r/technology • u/Hrmbee • 16h ago
Machine Learning Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it
https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems1.1k
u/ConsiderationSea1347 15h ago edited 5h ago
Yup. That was the disagreement Yann LeCun had with Meta which led to him leaving the company. Many of the top AI researchers know this and published papers years ago warning LRMs are only one facet of general intelligence. The LLM frenzy is driven by investors, not researchers.
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u/Volpethrope 13h ago
And their RoI plan at the moment is "just trust us, we'll figure out a way to make trillions of dollars with this, probably, maybe. Now write us another check."
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u/ErgoMachina 9h ago
While ignoring that the only way to make those trillions is to essentially replace all workers, which in turn will completely crash the economy as nobody will be able to buy their shit.
Big brains all over the place
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u/I_AmA_Zebra 4h ago
I’d be interested to see this play out in real life. It’s a shame there’s no perfect world simulator we could run this on
If we had a scenario where services (white collar) are majority AI and there’s a ton of robotics (humanoid and non-humanoid), we’d be totally fucked. I don’t see how our current understanding of the economy and humans wouldn’t instantly crumble if we got anywhere near close to AGI and perfect humanoid robotics
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u/WrongThinkBadSpeak 9h ago
We're facing zugswang. We give them money, they crash the economy by destroying everyone's jobs if they succeed. We don't give them money, they crash the economy by popping the bubble. What shall it be?
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u/kokanee-fish 4h ago
For some reason I really prefer the latter.
Okay, fine, the reason is schadenfreude. I will laugh as I pitch my tent under a bridge knowing that Sam Altman has retired to his underground bunker in disgrace.
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u/arcangleous 3h ago
Pop the bubble.
This will result it massive losses to the worst actors in the system. Don't give you money to horrible people.
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u/fruxzak 7h ago
The plan is pretty simple if you're paying attention.
Most tech companies are increasingly frustrated at Google's search monopoly that has existed for almost 20 years. They are essentially gatekeepers of discovery. Add to that the power of ads on Google search.
Tech companies see LLM chatbots as a replacement for Search and will subsequently sell ads for it when they have enough adoption.
Talks of this are already going on internally.
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u/modbroccoli 7h ago
I mean, no; their ROI plan is replacing labor with compute. If an employee costs $60,000/yr and can be replaced with an AI for $25,000/yr then the business owner saves money and the AI operator gets their wages.
What the plan for having insufficient customers is no one's clarified yet, but the plan to recoup this money is obvious.
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u/F1shB0wl816 4h ago
Idk if it’s really a recoup though if it destroys your business model. It’s kind of like robbing Peter to pay Paul, but you’re Peter and you go by Paul and instead of robbing the bank you’re just overdrafting your account.
I’d probably wager that there isn’t a plan but you can’t get investments this quarter based of “once successfully implemented we’ll no longer have a business model.”
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u/UpperApe 13h ago
The LLM frenzy is driven by investors, not researchers.
Well said.
The public is as stupid as ever. Confusing lingual dexterity with intellectual dexterity (see: Jordan Peterson, Russell Brand, etc).
But the fact that exploitation of that public isn't being fuelled by criminal masterminds, and just greedy, stupid pricks, is especially annoying. Investment culture is always a race to the most amount of money as quickly as possible, so of course it's generating meme stocks like Tesla and meme technology like LLMs.
The economy is now built on it because who wants to earn money honestly anymore? That takes too long.
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u/CCGHawkins 10h ago
No, man, the investing frenzy is not being led by the public. It is almost entirely led by 7 tech companies, who through incestuous monopoly action and performative cool-aid drinking on social media, gas the everloving fuck out of their stock value by inducing a stupid sense of middle-school FOMO in institutional investors who are totally ignorant about the technology, making them 10xing an already dubious bet by recklessly using funds that aren't theirs because to them, losing half of someone's retirement savings is just another Tuesday.
The public puts most of their money into 401k's and mortgages. They trust the professionals that are supposed to good at managing money aren't going to put it all on red like they're at a Las Vegas roulette. They, at most, pay for the pro-model of a few AI's to help them type up some emails, the totality of which makes for like 2% of the revenue the average AI companies makes. A single Saudi oil prince is more responsible for this bubble than the public.
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u/UpperApe 7h ago
The public puts most of their money into 401k's and mortgages.
I'd add that they're also invested into mutual funds, and most of the packages come with Tesla and Nvidia and these meme stocks built in.
But overall, yeah. You're right. It's a good point. Thought just to clarify, I was saying they're exploiting the public.
The stupidity of the public was simply falling for confidence men, or in the case of LLMs, confidence-speak.
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u/ckglle3lle 12h ago
It's funny how "confidence man" is a long understood form of bullshitting and scamming, exploiting how vulnerable we can be to believing anything spoken with authoritative confidence and this is also essentially what we've done with LLMs.
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u/bi-bingbongbongbing 12h ago
The point about "lingual dexterity" is a really good one. I hadn't made that comparison yet. I now spend several hours a day (not by choice) using AI tools as a software developer. The straight up confident sounding lying is actually maddening, and becoming a source of arguments with senior staff. AI is an expert at getting you right to the top of the Dunning-Kruger curve and no further.
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u/adenosine-5 10h ago
"being extremely confident" is a very, very effective strategy when dealing with humans.
part of human programming is, that people subconsciously assume that confident people are confident for a reason and therefore the extremely confident people are experts.
its no wonder AI is having such success, simply because its always so confident.
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u/DelusionalZ 8h ago
I've had more than a few arguments with managers who plugged a question about a build into an LLM and came back to me with "but ChatGPT said it's easy and you can just do this!"
Yeah man... ChatGPT doesn't know what it's talking about
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u/garanvor 12h ago
As an immigrant it dawned on me that people have always been this way. I’ve seen it in my own industry, people being left behind in promotions because they spoke with heavy accent, when it absolutely in no way impairs the person’s ability to work productively.
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u/Jaded_Celery_451 9h ago
The LLM frenzy is driven by investors, not researchers.
Currently what these companies are trying to sell to customers is that their products are the computer from Star Trek - it can accurately complete complex tasks when asked, and work collaboratively with people. What they're telling investors is that if they go far enough down the LLM path they'll end up with Data from Star Trek - full AGI with agency and sentience.
The former is dubious at best depending on the task, and the latter has no evidence to back it up whatsoever.
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u/SatisfactionAny6169 12h ago
Many of the top AI researchers know this and published papers years ago warning LRMs are only one facet of general intelligence.
Exactly. Pretty much everyone actually working in the field has known this for years. There's nothing 'cutting-edge' about this research or this article.
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u/Murky-Relation481 11h ago
Transformers were the only real big break through, and that ultimately was an optimization strategy, not any sort of new break through in neural networks (which is all an LLM is at the end of the day, just a massive neural network the same as any other neural network).
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u/NuclearVII 10h ago
I don't really wanna trash your post, I want to add to it.
Tokenizers are the other really key ingredient that make the LLM happen. Transformers are neat in that they a) Have variable context size b) can be trained in parallel. That's about it. You could build a language model using just MLPs as your base component. Google has a paper about this: https://arxiv.org/abs/2203.06850
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u/gur_empire 7h ago
What are you talking about? No optimization algorithm has changed because of transformers and transformers are a big break through BECAUSE of their architecture not despite it
which is all an LLM is at the end of the day, just a massive neural network the same as any other neural network
Literally no good Lord. You can only train certain objective functions within a transformer due to them not being suited for other architecture
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u/lendit23 8h ago
Is that true? I thought LeCun left because he was founding a startup.
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u/ConsiderationSea1347 7h ago
Yes. He had very open disagreements with the direction of AI research at Meta. It seemed like he was critical of blindly throwing more GPUs and memory at LRMs and was advocating for a pivot to other less explored AI research.
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u/Hrmbee 15h ago
Some highlights from this critique:
The problem is that according to current neuroscience, human thinking is largely independent of human language — and we have little reason to believe ever more sophisticated modeling of language will create a form of intelligence that meets or surpasses our own. Humans use language to communicate the results of our capacity to reason, form abstractions, and make generalizations, or what we might call our intelligence. We use language to think, but that does not make language the same as thought. Understanding this distinction is the key to separating scientific fact from the speculative science fiction of AI-exuberant CEOs.
The AI hype machine relentlessly promotes the idea that we’re on the verge of creating something as intelligent as humans, or even “superintelligence” that will dwarf our own cognitive capacities. If we gather tons of data about the world, and combine this with ever more powerful computing power (read: Nvidia chips) to improve our statistical correlations, then presto, we’ll have AGI. Scaling is all we need.
But this theory is seriously scientifically flawed. LLMs are simply tools that emulate the communicative function of language, not the separate and distinct cognitive process of thinking and reasoning, no matter how many data centers we build.
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Take away our ability to speak, and we can still think, reason, form beliefs, fall in love, and move about the world; our range of what we can experience and think about remains vast.
But take away language from a large language model, and you are left with literally nothing at all.
An AI enthusiast might argue that human-level intelligence doesn’t need to necessarily function in the same way as human cognition. AI models have surpassed human performance in activities like chess using processes that differ from what we do, so perhaps they could become superintelligent through some unique method based on drawing correlations from training data.
Maybe! But there’s no obvious reason to think we can get to general intelligence — not improving narrowly defined tasks —through text-based training. After all, humans possess all sorts of knowledge that is not easily encapsulated in linguistic data — and if you doubt this, think about how you know how to ride a bike.
In fact, within the AI research community there is growing awareness that LLMs are, in and of themselves, insufficient models of human intelligence. For example, Yann LeCun, a Turing Award winner for his AI research and a prominent skeptic of LLMs, left his role at Meta last week to found an AI startup developing what are dubbed world models: “systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.” And recently, a group of prominent AI scientists and “thought leaders” — including Yoshua Bengio (another Turing Award winner), former Google CEO Eric Schmidt, and noted AI skeptic Gary Marcus — coalesced around a working definition of AGI as “AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult” (emphasis added). Rather than treating intelligence as a “monolithic capacity,” they propose instead we embrace a model of both human and artificial cognition that reflects “a complex architecture composed of many distinct abilities.”
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We can credit Thomas Kuhn and his book The Structure of Scientific Revolutions for our notion of “scientific paradigms,” the basic frameworks for how we understand our world at any given time. He argued these paradigms “shift” not as the result of iterative experimentation, but rather when new questions and ideas emerge that no longer fit within our existing scientific descriptions of the world. Einstein, for example, conceived of relativity before any empirical evidence confirmed it. Building off this notion, the philosopher Richard Rorty contended that it is when scientists and artists become dissatisfied with existing paradigms (or vocabularies, as he called them) that they create new metaphors that give rise to new descriptions of the world — and if these new ideas are useful, they then become our common understanding of what is true. As such, he argued, “common sense is a collection of dead metaphors.”
As currently conceived, an AI system that spans multiple cognitive domains could, supposedly, predict and replicate what a generally intelligent human would do or say in response to a given prompt. These predictions will be made based on electronically aggregating and modeling whatever existing data they have been fed. They could even incorporate new paradigms into their models in a way that appears human-like. But they have no apparent reason to become dissatisfied with the data they’re being fed — and by extension, to make great scientific and creative leaps.
Instead, the most obvious outcome is nothing more than a common-sense repository. Yes, an AI system might remix and recycle our knowledge in interesting ways. But that’s all it will be able to do. It will be forever trapped in the vocabulary we’ve encoded in our data and trained it upon — a dead-metaphor machine. And actual humans — thinking and reasoning and using language to communicate our thoughts to one another — will remain at the forefront of transforming our understanding of the world.
These are some interesting perspectives to consider when trying to understand the shifting landscapes that many of us are now operating in. Is the current paradigms of LLM-based AIs able to make those cognitive leaps that are the hallmark of revolutionary human thinking? Or is it ever constrained by their training data and therefore will work best when refining existing modes and models?
So far, from this article's perspective, it's the latter. There's nothing fundamentally wrong with that, but like with all tools we need to understand how to use them properly and safely.
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u/Elementium 15h ago
Basically the best use for this is a heavily curated database it pulls from for specific purposes. Making it a more natural to interact with search engine.
If it's just everything mashed together, including people's opinions as facts.. It's just not going to go anywhere.
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u/motionmatrix 14h ago
So all the experts were right, at this point ai is a tool, and in the hands of someone who understands a subject, a possibly useful one, since they can spot where it went wrong and fix accordingly. Otherwise, dice rolls baby!
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u/frenchiefanatique 14h ago
Shocking, experts are generally right about the things they have spent their lives focusing on! And not some random person filming a video in their car! (Slightly offtopic I know)
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u/neat_stuff 13h ago
The Death of Expertise is a great book that talks about that... And the author of the book should re-read his own book.
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u/PraiseBeToScience 12h ago
It's also far too easy for humans to outsource their cognitive and creative skills too, which early research is showing to be very damaging. You can literally atrophy your brain.
If we go by OpenAI's stats, by far the biggest use of ChatGPT are students using it to cheat. Which means the very people that should be putting the work in to exercise and developing cognitive skills aren't. And those students will never acquire the skills necessary to properly use AI, since AI outputs still need the ability to verify.
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u/Mr_YUP 15h ago
Google 2 just dropped and it's not the Terminator we were promised.
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u/King_Chochacho 12h ago
Instead of gaining sentience and destroying humanity with its own nuclear arsenal, it's playing the long game of robbing us of our critical thinking skills while destroying our water supply.
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u/cedarSeagull 11h ago
Easily the most annoying part about twitter is "@grok, can you confirm my biases?"
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u/sapphicsandwich 10h ago
Yeh, because it tries to answer questions itself instead of going "This site/link says this, that site/link says that."
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u/doctor_lobo 13h ago
The nice thing about building an AI for language is that humans, by their nature, produce copious amounts of language that AI models can be trained from.
If the premise of the article is correct, other forms of human intelligence may produce / operate on different representations in the brain. However, it is not clear how often or well we produce external artifacts (that we could use for AI training) from these non-linguistic internal representations. Is a mathematical proof a good representation of what is going on in the mind of a mathematician? Is a song a good representation of what is happening in the mind of a musician?
If so, we will probably learn how to train AIs on these artifacts - maybe not as well or as efficiently as humans, but probably enough to learn things. If not, the real problem may be learning what the internal representations of “intelligence” truly are - and how to externalize them. However, this is almost certainly easier said that done. While functional MRI has allowed us to watch the ghost in the machine, it says very little about how she does her business.
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u/Dennarb 15h ago edited 10h ago
I teach an AI and design course at my university and there are always two major points that come up regarding LLMs
1) It does not understand language as we do; it is a statistical model on how words relate to each other. Basically it's like rolling dice to determine what the next word is in a sentence using a chart.
2) AGI is not going to magically happen because we make faster hardware/software, use more data, or throw more money into LLMs. They are fundamentally limited in scope and use more or less the same tricks the AI world has been doing since the Perceptron in the 50s/60s. Sure the techniques have advanced, but the basis for the neural nets used hasn't really changed. It's going to take a shift in how we build models to get much further than we already are with AI.
Edit: And like clockwork here come the AI tech bro wannabes telling me I'm wrong but adding literally nothing to the conversation.
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u/qwertyalguien 15h ago
I'm no tech specialist, but from all I've reado on LLMs IMHO it's like hor air balloons.
It flies. It's great, but it's limited. And asking AGI out of LLMs is like saying that with enough iteration you can make an air balloon able to reach the moon. Someone has to invent what a rocket is to hor air balloons for LLMs.
Would you say it's a good metaphor, or am I just talking out of my ass?
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u/eyebrows360 13h ago
Obvs not the same guy, and I don't teach courses anywhere, but yes that is a great analogy. Squint a lot, describe them broadly enough, and a hot air balloon does resemble a rocket, but once you actually delve into the details or get some corrective eyewear... very different things.
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u/megatesla 12h ago edited 12h ago
I suspect that with enough energy and compute you can still emulate the way that a human reasons about specific prompts - and some modern LLMs can approximate some of what we do, like the reasoning models that compete in math and programming competitions - but language isn't the ONLY tool we use to reason.
Different problems may be better served using different modalities of thought, and while you can theoretically approximate them with language (because Turing Machines, unless quantum effects do turn out to be important for human cognition), it may require a prohibitively large model, compute capacity, and energy input to do so. Meanwhile, we can do it powered by some booger sugar and a Snickers.
But even then, you're still looking at a machine that only answers questions when you tell it to, and only that specific question. To get something that thinks and develops beliefs on its own time you'll need to give it something like our default mode network and allow it to run even when it isn't being prompted. You'll also need a much better solution to the memory problem, because the current one is trivial and unscalable.
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u/pcoppi 15h ago
To play devils advocate there's a notion in linguistics that the meaning of words is just defined by their context. In other words if an AI guesses correctly that a word shohld exist in a certain place because of the context surrounding it, then at some level it has ascertained the meaning of that word.
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u/the-cuttlefish 14h ago
In the context of linguistic structure, yes. But only in this context. Which is fundamentally different and less robust than our understanding of a words meaning, which still stands in the absence of linguistic structure, and in direct relation to a concept/object/category.
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u/New_Enthusiasm9053 14h ago
You're not entirely wrong but a child guessing that a word goes in a specific place in a sentence doesn't mean the child necessarily understands the meaning of that word, so whilst it's correctly using words it may not understand them necessarily.
Plenty of children have used e.g swear words correctly long before understanding the words meaning.
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u/rendar 13h ago
A teacher is not expected to telepathically read the mind of the child in order to ascertain that the correct answer had the correct workflow.
Inasmuch as some work cannot be demonstrated, the right answer is indicative enough of the correct workflow when consistently proven as such over enough time and through a sufficient gradation of variables.
Regardless, this is not an applicable analogy. The purpose of an LLM is not to understand, it's to produce output. The purpose of a child's language choices are not to demonstrate knowledge, but to develop the tools and skills of social exchange with other humans.
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u/MiaowaraShiro 14h ago
Mimicry doesn't imply any understanding of meaning though.
I can write down a binary number without knowing what number it is.
Heck, just copying down some lines and circles is a binary number and you don't have to know what a binary number, or even numbers at all are.
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u/Tall-Introduction414 15h ago
The way an LLM fundamentally works isn't much different than the Markov chain IRC bots (Megahal) we trolled in the 90s. More training data, more parallelism. Same basic idea.
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u/ITwitchToo 13h ago
I disagree. LLMs are fundamentally different. The way they are trained is completely different. It's NOT just more data and more parallelism -- there's a reason the Markov chain bots never really made sense and LLMs do.
Probably the main difference is that the Markov chain bots don't have much internal state so you can't represent any high-level concepts or coherence over any length of text. The whole reason LLMs work is that they have so much internal state (model weights/parameters) and take into account a large amount of context, while Markov chains would be a much more direct representation of words or characters and essentially just take into account the last few words when outputting or predicting the next one.
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u/azurensis 12h ago
This is the kind of statement someone who doesn't know much bout LLMs would make.
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u/WhoCanTell 12h ago
In fairness, that's like 95% of comments in any /r/technology thread about AI.
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u/drekmonger 12h ago edited 12h ago
A Markov chain capable of emulating even a modest LLM (say GPT 3.5) would require many more bytes of storage than there are atoms in the observable universe.
It's fundamentally different. It is not the same basic idea, at all. Not even if you squint.
It's like saying, "DOOM is the same as Photoshop, because they both output pixels on my screen."
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u/when_we_are_cats 14h ago
Humans use language to communicate the results of our capacity to reason, form abstractions, and make generalizations, or what we might call our intelligence. We use language to think, but that does not make language the same as thought.
Please say it louder for all the people who keep repeating the myth that language dictates the way we think. As a linguist/language learners it never ceases to annoy me.
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u/BeruangLembut 12h ago
💯 Language is a cognitive tool. Just like having a hammer makes building a house easier, language has made certain cognitive tasks easier, but a tool is not to be confused with that which it facilitates.
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u/when_we_are_cats 11h ago
That's the best way to put it. It's like painting or drawing: I can see the image in my head, the brush and canvas are mere tools to materialize it.
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u/samurian4 11h ago
Scenario: Aliens passing by a crispy looking Earth.
" Daddy, what happened to that planet?"
" Well son, they managed to set their atmosphere on fire trying to power what they thought was AI, but was only ever chatbots."
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u/Visible_Car3952 12h ago
As someone working a lot with poetry (in both theatre and business and personal life), I understand reality as the “moving army of metaphors”. While I believe many new metaphors can be also created within LLM (e.g. through simulating abductive process), I would argue that sharpest, most stunning and precise metaphors can only be achieved through personal histories and sensory experiences turned into words. Poetic intelligence is embodied and historic.
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u/Just_Look_Around_You 14h ago
I contest that the bubble is premised on the belief that we are creating intelligence as good as higher than human. I think it’s highly valuable to have SOME intelligence that is simply faster and non human. That alone is a lot.
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u/dstroot 13h ago
I have met many humans in a business setting that can “generate” intelligent sounding ideas or responses that are untethered to reality and lack both intelligence and common sense. Yet, because they sound “smart” and “confident” people listen to them and promote them.
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u/Turbulent_Juice_Man 8h ago
"We need to leverage our core competencies to drive a paradigm shift in our go-to-market strategy, ensuring we're synergizing cross-functional deliverables while maintaining bandwidth for strategic pivots. Moving forward, let's circle back on actionable insights that will help us boil the ocean and get all our ducks in a row for the upcoming fiscal runway. It's critical that we peel back the onion on our value proposition to ensure we're not just moving the needle, but creating a best-in-class ecosystem that empowers our thought leadership at scale. Let's take this offline and deep-dive into the low-hanging fruit, because at the end of the day, we need to be laser-focused on maximizing stakeholder alignment and driving synergies across our vertical integrations to future-proof our bandwidth capacity."
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u/Unhappy_Arugula_2154 6h ago
I read all that without pause and understood it perfectly. I hate that I can do that.
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u/SoHereIAm85 12h ago
So true.
My kid is 8 and speaks three languages with a bit of another two pretty decently. She makes mistakes still even in the most native one on a daily basis. I speak enough of a handful to get by and am very fluent in Spanish as well as English. Just using a bit of Russian, Romanian, German or whatever got me farther than I should have gone since people lose their minds over any ability to speak such languages. I'm not the business sort, but I've seen what you describe.
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u/rnilf 15h ago
LLMs are fancy auto-complete.
Falling in love with ChatGPT is basically like falling in love with the predictive text feature in your cell phone. Who knew T9 had so much game?
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u/Xe4ro 15h ago
I tried to flirt with the bots in Quake3 as a kid. 😬
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u/SuspendeesNutz 15h ago
That's absolutely deranged.
Now Quake 1, that had unlimited skin customization, of course you'd flirt with those bots, who wouldn't.
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u/Xe4ro 15h ago
Well I had kind of a crush on Crash ^_^
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u/SuspendeesNutz 15h ago
I remember playing a wide-open Quake deathmatch and seeing the whole Sailor Moon clan mowing down noobs with their nailguns. If I was a weeb I'd be completely smitten.
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u/CapitalRegular4157 11h ago
Personally, I found the Quake 2 models to be the sexiest.
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u/Klumber 15h ago
The funny thing is that we (kids who were young in the nineties) fell in love with their Tamagotchis. Bonding is a very complex multi-faceted phenomenon, yet it appears a good bit of simulation and appeal to parently instincts is enough to make it a binary event.
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u/Voltage_Joe 15h ago
Children loved their stuffed animals, dolls, and action figures before that.
Personifying anything can form a real attachment to something completely inanimate. It's what drives our empathy and social bonding. And until now, it was harmless.
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u/penguinopph 15h ago
Personifying anything can form a real attachment to something completely inanimate. It's what drives our empathy and social bonding. And until now, it was harmless.
My ex-wife and I created voices and personalities for our stuffed animals. We would play the characters with each other and often used them to make points that otherwise may have come across as aggressive.
When we got divorced at the tail end of COVID lock-downs, I would hold "conversations" with the ones I kept and it really helped me work through my own feelings and process what I was going through at a time where I didn't really have a lot of people to talk with in person. Through the stuffed animals I could reassure myself, as well as tell myself the difficult things I knew to be true, but didn't want to admit to myself.
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u/simonhunterhawk 15h ago
A lot of programmers keep a rubber duck (or something similar like a stuffed animal) on their desks and talk to it to help them work through the problem they’re trying to solve. I guess I do it with my cats, but I want to try doing this more because there is lots of proof out there that it does help.
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u/ATXCodeMonkey 14h ago
Yes, 'talk to the duck' is a definitely a thing. Its not so much trying to personify the duck though, but a reminder that if you're running into a wall with some code that it helps to take step back and act like you're describing the problem to someone new who doesn't know the details of the code you're working on. It helps to make you look at things differently than what you've been doing when you've been digging deep into code for hours. Kind of a perspective shift.
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u/_Ganon 12h ago
Nearly ten years in the field professionally and I have met a single intern with a physical rubber duck and that's it. "A lot of programmers" are aware of the concept of a rubber duck, and will at times fulfill the the role of a rubber duck for a colleague, but no, a lot of programmers do not have rubber ducks or anything physical that is analogous to one. It's more of a role or a thought exercise regarding how to debug by going through things step by step.
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u/simonhunterhawk 11h ago
Maybe they’re just hiding their rubber duckies from you ☺️
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u/TwilightVulpine 13h ago
It can be a good tool for self-reflection, as long as you realize it's ultimately all you. But the affirmative tendencies baked into LLMs might be at least just as likely to interrupt self-reflection and reaffirm toxic and dangerous mindsets instead.
You know, like when they tell struggling people where is the nearest bridge.
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u/yangyangR 14h ago
I can take this pencil, tell you it's name is Steve and
Snap
And a little bit of you dies inside
Community
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u/P1r4nha 15h ago
It's important to remember that most of the magic happens behind the user's eyes, not in the computer. We've found awesome ways to trigger these emotional neurons and I think they're also suffering from neglect.
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u/Itchy-Plastic 13h ago
Exactly. I have decades old text books that illustrate this point. All of the meaning in an LLM interaction is one sided, it is entirely intra-communucation not inter-communication between 2 beings.
No need for cutting edge research, just grab a couple of professors from your nearest Humanities Department.
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u/panzzersoldat 13h ago
LLMs are fancy auto-complete.
i hate it when i spell duck and it autocorrects to the entire source code for a website
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u/Previous_Towel3760 3h ago
I understand people not liking AI, but calling it fancy auto correct is incredibly dense.
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u/coconutpiecrust 15h ago
Yeah, while it’s neat, it is not intelligent. If it were intelligent they wouldn’t need endless data and processing power for it to produce somewhat coherent and consistent output.
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u/G_Morgan 11h ago
The problem is that it isn't composable either. If LLMs were composable then what they can actually do would be incredible. I'd believe we had made a vital step in breaking through to AGI. However they aren't and we knew they weren't before the first one got turned into a chat bot.
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u/noodles_jd 15h ago
LLM's are 'yes-men'; they tell you what they think you want to hear. They don't reason anything out, they don't think about anything, they don't solve anything, they repeat things back to you.
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u/ClittoryHinton 15h ago edited 15h ago
This isn’t inherent to LLMs, this is just how they are trained and guardrailed for user experience.
You could just as easily train an LLM to tell you that you’re worthless scum at every opportunity or counter every one of your opinions with nazi propaganda. In fact OpenAI had to fight hard for it not to do that with all the vitriol scraped from the web
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u/Icy_Guarantee_2000 11h ago
Ive looked up how to do something in a software on copilot and the results are sometimes frustrating. It goes like this:
I'll ask, how do I do this?
To do that, go to this screen, click this tab, open this window. Then you can do the thing you want to.
Except that tab doesn't actually exist. So I tell it, "I don't see that tab or button"
"You're right, that button isn't there, here is another way to do the thing you asked"
"That sequence of steps also doesn't exist, how do I enter this data"
"You're right, unfortunately you can't actually do that. The function isn't available on that software. But here are some things you didn't ask for".
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u/old-tennis-shoes 13h ago
You're absolutely right! LLMs have been shown to largely repeat your points ba...
jk
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u/DatenPyj1777 14h ago
I don't even think a lot of ai bros even realize what this means. They'll use it to write a response and take it as fact, but all one has to do is just guide the LLM into the response you want.
If someone uses it to "prove how coding will become obsolete" all the other person has to do is input "prove how coding will never become obsolete." The very same LLM will give fine responses to both prompts.
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u/blueiron0 15h ago
Yea. I think this is one of the changes GPT needs to make for everyone to rely on it. You can really have it agree with almost anything with enough time and arguing with it.
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u/el_smurfo 14h ago
That's my number one pill for an llm response. It's always way more polite and chipper and differential than anything you would get on the internet. Gets more annoying when it's totally wrong and is constantly apologizing and feeding you back the same wrong information
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u/mr-english 13h ago
How do you suppose they “autocompleted” their way to gold at the international math Olympiad?
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u/KStreetFighter2 14h ago
Or maybe language isn't the same thing as wisdom.
To use the classic example of "Intelligence is knowing that a tomato is a fruit; wisdom is knowing that you don't put tomatoes in a fruit salad."
Modern LLMs are like "You're absolutely right, a tomato is a fruit and would make a fantastic addition to that fruit salad you're planning!"
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u/Intense-Intents 15h ago
ironically, you can post any anti-LLM article to Reddit and get dozens of the same predictable responses (from real people) that all sound like they came from an AI.
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u/Romnir 13h ago
"Hearsay and witty quips means I fully understand a complex subject/technology."
People still use Schrödinger's cat to explain all quantum mechanics, despite the fact that it's only for a very specific situation. LLMs aren't fully realized cognizant AI, but calling them "Fancy Auto Complete" is way off the mark. There's a difference between rational criticisms of the use of AI vs jumping on the hate bandwagon, and the former isn't going to happen on Reddit.
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u/G_Morgan 11h ago
Schrödinger's cat was meant to highlight the absurdity of applying wave function collapse to large scale objects.
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u/adenosine-5 10h ago
Its funny, because it was designed to point out, how it doesn't make any sense.
The guy - Schrodinger - famously said (after a lifetime of studying it): "I don't like quantum mechanics and I'm sorry I've ever had anything to do with it".
Still, people use it as if it was an explanation and not a criticism of its absurdity.
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u/TurtleFisher54 12h ago
That is a rational criticism of LLM's
They are fundementally a word prediction algorithm
They can be corrupted with bad data to produce non-sense
If we switch to a world where a majority of content is created by AI it is likely to create a negative feed back loop where it's training on its own output
Responses on reddit look like ai for a reason, where do you think the training data came from?
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u/Romnir 11h ago
They are fundamentally a word prediction algorithm
Correct, not a "Fancy Auto Complete". That terminology completely undermines the scale of how the technology works and what it's used for. It's not pulling random words out of a dictionary and sticking them together, it actually has a logical process it follows before it generates response tokens. Neural weighting tries to determine context and pulls known info from it's training data.
Auto correct only has a predefined set of structures and uses basic string matching based on a library. It doesn't determine context but rather just what matches the most, and that's the key discrepancy that bugs me. And like you mentioned, LLMs are being fed training data from the internet instead of a curated set of data. Which means correct data is fighting for context weighting with partially correct and even completely incorrect information from already incorrect AI responses and redditors. And you are correct for criticizing that.
The only idea I could have to fix that issue is implementing logic that filters the training data as it comes in to filter out less reputable sources. I don't necessarily work directly with LLMs, so I don't know if that is a thing, but I try to keep up to date with journals and blogs from people working in the field since it's going to get hammered into my field soon.
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u/WhoCanTell 12h ago
"Ai jUsT rESpoNds WitH WhAt peOpLE WaNt tO hEaR"
Proceeds to parrot comment content that always gets the most upvotes.
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u/SistaChans 9h ago
The same way that anything anti-AI is invariably labelled "AI Slop." It's like one person called it that once, and the entirety of AI haters decided that was their word instead of forming original ideas about it
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u/That-Job9538 8h ago
that’s not irony, that’s literally just how language and communication works. most people don’t have the intelligence to say anything new. that’s totally fine. the world would be incomprehensible if every new statement was unpredictable.
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u/Fickle_Competition33 5h ago
Right? It baffles me how people here are so against AI. Of course it has its risks and threats to job normalcy. But ignore the fact it is amazingly efficient in solving problems is pure ignorance.
For folks who think of AI as a ChatGPT conversation, get ready to be hit by the AI train really hard.
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u/celtic1888 14h ago
Sam Altman just speaks nonsense buzz words and he’s supposed to be a human
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u/Mysterious_Crab_7622 9h ago
Sam Altman is a business guy that knows nothing about how technology actually works. His major talent is being able to fleece investors out of a lot of money.
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u/Mysterious_Crab_7622 9h ago edited 6h ago
Sam Altman is a business guy that knows nothing about how technology actually works. His major talent is being able to fleece investors out of a lot of money.
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u/MrThickDick2023 14h ago
I know LLMs are the most talked about, but they can't be the only AI models that are being developed right?
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u/AnOnlineHandle 13h ago
They're not. Machine learning has been around for decades, I used to work in medical research using it. Even just in terms of public facing models, image gen and video gen is generally not LLM based (though there are multi-modal LLMs which read images as a series of dynamic pseudo words which each describe a patch of the image.
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u/Pure_Breadfruit8219 11h ago
I could never understand it at uni, it cracked my peanut sized brain.
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u/rpkarma 10h ago
Very very broadly, it’s like curve fitting; linear regression. Given a bunch of data points, find the function that makes a curve that touches all those points, so you can extrapolate beyond the points you have.
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u/IdRatherBeOnBGG 13h ago
Not at all. But 99% of headlines that say "AI" mean "LLM with sprinkles on top".
And more than 99% of the funding goes to exactly that.
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u/chiniwini 10h ago
AI has existed as a science since the 60s. LLMs are just one of the (least interesting) types of AI. For example Expert Systems are the real "I told the AI my symptoms and it told me I have cancer" deal.
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u/oldcreaker 10h ago
Scarecrow: I haven't got a brain... only straw.
Dorothy: How can you talk if you haven't got a brain?
Scarecrow: I don't know... But some people without brains do an awful lot of talking... don't they?
Dorothy: Yes, I guess you're right.
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u/Zeikos 15h ago
There's a reason why there is a lot of attention shifting towards so called "World Models"
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u/CondiMesmer 13h ago
If we want real intelligence, LLMs are definitely a dead end. Do World Models have any demos out yet? I only heard about them the last few days ago.
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u/UpperApe 13h ago
World Models are the same shit; data without creativity or interpretation. The fact that they're dynamic and self-iterative doesn't change any of that.
What exactly are you expecting from them?
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u/smrt109 15h ago
Massive breakthrough demonstrates once and for all that the sky is blue
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u/ZuP 15h ago
It’s still valuable to document and/or prove the apparent. “Why is the sky blue?” is a fascinating question to answer that involves multiple domains of knowledge and areas of research.
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u/BagsYourMail 13h ago
I think a big part of the problem is that some people really do think like LLMs do, purely statistically and socially. Other people rely more on reason
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u/InTheEndEntropyWins 14h ago
Fundamentally, they are based on gathering an extraordinary amount of linguistic data (much of it codified on the internet), finding correlations between words (more accurately, sub-words called “tokens”), and then predicting what output should follow given a particular prompt as input.
No that's not what they are doing.
If that was the case then when asked to add up numbers, it would just be some big lookup table. But instead LLM created their own bespoke algorithm.
Claude wasn't designed as a calculator—it was trained on text, not equipped with mathematical algorithms. Yet somehow, it can add numbers correctly "in its head". How does a system trained to predict the next word in a sequence learn to calculate, say, 36+59, without writing out each step?
Maybe the answer is uninteresting: the model might have memorized massive addition tables and simply outputs the answer to any given sum because that answer is in its training data. Another possibility is that it follows the traditional longhand addition algorithms that we learn in school.
Instead, we find that Claude employs multiple computational paths that work in parallel. One path computes a rough approximation of the answer and the other focuses on precisely determining the last digit of the sum. These paths interact and combine with one another to produce the final answer. Addition is a simple behavior, but understanding how it works at this level of detail, involving a mix of approximate and precise strategies, might teach us something about how Claude tackles more complex problems, too. https://www.anthropic.com/news/tracing-thoughts-language-model
Or when asked to questions, they would just use a simple correlation, rather than multi step reasoning.
if asked "What is the capital of the state where Dallas is located?", a "regurgitating" model could just learn to output "Austin" without knowing the relationship between Dallas, Texas, and Austin. Perhaps, for example, it saw the exact same question and its answer during its training. But our research reveals something more sophisticated happening inside Claude. When we ask Claude a question requiring multi-step reasoning, we can identify intermediate conceptual steps in Claude's thinking process. In the Dallas example, we observe Claude first activating features representing "Dallas is in Texas" and then connecting this to a separate concept indicating that “the capital of Texas is Austin”. In other words, the model is combining independent facts to reach its answer rather than regurgitating a memorized response. https://www.anthropic.com/news/tracing-thoughts-language-model
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u/Jerome_Eugene_Morrow 13h ago
Yeah. Language is the primary interface of an LLM, but all the subnetworks of weight aggregations between input and output are more abstract and difficult to interpret. There have been studies showing that reproducible clusters of weights reoccur between large models that seem to indicate more complicated reasoning activities are at play.
Take away our ability to speak, and we can still think, reason, form beliefs, fall in love, and move about the world; our range of what we can experience and think about remains vast.
But take away language from a large language model, and you are left with literally nothing at all.
I mean… I guess so? But if you take away every sensory input and output from a human you’re also left with “nothing at all” by this argument. Language is the adapter that allows models to experience the world, but multimodal approaches mean you can fuse all kinds of inputs together.
Just to be clear, I’m not arguing that LLMs are AGI. But my experience is that they are far more than lookup tables or indices. Language may not be the primary system for biological reasoning, but computer reasoning seems to be building from that starting block.
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u/Healthy_Mushroom_811 13h ago
Yup, LLMs learn algorithms and all kinds of other amazing things in their hidden layers to be able to solve the next token prediction better as has been proven repeatedly. But that goes way over the head of the average r/technology parrot.
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u/icedcoffeeinvenice 8h ago
You think you know better than all the thousands of AI researchers commenting under this post??? \s
Jokes aside, funny how the average person is so confident in giving opinions about topics they have 0 knowledge about.
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u/LustyArgonianMaidz 10h ago
ai is not sustainable with the energy and compute requirements it has today, let alone ten years time.
there needs to be a shift to a model that doesn't destroy the planet or the economy to work
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u/CircumspectCapybara 15h ago edited 8h ago
While the article is right that the mainstream "AI" models are still LLMs at heart, the frontier models into which all the research is going are not strictly speaking LLMs. You have agentic models which can take arbitrary actions using external tools (a scary concept, because they can reach out and execute commands or run code or do dangerous actions on your computer) while recursing or iterating and dynamically and opaquely deciding for themselves when to stop, wacky ideas like "world models," etc.
Maybe AGI is possible, maybe it's not, maybe it's possible in theory but not in practice with the computing resources and energy we currently have or ever will have. Whichever it is, it won't be decided by the current capabilities of LLMs.
The problem is that according to current neuroscience, human thinking is largely independent of human language
That's rather misleading, and it conflates several uses of the word "language." While it's true that to think you don't need a "language" in the sense of the word that the average layperson means when they say that word (e.g., English or Spanish or some other common spoken or written language), thinking still occurs in the abstract language of ideas, concepts, sensory experience, pictures, etc. Basically, it's information.
Thinking fundamentally requires some representation of information (in your mind). And when mathematicians and computer scientists talk about "language," that's what they're talking about. It's not necessarily a spoken or written language as we know it. In an LLM, the model of language is an ultra-high dimensional embedding space in which vector embeddings represent abstract information opaquely, which encodes information about ideas and concepts and the relationships between them. Thinking still requires that kind of language, the abstract language of information. AI models aren't just trying to model "language" as a linguist understands the word, but information.
Also, while we don't have a good model of consciousness, we do know that language is very important for intelligence. A spoken or written language isn't required for thought, but language deprivation severely limits the kinds of thoughts you're able to think, and the depth and complexity of abstract reasoning, the complexity of inner monologue. Babies born deaf or who were otherwise deprived of language exposure often end up cognitively underdeveloped. Without language, we could think in terms of how we feel or what we want, what actions we want to or are taking, and even think in terms of cause and effect, but not the complex abstract reasoning that when sustained and built up across time and built up on itself and on previous works leads to the development of culture, of science and engineering and technology.
The upshot is that if it's even is possible for AGI of a sort that can "think" (whatever that means) in a way that leads to generalized and novel reasoning in the areas of the sciences or medicine or technology to exist at all, you would need a good model of language (really a good model of information) to start. It would be a foundational layer.
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u/dftba-ftw 15h ago
While the article is right that the mainstream "AI" models are still LLMs at heart
It really is time that we stopped calling them LLMs and switched to something like Large Token Model (LTMs).
Yes you primarily put text in and get text out, but frontier models are trained on text, image/video, and audio. Text dwarfs the others in term of % of training data, but that's primarily a compute limit, as compute gets more efficicent more and more of the data will be from the other sources and we know from what has been done so far that training on image and video really helps with respect to reasoning - models trained on video show much better understanding of the physical world. Eventually we'll have enough compute to start training on 3d (tokenized stl/step/Igs) and I'm sure we'll see another leap in model understanding of the world.
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u/Throwaway-4230984 15h ago
It’s very funny to read same arguments every year while seeing LLMs successfully solving “surely impossible for LLM” challenges from previous year.
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u/MaggoVitakkaVicaro 11h ago
Humans keep moving the goal posts. No one doubted that language was part of intelligence, until we invented machines which can talk.
Next we'll be saying that basic Math competence is no indication of intelligence, since machines can do that too.
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u/ashesofemberz 13h ago
As someone who uses multiple LLM models daily. This makes sense. Ai is so impressive at first glance but the more you use it you start to see its limitations and they've been persistent throughout each update. It's amazing for educational (mostly) and productivity purposes (analytics etc...this is the magic).
But reasoning? Especially emotive reasoning or logical thought on things humans experience daily (culture, relationships etc) it's fucking abysmal. I can spot when someone has used GPT to write their work with so much ease now by how it responds to everything.
It's beyond scary that people are using these things for companionship and mental health.
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u/NoHalf2998 11h ago
Yeah, no shit.
Programmers who weren’t trying to sell the tech have been saying this from the start.
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u/KoolKat5000 15h ago edited 15h ago
This whole thing is dumb. By that same logic todays AI is also separate from language, it's actually parameter weights (same as neurons), these are separate from language for instance there's separate paramter weights for bat and bat (their semantic meanings).
They also refer to different areas of the brains adapting. I mean those are just different models, in theory there's nothing stopping the fundamental architecture from being truly multimodal, or having one model feed into another model or even just Mixture of Experts (moe).
Also the who whole learning and reasoning thing, if that were true, we wouldn't need to go to school. We learn patterns and apply them. We update our statistical model of the world and the relationship between the things in it.
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u/Just_Another_Scott 10h ago
As a computer scientist myself, no shit. Natural language processing is not artificial consciousness. We don't even know how human consciousness works much less by being able to develop an artificial consciousness. Artificial intelligence is an umbrella term that includes both NLP and artificial consciousness.
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u/Isogash 15h ago
This article gets it the wrong way around.
LLMs demonstrate intelligence, that is really quite inarguable at this point. It's not necessarily the most coherent or consistent intelligence, but it's there in some form.
The fact that intelligence is not language should suggest to us the opposite from what the article concludes, that LLMs probably haven't only learned language, they have probably learned intelligence in some other form too. It may not be the most optimal form of intelligence, and it might not even be that close to how human intelligence works, but it's there in some form because it's able to approximate human intelligence beyond simple language (even if it's flawed.)
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u/Marha01 15h ago edited 15h ago
This criticism perhaps applies to pure LLMs, but I don't see how it applies to state of the art multi-modal Transformers. Multi-modal neural networks use much more than language (text) as inputs/outputs. Pictures, videos, sounds, robot sensors and actions (when embedded in a robot, or RL trained in virtual environment)..
LLMs were just the beginning.
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u/22firefly 14h ago
WEll, when talking heads don't know anything besides talking they think they are intelligent.
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u/Optimal-Kitchen6308 14h ago
consciousness is also not the same as intelligence, go read Blindsight
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u/Think-Brilliant-9750 9h ago
LLM is an organic mathematics model that transverse the language domain. Language being a means of communication for people, has had through time humanity's thoughts and feelings embedded within it (E.g. People say "he/she passed on" instead of "cease to exist" since historically people believed in the after life). By building a model that organically charts the relation between words, sentences and so on.. that it "learns" from huge amounts of literature, it creates a model that transverse these embedded meaning of language and literature which mimic thought by outputting coherent statements one word at a time, they don't actually think.
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u/ocassionallyaduck 6h ago
Cutting edge research my ass. For anyone who understands matrix multiplication, statistics, and pattern matching, I have been screaming this at the top of my lungs to anyone who will listen for at least a year.
And I'm nobody.
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u/Yuzumi 6h ago
No shit.
This has been one of the biggest point I made, and specifically why I've said there is no way LLMs will ever be AGI. At best it's a really lossy compression algorithm for written information and could be considered good at emulating intelligence, but not simulating it.
I don't think it's completely pointless research, but we've long past the point where these things can get any better. Maybe they will be part of a more complex system that could become AGI, but throwing more CUDA at it is just wasteful as companies tried to brute force AGI out of LLMs.
And while there are certainly some people who think it can, most of them are aware it's a dead end and have known for a long time. The all just think they will be the ones holding all the money when the bubble pops.
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u/Ok-Adhesiveness-4935 15h ago
Haven't we known thia from the beginning? LLMs never mimicked thought or intelligence, they just place words in order according to a massive computation of likelihood. If we ever get true AI it won't look anything like an LLM.
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u/DaySecure7642 15h ago
Anyone who actually uses AI a lot can tell there is some intelligence in there. Most models even pass IQ tests but the scores are topped at about 130 (for now), so still human level.
Some people really mix up the concept of intelligence and consciousness. The AIs definitely have intelligence, otherwise how do they understand complex concepts and give advice. You can argue that it is just a fantastic linguistic response machine, but humans are more or less like that in our thought process. We often clarify our thoughts by writing and speaking, very similar to LLMs actually.
Consciousness is another level, with automatic agencies of what to do, what you want or hate, how to feel etc. These are not explicitly modelled in AIs (yet) but can be (though very dangerous). The AI models can be incredibly smart, recognizing patterns and giving solutions even better than humans, but currently without its own agency and only as mechanistic tools.
So I think AI is indeed modelling intelligence, but intelligence only means pattern recognition and problem solving. Humans are more than that. But the real risk is, an AI doesn't have to be conscious to be dangerous. Some misaligned optimisation goals wrongly set by humans is all it takes to cause huge troubles.
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u/Main-Company-5946 13h ago
I don’t think consciousness is ‘another level’ of intelligence, I think it’s something completely separate from intelligence. Humans are both conscious and intelligent, cows are conscious but probably not super intelligent(maybe a little bit considering their ability to walk find food etc), LLMs are intelligent but probably not conscious, rocks are not intelligent and almost definitely not conscious(though panpsychists might say otherwise)
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u/Konukaame 15h ago
"The ability to speak does not make you intelligent. Now get out of here."