.
We use words like "understand" and "knowledge" and "skill" because those are the appropriate words to describe things, they aren't metaphors or analogies.
When it gets down to it, "understanding" is just about making connections. You "understand" what a dog is because you recognize the collection of features. If you see dogs, you can learn to identify dog shaped things. If you've heard a dog barking, you could learn to identify dog barking sounds. If I describe a dog, you can recognize it by the collection and sequence of words I use. If I mime dog behaviors, you'd probably recognize dog behaviors. What more is there to "understanding"?
A multimodal LLM can identify dogs, describe dogs, generate dog pictures. By what definition does the LLM not "understand" what a dog is, in any meaningful, verifiable way?
You can be a fully formed conscious person and lack understanding in a subject while being able to regurgitate words about it.
A person can memorize math formulas but not be able recognize when to apply them if the problem isn't set up for them and they aren't told to use the formula.
You might be able to do the process for the calculation, but not understand anything about the implications of the math being done.
How do we usually determine if people understand the subject material a class?
With coursework and tests.
It's good enough for humans, but suddenly it's not good enough when testing a computer system.
Within a domain, the computer system can do all the same tasks the same or better than most people, but people want to say 'it doesn't understand", without providing any alternative falsifiable mechanism for that determination.
If you make the problems harder and more abstract, it still does better than most people, right up until you reach the limit of the system's ability where it's not as good as the absolute best humans, and people go "aha!" As if it didn't beat +90% of the population.
"Understanding" can mean different things, and you can "understand" to different degrees.
If you use testable, scaling definitions, the LLMs have to have some measures of understanding, or else they would not work. They don't have infinite knowledge or infinite understanding, and they don't continually learn in real time. They are not conscious minds.
TheGreenJedi@reddit
Honestly when they pretend it's demonstrating reasoning is more ridiculous to me
JamesMapledoram@reddit
I think it's because a lot of devs don't actually understand what you're asking - and who cares?
You might be able to set up Databricks clusters, wiring up training/inference pipelines and build a RAG, yet not be able to give a detailed walkthrough of how a CNN, transformer, or hybrid model works at the algorithmic level - and does that actually matter if it's not your job? I don't know... not sure this troubles me for the average dev honestly. I'll be the first to admit, I don't have a deep algorithmic understanding either and I've been an engineer for 20 years. My current job doesn't require it.
A month ago, I was voluntold to give a 3-hour talk to high school students on the history of AI. I started with AlexNet, talked about CUDA and how Nvidia help'd propel everything, explained CCNs with diagrams, showed how backpropagation works with a live classroom demo. I actually learned a lot - and realized, there are a lot of things I don't understand in the layers I never work with.
Perfect-Campaign9551@reddit
If I use an LLM agent to help write some code, and the code has a compilation error, and the agent sees the error and finds a way to correct it, how is that not reasoning?
It wasn't programmed to know how to do that, it was taught.
Yes, it's reasoning. Yes it obviously does understand there is a problem
Sure. It's not sentient. But it's definitely reasoning
Blasket_Basket@reddit
I mean, you seem to be taking a fundamental position on what LLMs can't do that is at odds with the evidence. I'm not saying their sentient or self-aware or anything like that, that obviously isn't true.
But reasoning? Yeah, they're scoring at parity with humans on reasoning benchmarks now. I think it's fair to say that "reasoning" is an acceptable term to describe what some of these models are doing given that fact (with the caveat that not all models are designed for reasoning, this is mainly the latest gen that scores well on reasoning tasks).
As for "understanding", question answering has been a core part of the field of Natural Language Understanding for a while now. No one found that term controversial a decade ago, why now? It seems a bit ironic that no one minded that term when the models were worse, but now object to it when they're at or above human level on a number of tasks.
Humans have a tendency to anthropomorphize just about everything with language anyways, and if that's a pet peeve of yours that's fine. If your argument is also grounded in some sort of dualist, metaphysical argument that that's fine too (although I personally disagree).
Overall, I'd suggest that if we're going to try and tell people why they shouldn't be using terms like "reasoning" to describe what these models are doing, then it falls on you to 1) define a clear, quantifiable definition for reasoning and 2) provide evidence that we are meeting that bar as humans but LLMs are not.
You've got your work cut out for you on that front, I think.
xternalAgent@reddit
Your PR reviews must be a lot of fun for all your teammates
scodagama1@reddit
and what alternatives to "understanding", "reasoning" and "suggesting" would you use in the context of LLMs that would convey similar meaning?
tmetler@reddit
Use the same words you would use to describe knowledge in a book.
I'm looking for a solution in the book: The agent is synthesizing a solution.
This book has good information: The agent provided good information.
There's good knowledge in this book: The agent surfaced good knowledge.
FourForYouGlennCoco@reddit
Why do we have to restrict ourselves to this pre-approved list of words?
Interacting with an LLM isn’t like interacting with a book. I can’t ask a book a question and get a response. So, first of all, we can and do use language like “suggest” for authors or books (“‘How to Win Friends’ suggests learning people’s names and repeating them often”), and second, it’s more natural to use conversational metaphors for something you can interact with.
tmetler@reddit
You can do whatever you want. I'm responding to a comment asking for alternatives. These are alternatives.
Sheldor5@reddit
statistics-based text generators ... not that hard
BuzzAlderaan@reddit
It rolls right off the tongue
Sheldor5@reddit
that's the only thing LLMs do if you didn't know LOL
scodagama1@reddit
And formula 1 bolid rides in circles using power of exploding chemicals yet you don't call them "combustion-engine based conveyance" in everyday speech, do you?
Dr_Gonzo13@reddit
What do you call Formula 1 cars? I don't follow the sport so I had no idea there was another term.
scodagama1@reddit
Actually it's a car, sorry. In my native tongue we have a different word for super fast race cars ("bolid") but I see it has French origin and is not actually used in English
Dr_Gonzo13@reddit
🤣
Nilpotent_milker@reddit
Humans are just DNA replicators if you think about it.
valence_engineer@reddit
That’s a lot broader of a meaning since a random if is that as well. It’s like describing planes as moving things. Technically correct but useless.
GammaGargoyle@reddit
It’s called processing or computing. The ML industry gives everything human, unscientific terms and we just adopt it. It’s marketing.
scodagama1@reddit
"Processing" and "computing" is too generic, computer computes, duh. Anything digital is computing, kid learning elementary school math is also computing. Language should be useful, using generic terms in everyday speech would be tiresome.
Car drives, bicycle rides, human walks, airplane flies and ship sails. Yet they all merely "move" but if giving these terms specific more narrow verbs was useful to convey more meaning, why not?
mxldevs@reddit
But is your process of reasoning and thinking really that much different from LLMs?
What would you say is the difference between how you come up with an answer to a question, and how LLM comes up with an answer to the same question?
ieatdownvotes4food@reddit
LLM reasoning is wrapping iteration loops around LLMs.
One step leads to another
y-c-c@reddit
I posted in another comment but reasoning models have a specific meaning in LLM. People who know what they are talking about is referring to the specific process these types of LLMs arrive at the conclusion. Maybe in the future the term will be deprecated / out of fashion as we have more advanced models but as of now it does mean something very specific about how the LLM is trained and works.
That said AI bros have a history of abusing terminology anyway. I still find it funny they still use the word "tensor" to refer to any multi-dimensional array (which is incorrect).
bloudraak@reddit
Define reasoning if it’s not the drawing of inferences or conclusions through reason, and reason being a statement offered in explanation.
And how is this different than when humans reason?
przemo_li@reddit
Machine spirit priests are having their best days in their life's.
Like who in their right mind would ask AI "why" it produced output it did? There is literally no information on which LLM can be trained for such a question. It's pure "Dear LLM, kindly lie to me now so that I can get a bit emotional uptake". Furthermore there is no particular information that can be given to LLM to get an answer when is such a thing was possible.
People are literally at a point where you tell them they are talking to a certified psychological patient with 100% disconnect from reality and they still want to treat answers as meaningful predictions for their life.
(Again: story is here about LLM "explaining" how and why it produced output it did)
MorallyDeplorable@reddit
lmao, this is the saddest "I'm scared of AI" post I've seen yet
Amichayg@reddit
Yeah, I’m also so frustrated when people use the letters of the alphabet instead of the binary equivalent. Don’t they get that A is actually 1000001? It’s all a bunch of numbers. Why did we develop CS again?
Remarkable_Tip3076@reddit
I am a mid level developer and recently reviewed a PR from a senior on my team that was clearly written by genAI. There were various things that made me think that, the main being the odd comments, but worse than that was the lack of intention behind the work.
It was a refactor that made no sense, it’s the kind of thing I would expect from a junior colleague. I raised it with a more senior colleague. I was just shocked more than anything - I genuinely don’t understand how someone at senior level with 20 years experience can turn to genAI in such a way!
Michaeli_Starky@reddit
There are literally reasoning models. Check for yourself.
Neat_Issue8569@reddit
All depends on your definition of "reasoning", but I doubt many people would agree a reasoning model actually reasons according to their own personal definition of the word. Taking an input string and reformatting it into a series of substrings that then get fed back into the model isn't what most people would consider "reasoning", but again, all depends on the definition. If you're thinking of cognitive reasoning like a human being does, with a capacity for logical deduction, critical thinking, metacognition, then no, they don't do any of that.
79215185-1feb-44c6@reddit
I'm not sure what you'd call a DFA with backtracking and the ability to modify the DFA besides "reasoning".
Michaeli_Starky@reddit
And DFA means?..
79215185-1feb-44c6@reddit
How are you on this sub and not know what a DFA is?
Michaeli_Starky@reddit
So what DFA is?
Angelsomething@reddit
LLMs work 100% of the time, 60% of the time.
superdurszlak@reddit
If I'm in a work-related discussion I will not say "I prompted LLM and it happened to make useful predictions" or something like that, unless I'm doing this in some sort of a goofy way. It would be ridiculous, excessive, and distracting from the merit of the discussion.
Likewise, I would not be discussing how compiler generated binary executable from my code, to be then executed by CPUs powering the servers. Nor would I correct myself because actually I'm a Java engineer so my code ultimately runs on a JRE.
Usually I'd just say "I used to do this and that" and state whether it was helpful or not. Obviously, when saying that an LLM is helpful I mean that it was helpful for me to use it, rather that an inanimate LLM could have a conscious intent to help me.
DependentPoet8410@reddit
I spend $5k on Claude Opus every month. Why? Because it is better than three shitty junior engineers and a junior engineer with a PhD in AI costs at least $250k a year.
The way I see it, the LLM's output shows more "thought" than what I get from my junior engineers. If the LLM isn't "thinking," then I guess those engineers aren't either.
Cyral@reddit
How do you like opus over sonnet? I never really use opus because it's so expensive but wonder if I am missing out on better quality.
DependentPoet8410@reddit
Never used sonnet. I asked if LLM spend is a problem and I was told no. So I just use Opus by default.
originalchronoguy@reddit
Take my upvote.
I basically have my own virtual team -- 2 mid level engineers, QA person, DevOps, and load tester. No UI/UX but they sure know how to read a design spec.
danteselv@reddit
I'll happily take only 150k a year to use the ai on your behalf.
majorchambus@reddit
“The use of anthropomorphic terminology when dealing with computing systems is a symptom of professional immaturity.”
Dijkstra
reboog711@reddit
FWIW: I never heard anyone say that.
metarobert@reddit
Give me better terms and I’ll use them. Are there better technical terms already? I know there are, but I’ll also bet that you’d get a grimace from listeners if you use them.
Don’t bother being pedantic about this one. We need words, better words would be better, but we don’t have them yet (they may exist, but they have to be understood before we can use them )
AchillesDev@reddit
Ask them how the text is generated, next.
Is it? Are you unfamiliar with terms of art?
minn0w@reddit
Yes! I heard one dev (a good dev that I know does know better) say "Korbit will understand all this better than me", which he was categorically incorrect about. Not only does the LLM not know anything, this Dev could have figured it out without much friction. This was the language I heard from the upper levels of management tricking down. It was alarming to me that nobody was keeping them in check... It's like whatever management says must be taken as the truth despite their own experience.
I'm not being quiet about it anytime, and I think it might cost me my job, but it's a sinking ship with that internal attitude.
Synor@reddit
Do you have trouble accepting the fact that LLMs do "understanding", "reasoning" or "suggesting"?
eraserhd@reddit
Do not anthropomorphize LLMs, they do not like that.
tmetler@reddit
I try to use terms like "surfacing knowledge" instead.
distroflow@reddit
“When we train an LLM to accurately predict the next word, what we are doing is learning a world model. It may look like we’re learning statistical correlations in text but it turns out that what's actually learned is some representation of the process that produced the text. The text is a projection of the world model. The neural network is not learning the sequence of text, it's learning the model of the world that is projected down into that text”. - Ilya Suskever, Cofouder of OpenAI
chewbaccajesus@reddit
I mean listen to none other than Geoffrey Hinton. He has a nobel prize and yet everytime I hear him talk about LLMs taking over I cringe - I am a trained neuroscientist and it is plain as day that LLMs are nowhere near cognition. They are also definitely not going to "become conscious" and take over. Nothing about LLMs imbues them with motivational ability and if you lack that you don't even have a survival instinct, let alone cognition.
NotScrollsApparently@reddit
I've seen stubborn reddit discussion where people claim that LLMs are the first step to sentience, not understanding there is no... understanding... or intelligence of any kind... behind them in general. It's a very bleak future ahead of us
ILikeBubblyWater@reddit
The developers of Claude themselves use those words and they most certainly know more about it than you by a large margin. They are called reasoning models for a reason.
So this seems more ignorance on your side.
NotNormo@reddit
I read your entire post and this is the closest you've come to actually explaining what your problem is with the language being used. But even this requires more explanation. Can you expand on this thought?
If you're right, and there are better words to use, then I'll agree with you just on the basis of trying to use more accurate and precise terminology whenever possible. (Not because I'm distressed by anything symbolic about using the other words.)
But as far as I can tell, "thinking" is a pretty good approximation / analogy of what the LLM is doing. In other words I don't agree with you that it's "diametrically opposed" to what is happening.
Oreamnos_americanus@reddit
LLMs are “just” a next token predictor in the same way that you’re “just” a pile of cells. Sure, that is fundamentally what it is, but you still have to deal with what it’s capable of.
rashnull@reddit
Yes. Makes my brain curl when I hear normally smart people talking this way about LLMs
Ynkwmh@reddit
I read quite a bit on the theory behind it. Like Deep Neural networks and related math, as well as on the transformer architecture, etc. and I use the term “cognition” in relation to it, because it does seem like it’s what it’s doing on some level. Not saying it’s conscious or even self-aware, but to me it is doing cognition.
ur_fault@reddit
So are you an alien? Or a robot?
AnomalousBrain@reddit
What else would you call the "chain of thought" that is produced prior to responding? If it's not reasoning then what is it? Auto completing its way to a conclusion?
__loam@reddit
Before I became a programmer I worked in biology. Software engineers could really use a course in both ethics and neuroscience.
Deranged40@reddit
So, this is called personification, and is very common and generally useful tool for us.
However, this particular personification happens to occur in a sort of "uncanny valley".
originalchronoguy@reddit
I dont think you know how LLMs (large language models) work
They technically "don't think" but they do have processing on knowing how to react and determine my "intent."
When I say, build a a CRUD REST API to this model I have, a good LLM like Claude, looks at my source code. It knows the language, it knows how the front end is suppose to connect to my backend, it knows my backend connects to a database, it sees the schema.
And from a simple "build me a CRUD API", it has a wealth of knowledge they farmed. Language MAN files, list of documentation. It knows what a list is, how to pop items out of an array, how to insert. How to enable a middle ware because it sees my API has auth guarding, it sees I am using a ingress that checks and returns 403s... It can do all of this analysis in 15 seconds. Versus even a senior grepping/AWK a code base. It is literally typing u p 400 words per second, reading 2000s of lines of text in seconds.
So it knows what kind of API I want, how to enforce security, all the typical "Swagger/OpenAPI" contract models. And produces exactly what I want.
Sure, it is not thinking but it is doing it very , very, very fast.
Then I just say "Make sure you don't have stored keys that can be passed to .git"
It replies, "I see you have in your helm chart, you call Hashicorp Vault to rotate secrets, should I implement that and make a test plan, test suite, pen-test so you can run and make sure this API is secured?"
I reply,"yes please. Thanks for reading my CLAUD .md and rules manifest"
So it is just writing out text. It is following my intent as it gathers context. From my prompt, from my code, from my deployment files, from my Swagger Specs, from my rules playbook.
And it does it faster than most people; seniors included who have to digest 3000 words of dcoumentation and configs in less than a minute,
benkalam@reddit
There are a lot of people who value AI solely for its ability to output some finished product rather than as a tool to enhance their own production in their job or school or even day-to-day life. I think of students who have AI write entire papers for them, and I think in my teens and maybe early 20s I would have felt a lot of incentive to do that as well.
But if I had to write my 40 page senior thesis today it would be so much easier by utilizing AI not to write any content, but for identifying interesting thesis topics, helping me understand the breadth of conflict about whatever topic I choose, pointing out flaws in my arguments and sources for those flaws that I can respond to, etc. etc.
40 pages felt nearly impossible to college aged me (which I realize is dumb and people can and do write much longer shit for their PHDs or whatever), but using AI as a tool, as a sounding board and context specific source-finder, I think I could probably do it in 8-16 hours with probably better quality than my original.
My concern with AI doesn't have much to do with the language around it, I'm much more concerned with the skill gap it's going to create, particularly for young people, between those that learn how to use AI to think better for themselves, and those that just let AI 'think' on their behalf.
WillCode4Cats@reddit
It is true that LLMs do not think. However, I know plenty of humans that do not think either.
iPissVelvet@reddit
Let's try and figure out how the technology will affect us first, before focusing on semantics.
Sevii@reddit
What 'LLMs are capable of' changes every 3 months. The tech is advancing way too fast to make sweeping statements like that.
spicyville@reddit
Lord I wish I had enough time to be worried about these types of things
defmacro-jam@reddit
Can a submarine swim? Does it hurt anything to call what a submarine is doing swimming?
you-create-energy@reddit
With every additional month that goes by, I am even more deeply incredulous and amused at the determined ignorance of the majority of this sub around this impactful emerging technology. It's like you use cursor and think you're experts on AI. Do you not read any news? Have you not heard about the many breakthroughs and science, math, medicine, and so forth entirely driven by LLMS? Have you not had a single deep conversation with any of the cutting edge AIs with the reasoning previews turned on? You can see it's reasoning step-by-step. Here is a handy link that provides a simple introduction: https://en.m.wikipedia.org/wiki/Reasoning_language_model
I'm hopeful that some equally bizarre programmer Luddite amalgam informs me that nothing on Wikipedia is reliable because editors can edit it. I look forward to reading all of the statistics based text generation you generate in response to my statistics based text generation.
Additional-Bee1379@reddit
Honestly the idea that the SOTA AI models don't reason is completely outdated anyway. LLMs posess a limited reasoning capability by all definitions of the word.
wrex1816@reddit
Easy answer. Stop existing in echo chambers. The above statement seems like you would have solicited my opinions and you certainly didn't.
a1454a@reddit
LLM is a glorified next token prediction machine. But if given the same input, it produces output tokens similar to what a highly educated intelligent human would, what is wrong to call that “understanding” or “reasoning”? Or maybe think of this another way around, human brain process input and produce output, isn’t human also glorified next token prediction machine?
SynthRogue@reddit
The personification of LLMs
m3t4lf0x@reddit
Hey Siri: Define Dunning-Kruger
flavius-as@reddit
When I use the word "think" in an instruction, my goal is not to make the LLM think, but to increase its weights of those connections connected to thinking and rational thinking.
Also, I equally write the instructions for me and other humans to be able to read, understand and audit.
Main-Drag-4975@reddit
I’ve long preferred conversational terms when discussing distributed systems and OOP:
Client says hi I’d like XYZ and server says ok your order has been placed, take this ticket and wait for a call.
That sort of framing is helpful. Folks talking about LLM agents conversing with them and understanding and researching stuff though? Blech. 🤮
im-a-guy-like-me@reddit
Fighting how humans use language is a losing fight. Prioritize better. 😂
TalesfromCryptKeeper@reddit
The N word is a good example of a winning fight. You give up easy.
im-a-guy-like-me@reddit
Your snappy retort falls apart as soon as you remember the kkk and teenage rap fans exist.
You call that a win?
TalesfromCryptKeeper@reddit
Of course. Write it out right now. :)
im-a-guy-like-me@reddit
You're bad at logic. Youre talking as though an opt-in system can be used as an authority.
gdvs@reddit
Experience means they have seen more projects fail and succeed. It doesn't mean they have a better understanding of how stuff works.
79215185-1feb-44c6@reddit
And someone who's been programming for 30 years likely isn't too concerned about getting things done in a timely manner or learning new technology when their ways of doing things work for them.
Synth_Sapiens@reddit
Define "understanding", "reasoning" and "suggesting".
I'll wait.
Own-Chemist2228@reddit
Claims of computers "reasoning" have been around a long time. Here's the Wikipedia description of an expert system which has been around since at least the 1980s:
"Expert systems are designed to solve complex problems by reasoning through bodies of knowledge ..."
nextnode@reddit
Not just claim - proven. Eg first-order logic is a form of reasoning and we have had algorithms that can do first-order logic for decades.
Empty_Geologist9645@reddit
Bro the language of your post is exactly why LLM will win over peoples hearts. People don’t talk like this.
79215185-1feb-44c6@reddit
It's also why OP won't have a job in a decade or so.
TheRealStepBot@reddit
Sure bud. It’s just a glorified text generator. This bodes well for your career.
Probably should do a bit more learning, reasoning and understanding yourself about what they are and how they work before going off on the internet.
If they are not reasoning give a definition of reasoning? As no one can it’s safe to say they are reasoning at least in as much as they can the process they follow to arrive at the sorts of answer humans can only arrive at by what we would consider reasoning.
The mechanisms might be different, and the capabilities not entirely equivalent but the there is definitely reasoning and understanding occurring to the best of anyone’s definitions of those words.
nextnode@reddit
There are definitions of reasoning and LLMs are recognize to reason according to those and a multitude of recent papers.
Reasoning is not special.
likeittight_@reddit
Upvoted because I’m assuming this is irony….
TheRealStepBot@reddit
Then maybe you too should spend more time learning about the dialogue in the field today. What is reasoning? is the standard response given to all the “it’s not really reasoning” people
DrXaos@reddit
It is somewhat true. These systems are indeed trained fairly stupidly in their mechanism, and surprisingly they have more capabilities than the shallowness of their architecture would suggest.
They are non-human learners with better than human capabilities in some areas (exact token buffers and larger train corpus on text) and worse than humans in others (no experience, senses, will or planning or stable point of view).
Smart birds learn too, and they don't have human capabilities but there is certainly understanding and reasoning.
mauriciocap@reddit
the "bro", the misspelled and repeated words, may be 😯
mauriciocap@reddit
Agree. But we will be downvoted to oblivion 😂
These group names don't work, it's like r/gifted.
It's quite stimulating to foresee how we will make money from other people's mistakes.
nextnode@reddit
The OP sentiment is anti-correlated with the sub name.
nextnode@reddit
'Reasoning' is a technical term that has existed for four decades and we have had algorithms that can reason to some extent for four decades.
The problem here rather lies on those that have an emotional reaction to the terms and who inject mysticism.
greensodacan@reddit
I'm less concerned about "suggest" because it doesn't imply expertise. I think, when people say "reason" they're implying that there was some logical path involved to arrive at a result, but not that the logic itself was sound.
"My neighbor suggested I attend a flat earther meetup because, when he places a tennis ball on our sidewalk, it stays in place."
Still, you could normalize different terms where you work by using alternatives like, "computed" or "resulted". "Cursor resulted that the bug is likely a display issue due to an incorrect template variable."
Stubbby@reddit
Anthropomorphizing is a part of any design - cars have faces and industrial machines sound concerned when they set an alarm. Best way to communicate with humans is by making things act human-like.
Careful_Ad_9077@reddit
To add to the other answers; just think of it as being another business domain.
Like how the word client means different things in differnt types of systems, reasoning, mood, prediction, etc.. mean something differnet when talking about AI.
CalmLake999@reddit
If you can read code fast, cursor is simply amazing.
If you can't you're in for a world of pain.
globalaf@reddit
Yes it does bother me, but people unfortunately are really bad at understanding what an AI is actually doing, even the creators, so they bucket it up into some familiar concept even if it’s wildly inaccurate.
Sheldor5@reddit
experience != intelligence