They’re kind of right. LLMs are not general intelligence and there’s not much evidence to suggest that LLMs will lead to general intelligence. A lot of the hype around AI is manufactured by VCs and companies that stand to make a lot of money off of the AI branding/hype.
I believe they were implying that a lot of the people who say “it’s not real AI it’s just an LLM” are simply parroting what they’ve heard.
Which is a fair point, because AI has never meant “general AI”, it’s an umbrella term for a wide variety of intelligence like tasks as performed by computers.
Autocorrect on your phone is a type of AI, because it compares what words you type against a database of known words, compares what you typed to those words via a “typo distance”, and adds new words to it’s database when you overrule it so it doesn’t make the same mistake.
It’s like saying a motorcycle isn’t a real vehicle because a real vehicle has two wings, a roof, and flies through the air filled with hundreds of people.
I’ve often seen people on Lemmy confidently state that current “AI” thinks and learns exactly like humans and that LLMs work exactly like human brains, etc.
Are you sure this wasn’t just people stating that when it comes to training on art there is no functional difference in the sense that both humans and AI need to see art to make it?
Weird, I don’t think I’ve ever seen that even remotely claimed.
Closest I think I’ve come is the argument that legally, AI learning systems are similar to how humans learn, namely storing information about information.
It’s usually some rant about “brains are just probability machines as well” or “every artists learns from thousands of pictures of other artists, just as image generator xy does”.
Which is a fair point, because AI has never meant “general AI”, it’s an umbrella term for a wide variety of intelligence like tasks as performed by computers.
Do you mean in the everyday sense or the academic sense? I think this is why there’s such grumbling around the topic. Academically speaking that may be correct, but I think for the general public, AI has been more muddled and presented in a much more robust, general AI way, especially in fiction. Look at any number of scifi movies featuring forms of AI, whether it’s the movie literally named AI or Terminator or Blade Runner or more recently Ex Machina.
Each of these technically may be presenting general AI, but for the public, it’s just AI. In a weird way, this discussion is sort of an inversion of what one usually sees between academics and the public. Generally academics are trying to get the public not to use technical terms loosely, yet here some of the public is trying to get some of the tech/academic sphere to not, at least as they think, use technical terms loosely.
Arguably it’s from a misunderstanding, but if anyone should understand the dynamics of language, you’d hope it would be those trying to calibrate machines to process language.
Well, that’s the issue at the heart of it I think.
How much should we cater our choice of words to those who know the least?
I’m not an academic, and I don’t work with AI, but I do work with computers and I know the distinction between AI and general AI.
I have a little irritation at the theme, given I work in the security industry and it’s now difficult to use the more common abbreviation for cryptography without getting Bitcoin mixed up in everything.
All that aside, the point is that people talking about how it’s not “real AI” often come across as people who don’t know what they’re talking about, which was the point of the image.
All that aside, the point is that people talking about how it’s not “real AI” often come across as people who don’t know what they’re talking about, which was the point of the image.
The funny part is, as I mention in my comment, isn’t that how both parties to these conversations feel? The problem is they’re talking past each other, but the worst part is, arguably the more educated participant should be more apt to recognize this and clarify or better yet, ask for clarification so they can see where the disconnect is emerging to improve communication.
Also, let’s remember that it’s not the laypeople describing the technology in general personified terms like “learning” or “hallucinating”, which furthers some of the grumbling.
Well, I don’t generally expect an academic level of discourse out of image macros found on the Internet.
Usually when I see people talking about it, I do see people making clarifying comments and asking questions like you describe. Sorta like when I described how AI is an umbrella term.
I’m not sure I’d say that learning and hallucinating are personified terms. We see both of those out of any organism complex enough to have something that works like a nervous system, for example.
Pretty sure the meme format is for something you get extremely worked up about and want to passionately tell someone, even in inappropriate moments, but no one really gives a fuck
People who don’t understand or use AI think it’s less capable than it is and claim it’s not AGI (which no one else was saying anyways) and try to make it seem like it’s less valuable because it’s “just using datasets to extrapolate, it doesn’t actually think.”
Guess what you’re doing right now when you “think” about something? That’s right, you’re calling up the thousands of experiences that make up your “training data” and using it to extrapolate on what actions you should take based on said data.
You know how to parallel park because you’ve assimilated road laws, your muscle memory, and the knowledge of your cars wheelbase into a single action. AI just doesn’t have sapience and therefore cannot act without input, but the process it does things with is functionally similar to how we make decisions, the difference is the training data gets input within seconds as opposed to being built over a lifetime.
That’s true of any technology. As someone who is a programmer, has studied computer science, and does understand LLMs, this represents a massive leap in capability. Is it AGI? No. Is it a potential paradigm shift? Yes. This isn’t pure hype like Crypto was, there is a core of utility here.
Crypto was never pure hype either. Decentralized currency is an important thing to have, it’s just shitty it turned into some investment speculative asset rather than a way to buy drugs online without the glowies looking
Yeah I studied CS and work in IT Ops, I’m not claiming this shit is Cortana from Halo, but it’s also not NFTs. If you can’t see the value you haven’t used it for anything serious, cause it’s taking jobs left and right.
If you’ve ever actually used any of these algorithms it becomes painfully obvious they do not “think”. Give it a task slightly more complex/nuanced than what it has been trained on and you will see it draws obviously false conclusions that would be obviously wrong had any thought actual taken place. Generalization is not something they do, which is a fundamental part of human problem solving.
Depends on what you mean by general intelligence. I’ve seen a lot of people confuse Artificial General Intelligence and AI more broadly. Even something as simple as the K-nearest neighbor algorithm is artificial intelligence, as this is a much broader topic than AGI.
An artificial general intelligence (AGI) is a hypothetical type of intelligent agent which, if realized, could learn to accomplish any intellectual task that human beings or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks.
If some task can be represented through text, an LLM can, in theory, be trained to perform it either through fine-tuning or few-shot learning. The question then is how general do LLMs have to be for one to consider them to be AGIs, and there’s no hard metric for that question.
I can’t pass the bar exam like GPT-4 did, and it also has a lot more general knowledge than me. Sure, it gets stuff wrong, but so do humans. We can interact with physical objects in ways that GPT-4 can’t, but it is catching up. Plus Stephen Hawking couldn’t move the same way that most people can either and we certainly wouldn’t say that he didn’t have general intelligence.
I’m rambling but I think you get the point. There’s no clear threshold or way to calculate how “general” an AI has to be before we consider it an AGI, which is why some people argue that the best LLMs are already examples of general intelligence.
Depends on what you mean by general intelligence. I’ve seen a lot of people confuse Artificial General Intelligence and AI more broadly. Even something as simple as the K-nearest neighbor algorithm is artificial intelligence, as this is a much broader topic than AGI.
Well, I mean the ability to solve problems we don’t already have the solution to. Can it cure cancer? Can it solve the p vs np problem?
And by the way, wikipedia tags that second definition as dubious as that is the definition put fourth by OpenAI, who again, has a financial incentive to make us believe LLMs will lead to AGI.
Not only has it not been proven whether LLMs will lead to AGI, it hasn’t even been proven that AGIs are possible.
If some task can be represented through text, an LLM can, in theory, be trained to perform it either through fine-tuning or few-shot learning.
No it can’t. If the task requires the LLM to solve a problem that hasn’t been solved before, it will fail.
I can’t pass the bar exam like GPT-4 did
Exams often are bad measures of intelligence. They typically measure your ability to consume, retain, and recall facts. LLMs are very good at that.
Ask an LLM to solve a problem without a known solution and it will fail.
We can interact with physical objects in ways that GPT-4 can’t, but it is catching up. Plus Stephen Hawking couldn’t move the same way that most people can either and we certainly wouldn’t say that he didn’t have general intelligence.
The ability to interact with physical objects is very clearly not a good test for general intelligence and I never claimed otherwise.
I know the second definition was proposed by OpenAI, who obviously has a vested interest in this topic, but that doesn’t mean it can’t be a useful or informative conceptualization of AGI, after all we have to set some threshold for the amount of intelligence AI needs to display and in what areas for it to be considered an AGI. Their proposal of an autonomous system that surpasses humans in economically valuable tasks is fairly reasonable, though it’s still pretty vague and very much debatable, which is why this isn’t the only definition that’s been proposed.
Your definition is definitely more peculiar as I’ve never seen anyone else propose something like it, and it also seems to exclude humans since you’re referring to problems we can’t solve.
The next question then is what problems specifically AI would need to solve to fit your definition, and with what accuracy. Do you mean solve any problem we can throw at it? At that point we’d be going past AGI and now we’re talking about artificial superintelligence…
Not only has it not been proven whether LLMs will lead to AGI, it hasn’t even been proven that AGIs are possible.
By your definition AGI doesn’t really seem possible at all. But of course, your definition isn’t how most data scientists or people in general conceptualize AGI, which is the point of my comment. It’s very difficult to put a clear-cut line on what AGI is or isn’t, which is why there are those like you who believe it will never be possible, but there are also those who argue it’s already here.
No it can’t. If the task requires the LLM to solve a problem that hasn’t been solved before, it will fail.
Ask an LLM to solve a problem without a known solution and it will fail.
That’s simply not true. That’s the whole point of the concept of generalization in AI and what the few-shot and zero-shot metrics represent - LLMs solving problems represented in text with few or no prior examples by reasoning beyond what they saw in the training data. You can actually test this yourself by simply signing up to use ChatGPT since it’s free.
Exams often are bad measures of intelligence. They typically measure your ability to consume, retain, and recall facts. LLMs are very good at that.
So are humans. We’re also deterministic machines that output some action depending on the inputs we get through our senses, much like an LLM outputs some text depending on the inputs it received, plus as I mentioned they can reason beyond what they’ve seen in the training data.
The ability to interact with physical objects is very clearly not a good test for general intelligence and I never claimed otherwise.
I wasn’t accusing you of anything, I was just pointing out that there are many things we can argue require some degree of intelligence, even physical tasks. The example in the video requires understanding the instructions, the environment, and how to move the robotic arm in order to complete new instructions.
I find LLMs and AGI interesting subjects and was hoping to have a conversation on the nuances of these topics, but it’s pretty clear that you just want to turn this into some sort of debate to “debunk” AGI, so I’ll be taking my leave.
I agree, there is no formal definition for AGI so a bit silly to discuss that really. Funnily enough I inadvertantly wrote the nearest neighbour algorithm to model swarming behavour back when I was an undergrad and didn’t even consider it rudimentary AI.
Can I ask what your take on the possibility of neural networks understanding what they are doing is?
It depends a lot on how we perceive “intelligence”. It’s a lot more vague of a term than most, so people have very different views of it. Some people might have the idea of it meaning the response to stimuli & the output (language or art or any other form) being indistinguishable from humans. But many people may also agree that whales/dolphins have the same level of, or superior, “intelligence” to humans. The term is too vague to really prescribe with confidence, and more importantly people often use it to mean many completely different concepts (“intelligence” as a measurable/quantifiable property of either how quickly/efficiently a being can learn or use knowledge or more vaguely its “capacity to reason”, “intelligence” as the idea of “consciousness” in general, “intelligence” to refer to amount of knowledge/experience one currently has or can memorize, etc.)
In computer science “artificial intelligence” has always simply referred to a program making decisions based on input. There was never any bar to reach for how “complex” it had to be to be considered AI. That’s why minecraft zombies or shitty FPS bots are “AI”, or a simple algorithm made to beat table games are “AI”, even though clearly they’re not all that smart and don’t even “learn”.
Even sentience is on a scale. Even cows or dogs or parrots or crows are sentient, but not as much as we are. Computers are not sentient yet, but one day they will be. And then soon after they will be more sentient than us. They’ll be able to see their own brains working, analyze their own thoughts and emotions(?) in real time and be able to achieve a level of self reflection and navel gazing undreamed of by human minds! :D
But also the people who seem to think we need a magic soul to perform useful work is way way too high.
The main problem is Idiots seem to have watched one too many movies about robots with souls and gotten confused between real life and fantasy - especially shitty journalists way out their depth.
This big gotcha ‘they don’t live upto the hype’ is 100% people who heard ‘ai’ and thought of bad Will Smith movies. LLMs absolutely live upto the actual sensible things people hoped and have exceeded those expectations, they’re also incredibly good at a huge range of very useful tasks which have traditionally been considered as requiring intelligence but they’re not magically able everything, of course they’re not that’s not how anyone actually involved in anything said they would work or expected them to work.
Even if LLM’s can’t be said to have ‘true understanding’ (however you’re choosing to define it), there is very little to suggest they should be able to understand predict the correct response to a particular context, abstract meaning, and intent with what primitive tools they were built with.
If there’s some as-yet uncrossed threshold to a bare-minimum ‘understanding’, it’s because we simply don’t have the language to describe what that threshold is or know when it has been crossed. If the assumption is that ‘understanding’ cannot be a quality granted to a transformer-based model -or even a quality granted to computers generally- then we need some other word to describe what LLM’s are doing, because ‘predicting the next-best word’ is an insufficient description for what would otherwise be a slight-of-hand trick.
There’s no doubt that there’s a lot of exaggerated hype around these models and LLM companies, but some of these advancements published in 2022 surprised a lot of people in the field, and their significance shouldn’t be slept on.
Certainly don’t trust the billion-dollar companies hawking their wares, but don’t ignore the technology they’re building, either.
You are best off thinking of LLMs as highly advanced auto correct. They don’t know what words mean. When they output a response to your question the only process that occurred was “which words are most likely to come next”.
That’s only true on a very basic level, I understand that Turings maths is complex and unintuitive even more so than calculus but it’s a very established fact that relatively simple mathematical operations can have emergent properties when they interact to have far more complexity than initially expected.
The same way the giraffe gets its spots the same way all the hardware of our brain is built, a strand of code is converted into physical structures that interact and result in more complex behaviours - the actual reality is just math, and that math is almost entirely just probability when you get down to it. We’re all just next word guessing machines.
We don’t guess words like a Markov chain instead use a rather complex token system in our brain which then gets converted to words, LLMs do this too - that’s how they can learn about a subject in one language then explain it in another.
Calling an LLM predictive text is a fundamental misunderstanding of reality, it’s somewhat true on a technical level but only when you understand that predicting the next word can be a hugely complex operation which is the fundamental math behind all human thought also.
Plus they’re not really just predicting one word ahead anymore, they do structured generation much like how image generators do - first they get the higher level principles to a valid state then propagate down into structure and form before making word and grammar choices. You can manually change values in the different layers and see the output change, exploring the latent space like this makes it clear that it’s not simply guessing the next word but guessing the next word which will best fit into a required structure to express a desired point - I don’t know how other people are coming up with sentences but that feels a lot like what I do
LLMs don’t “learn” they literally don’t have the capacity to “learn”. We train them on an insane amount of text and then the LLMs job is to produce output that looks like that text. That’s why when you attempt to correct it nothing happens. It can’t learn, it doesn’t have the capacity to.
Humans aren’t “word guessing machines”. Humans produce language with intent and meaning. This is why you and I can communicate. We use language to represent things. When I say “Tree” you know what that is because it’s the word we use to describe an object we all know about. LLMs don’t know what a tree is. They can use “tree” in a sentence correctly but they don’t know what it means. They can even translate it to another language but they still don’t know what “tree” means. What they know is generating text that looks like what they were trained on.
Even if LLM’s can’t be said to have ‘true understanding’ (however you’re choosing to define it), there is very little to suggest they should be able to understand predict the correct response to a particular context, abstract meaning, and intent with what primitive tools they were built with.
Did you mean “shouldn’t”? Otherwise I’m very confused by your response
There’s no reason to expect a program that calculates the probability of the next most likely word in a sentence should be able to do anything more than string together an incoherent sentence, let alone correctly answer even an arbitrary question
It’s like using a description for how covalent bonds are formed as an explanation for how it is you know when you need to take a shit.
Yes. But the more advanced LLMs get, the less it matters in my opinion. I mean of you have two boxes, one of which is actually intelligent and the other is “just” a very advanced parrot - it doesn’t matter, given they produce the same output. I’m sure that already LLMs can surpass some humans, at least at certain disciplines. In a couple years the difference of a parrot-box and something actually intelligent will only merely show at the very fringes of massively complicated tasks. And that is way beyond the capability threshold that allows to do nasty stuff with it, to shed a dystopian light on it.
I mean of you have two boxes, one of which is actually intelligent and the other is “just” a very advanced parrot - it doesn’t matter, given they produce the same output.
You’re making a huge assumption; that an advanced parrot produces the same output as something with general intelligence. And I reject that assumption. Something with general intelligence can produce something novel. An advanced parrot can only repeat things it’s already heard.
LLMs can’t produce anything without being prompted by a human. There’s nothing intelligent about them. Imo it’s an abuse of the word intelligence since they have exactly zero autonomy.
I use LLMs to create things no human has likely ever said and it’s great at it, for example
‘while juggling chainsaws atop a unicycle made of marshmallows, I pondered the existential implications of the colour blue on a pineapples dream of becoming a unicorn’
When I ask it to do the same using neologisms the output is even better, one of the words was exquimodal which I then asked for it to invent an etymology and it came up with one that combined excuistus and modial to define it as something beyond traditional measures which fits perfectly into the sentence it created.
You can’t ask a parrot to invent words with meaning and use them in context, that’s a step beyond repetition - of course it’s not full dynamic self aware reasoning but it’s certainly not being a parrot
If you ask it to make up nonsense and it does it then you can’t get angry lol. I normally use it to help analyse code or write sections of code, sometimes to teach me how certain functions or principles work - it’s incredibly good at that, I do need to verify it’s doing the right thing but I do that with my code too and I’m not always right either.
As a research tool it’s great at taking a basic dumb description and pointing me to the right things to look for, especially for things with a lot of technical terms and obscure areas.
And yes they can occasionally make mistakes or invent things but if you ask properly and verify what you’re told then it’s pretty reliable, far more so than a lot of humans I know.
The difference is that you can throw enough bad info at it that it will start paroting that instead of factual information because it doesn’t have the ability to criticize the information it receives whereas an human can be told that the sky is purple with orange dots a thousand times a day and it will always point at the sky and tell you “No.”
To make the analogy actually comparable the human in question would need to be learning about it for the first time (which is analogous to the training data) and in that case you absolutely could convince the small child of that. Not only would they believe it if told enough times by an authority figure, you could convince them that the colors we see are different as well, or something along the lines of giving them bad data.
A fully trained AI will tell you that you’re wrong if you told it the sky was orange, it’s not going to just believe you and start claiming it to everyone else it interacts with. It’s been trained to know the sky is blue and won’t deviate from that outside of having its training data modified. Which is like brainwashing an adult human, in which case yeah you absolutely could have them convinced the sky is orange. We’ve got plenty of information on gaslighting, high control group and POW psychology to back that up too.
Feed LLMs all new data that’s false and it will regurgitate it as being true even if it had previously been fed information that contradicts it, it doesn’t make the difference between the two because there’s no actual analysis of what’s presented. Heck, even without intentionally feeding them false info, LLMs keep inventing fake information.
Feed an adult new data that’s false and it’s able to analyse it and make deductions based on what they know already.
We don’t compare it to a child or to someone that was brainwashed because it makes no sense to do so and it’s completely disingenuous. “Compare it to the worst so it has a chance to win!” Hell no, we need to compare it to the people that are references in their field because people will now be using LLMs as a reference!
Ha ha yeah humans sure are great at not being convinced by the opinions of other people, that’s why religion and politics are so simple and society is so sane and reasonable.
Helen Keller would belive you it’s purple.
If humans didn’t have eyes they wouldn’t know the colour of the sky, if you give an ai a colour video feed of outside then it’ll be able to tell you exactly what colour the sky is using a whole range of very accurate metrics.
This is one of the worst rebuttals I’ve seen today because you aren’t addressing the fact that the LLM has zero awareness of anything. It’s not an intelligence and never will be without additional technologies built on top of it.
Why would I rebut that? I’m simply arguing that they don’t need to be ‘intelligent’ to accurately determine the colour of the sky and that if you expect an intelligence to know the colour of the sky without ever seeing it then you’re being absurd.
The way the comment I responded to was written makes no sense to reality and I addressed that.
Again as I said in other comments you’re arguing that an LLM is not will smith in I Robot and or Scarlett Johansson playing the role of a usb stick but that’s not what anyone sane is suggesting.
A fork isn’t great for eating soup, neither is a knife required but that doesn’t mean they’re not incredibly useful eating utensils.
Try thinking of an LLM as a type of NLP or natural language processing tool which allows computers to use normal human text as input to perform a range of tasks. It’s hugely useful and unlocks a vast amount of potential but it’s not going to slap anyone for joking about it’s wife.
People do that too, actually we do it a lot more than we realise. Studies of memory for example have shown we create details that we expect to be there to fill in blanks and that we convince ourselves we remember them even when presented with evidence that refutes it.
A lot of the newer implementations use more complex methods of fact verification, it’s not easy to explain but essentially it comes down to the weight you give different layers. GPT 5 is already training and likely to be out around October but even before that we’re seeing pipelines using LLM to code task based processes - an LLM is bad at chess but could easily install stockfish in a VM and beat you every time.
They’re kind of right. LLMs are not general intelligence and there’s not much evidence to suggest that LLMs will lead to general intelligence. A lot of the hype around AI is manufactured by VCs and companies that stand to make a lot of money off of the AI branding/hype.
Yeah this sounds about right. What was OP implying I’m a bit lost?
I believe they were implying that a lot of the people who say “it’s not real AI it’s just an LLM” are simply parroting what they’ve heard.
Which is a fair point, because AI has never meant “general AI”, it’s an umbrella term for a wide variety of intelligence like tasks as performed by computers.
Autocorrect on your phone is a type of AI, because it compares what words you type against a database of known words, compares what you typed to those words via a “typo distance”, and adds new words to it’s database when you overrule it so it doesn’t make the same mistake.
It’s like saying a motorcycle isn’t a real vehicle because a real vehicle has two wings, a roof, and flies through the air filled with hundreds of people.
I’ve often seen people on Lemmy confidently state that current “AI” thinks and learns exactly like humans and that LLMs work exactly like human brains, etc.
Are you sure this wasn’t just people stating that when it comes to training on art there is no functional difference in the sense that both humans and AI need to see art to make it?
Weird, I don’t think I’ve ever seen that even remotely claimed.
Closest I think I’ve come is the argument that legally, AI learning systems are similar to how humans learn, namely storing information about information.
It’s usually some rant about “brains are just probability machines as well” or “every artists learns from thousands of pictures of other artists, just as image generator xy does”.
Do you mean in the everyday sense or the academic sense? I think this is why there’s such grumbling around the topic. Academically speaking that may be correct, but I think for the general public, AI has been more muddled and presented in a much more robust, general AI way, especially in fiction. Look at any number of scifi movies featuring forms of AI, whether it’s the movie literally named AI or Terminator or Blade Runner or more recently Ex Machina.
Each of these technically may be presenting general AI, but for the public, it’s just AI. In a weird way, this discussion is sort of an inversion of what one usually sees between academics and the public. Generally academics are trying to get the public not to use technical terms loosely, yet here some of the public is trying to get some of the tech/academic sphere to not, at least as they think, use technical terms loosely.
Arguably it’s from a misunderstanding, but if anyone should understand the dynamics of language, you’d hope it would be those trying to calibrate machines to process language.
Well, that’s the issue at the heart of it I think.
How much should we cater our choice of words to those who know the least?
I’m not an academic, and I don’t work with AI, but I do work with computers and I know the distinction between AI and general AI.
I have a little irritation at the theme, given I work in the security industry and it’s now difficult to use the more common abbreviation for cryptography without getting Bitcoin mixed up in everything.
All that aside, the point is that people talking about how it’s not “real AI” often come across as people who don’t know what they’re talking about, which was the point of the image.
The funny part is, as I mention in my comment, isn’t that how both parties to these conversations feel? The problem is they’re talking past each other, but the worst part is, arguably the more educated participant should be more apt to recognize this and clarify or better yet, ask for clarification so they can see where the disconnect is emerging to improve communication.
Also, let’s remember that it’s not the laypeople describing the technology in general personified terms like “learning” or “hallucinating”, which furthers some of the grumbling.
Well, I don’t generally expect an academic level of discourse out of image macros found on the Internet.
Usually when I see people talking about it, I do see people making clarifying comments and asking questions like you describe. Sorta like when I described how AI is an umbrella term.
I’m not sure I’d say that learning and hallucinating are personified terms. We see both of those out of any organism complex enough to have something that works like a nervous system, for example.
I believe OP is attempting to take on an army of straw men in the form of a poorly chosen meme template.
No people say this constantly it’s not just a strawman
I think OP implied that AI is neat.
I guess that no matter what they are or what you call them they still can be useful
Pretty sure the meme format is for something you get extremely worked up about and want to passionately tell someone, even in inappropriate moments, but no one really gives a fuck
People who don’t understand or use AI think it’s less capable than it is and claim it’s not AGI (which no one else was saying anyways) and try to make it seem like it’s less valuable because it’s “just using datasets to extrapolate, it doesn’t actually think.”
Guess what you’re doing right now when you “think” about something? That’s right, you’re calling up the thousands of experiences that make up your “training data” and using it to extrapolate on what actions you should take based on said data.
You know how to parallel park because you’ve assimilated road laws, your muscle memory, and the knowledge of your cars wheelbase into a single action. AI just doesn’t have sapience and therefore cannot act without input, but the process it does things with is functionally similar to how we make decisions, the difference is the training data gets input within seconds as opposed to being built over a lifetime.
People who aren’t programmers, haven’t studied computer science, and don’t understand LLMs are much more impressed by LLMs.
That’s true of any technology. As someone who is a programmer, has studied computer science, and does understand LLMs, this represents a massive leap in capability. Is it AGI? No. Is it a potential paradigm shift? Yes. This isn’t pure hype like Crypto was, there is a core of utility here.
Crypto was never pure hype either. Decentralized currency is an important thing to have, it’s just shitty it turned into some investment speculative asset rather than a way to buy drugs online without the glowies looking
Crypto solves a few theoretical problems and creates a few real ones
Yeah I studied CS and work in IT Ops, I’m not claiming this shit is Cortana from Halo, but it’s also not NFTs. If you can’t see the value you haven’t used it for anything serious, cause it’s taking jobs left and right.
In my experience it’s the opposite, but the emotional reaction isn’t so much being impressed as being afraid and claiming it’s just all plagiarism
If you’ve ever actually used any of these algorithms it becomes painfully obvious they do not “think”. Give it a task slightly more complex/nuanced than what it has been trained on and you will see it draws obviously false conclusions that would be obviously wrong had any thought actual taken place. Generalization is not something they do, which is a fundamental part of human problem solving.
Make no mistake: they are text predictors.
Depends on what you mean by general intelligence. I’ve seen a lot of people confuse Artificial General Intelligence and AI more broadly. Even something as simple as the K-nearest neighbor algorithm is artificial intelligence, as this is a much broader topic than AGI.
Wikipedia gives two definitions of AGI:
If some task can be represented through text, an LLM can, in theory, be trained to perform it either through fine-tuning or few-shot learning. The question then is how general do LLMs have to be for one to consider them to be AGIs, and there’s no hard metric for that question.
I can’t pass the bar exam like GPT-4 did, and it also has a lot more general knowledge than me. Sure, it gets stuff wrong, but so do humans. We can interact with physical objects in ways that GPT-4 can’t, but it is catching up. Plus Stephen Hawking couldn’t move the same way that most people can either and we certainly wouldn’t say that he didn’t have general intelligence.
I’m rambling but I think you get the point. There’s no clear threshold or way to calculate how “general” an AI has to be before we consider it an AGI, which is why some people argue that the best LLMs are already examples of general intelligence.
Well, I mean the ability to solve problems we don’t already have the solution to. Can it cure cancer? Can it solve the p vs np problem?
And by the way, wikipedia tags that second definition as dubious as that is the definition put fourth by OpenAI, who again, has a financial incentive to make us believe LLMs will lead to AGI.
Not only has it not been proven whether LLMs will lead to AGI, it hasn’t even been proven that AGIs are possible.
No it can’t. If the task requires the LLM to solve a problem that hasn’t been solved before, it will fail.
Exams often are bad measures of intelligence. They typically measure your ability to consume, retain, and recall facts. LLMs are very good at that.
Ask an LLM to solve a problem without a known solution and it will fail.
The ability to interact with physical objects is very clearly not a good test for general intelligence and I never claimed otherwise.
I know the second definition was proposed by OpenAI, who obviously has a vested interest in this topic, but that doesn’t mean it can’t be a useful or informative conceptualization of AGI, after all we have to set some threshold for the amount of intelligence AI needs to display and in what areas for it to be considered an AGI. Their proposal of an autonomous system that surpasses humans in economically valuable tasks is fairly reasonable, though it’s still pretty vague and very much debatable, which is why this isn’t the only definition that’s been proposed.
Your definition is definitely more peculiar as I’ve never seen anyone else propose something like it, and it also seems to exclude humans since you’re referring to problems we can’t solve.
The next question then is what problems specifically AI would need to solve to fit your definition, and with what accuracy. Do you mean solve any problem we can throw at it? At that point we’d be going past AGI and now we’re talking about artificial superintelligence…
By your definition AGI doesn’t really seem possible at all. But of course, your definition isn’t how most data scientists or people in general conceptualize AGI, which is the point of my comment. It’s very difficult to put a clear-cut line on what AGI is or isn’t, which is why there are those like you who believe it will never be possible, but there are also those who argue it’s already here.
That’s simply not true. That’s the whole point of the concept of generalization in AI and what the few-shot and zero-shot metrics represent - LLMs solving problems represented in text with few or no prior examples by reasoning beyond what they saw in the training data. You can actually test this yourself by simply signing up to use ChatGPT since it’s free.
So are humans. We’re also deterministic machines that output some action depending on the inputs we get through our senses, much like an LLM outputs some text depending on the inputs it received, plus as I mentioned they can reason beyond what they’ve seen in the training data.
I wasn’t accusing you of anything, I was just pointing out that there are many things we can argue require some degree of intelligence, even physical tasks. The example in the video requires understanding the instructions, the environment, and how to move the robotic arm in order to complete new instructions.
I find LLMs and AGI interesting subjects and was hoping to have a conversation on the nuances of these topics, but it’s pretty clear that you just want to turn this into some sort of debate to “debunk” AGI, so I’ll be taking my leave.
IME when you prompt an LLM to solve a new problem it usually just makes up a bunch of complete bullshit that sounds good but doesn’t mean anything.
I agree, there is no formal definition for AGI so a bit silly to discuss that really. Funnily enough I inadvertantly wrote the nearest neighbour algorithm to model swarming behavour back when I was an undergrad and didn’t even consider it rudimentary AI.
Can I ask what your take on the possibility of neural networks understanding what they are doing is?
Yes refreshing to see someone a little literate here thanks for fighting the misinformation man
Can your calculator only serve problems you already solved? I really don’t buy that take
Llms are in fact not at all good at retaining facts, it’s one of the most worked on problems for them
Llms can solve novel problems. It’s actually much more complex than just a lookup robot, which we already have for such tasks
You just take wild guesstimates on how they work and it just feels wrong to me to not point that out
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It depends a lot on how we perceive “intelligence”. It’s a lot more vague of a term than most, so people have very different views of it. Some people might have the idea of it meaning the response to stimuli & the output (language or art or any other form) being indistinguishable from humans. But many people may also agree that whales/dolphins have the same level of, or superior, “intelligence” to humans. The term is too vague to really prescribe with confidence, and more importantly people often use it to mean many completely different concepts (“intelligence” as a measurable/quantifiable property of either how quickly/efficiently a being can learn or use knowledge or more vaguely its “capacity to reason”, “intelligence” as the idea of “consciousness” in general, “intelligence” to refer to amount of knowledge/experience one currently has or can memorize, etc.)
In computer science “artificial intelligence” has always simply referred to a program making decisions based on input. There was never any bar to reach for how “complex” it had to be to be considered AI. That’s why minecraft zombies or shitty FPS bots are “AI”, or a simple algorithm made to beat table games are “AI”, even though clearly they’re not all that smart and don’t even “learn”.
Even sentience is on a scale. Even cows or dogs or parrots or crows are sentient, but not as much as we are. Computers are not sentient yet, but one day they will be. And then soon after they will be more sentient than us. They’ll be able to see their own brains working, analyze their own thoughts and emotions(?) in real time and be able to achieve a level of self reflection and navel gazing undreamed of by human minds! :D
The damn Viet Cong 😒
Only 2 people on the server left alive, knife fight in the center
OP didn’t say general intelligence. LLMs mimic what actually intelligent beings do, AKA artificial intelligence.
Claiming AGI is the only “real” AI is like claiming Swiss army knives are the only “real” knives. It’s just silly.
But also the people who seem to think we need a magic soul to perform useful work is way way too high.
The main problem is Idiots seem to have watched one too many movies about robots with souls and gotten confused between real life and fantasy - especially shitty journalists way out their depth.
This big gotcha ‘they don’t live upto the hype’ is 100% people who heard ‘ai’ and thought of bad Will Smith movies. LLMs absolutely live upto the actual sensible things people hoped and have exceeded those expectations, they’re also incredibly good at a huge range of very useful tasks which have traditionally been considered as requiring intelligence but they’re not magically able everything, of course they’re not that’s not how anyone actually involved in anything said they would work or expected them to work.
No idea why you’re downvoted. This is correct.
I find this line of thinking tedious.
Even if LLM’s can’t be said to have ‘true understanding’ (however you’re choosing to define it), there is very little to suggest they should be able to
understandpredict the correct response to a particular context, abstract meaning, and intent with what primitive tools they were built with.If there’s some as-yet uncrossed threshold to a bare-minimum ‘understanding’, it’s because we simply don’t have the language to describe what that threshold is or know when it has been crossed. If the assumption is that ‘understanding’ cannot be a quality granted to a transformer-based model -or even a quality granted to computers generally- then we need some other word to describe what LLM’s are doing, because ‘predicting the next-best word’ is an insufficient description for what would otherwise be a slight-of-hand trick.
There’s no doubt that there’s a lot of exaggerated hype around these models and LLM companies, but some of these advancements published in 2022 surprised a lot of people in the field, and their significance shouldn’t be slept on.
Certainly don’t trust the billion-dollar companies hawking their wares, but don’t ignore the technology they’re building, either.
You are best off thinking of LLMs as highly advanced auto correct. They don’t know what words mean. When they output a response to your question the only process that occurred was “which words are most likely to come next”.
And we all know how often auto correct is wrong
Yep. Been having trouble with mine recently, it’s managed to learn my typos and it’s getting quite frustrating
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That’s only true on a very basic level, I understand that Turings maths is complex and unintuitive even more so than calculus but it’s a very established fact that relatively simple mathematical operations can have emergent properties when they interact to have far more complexity than initially expected.
The same way the giraffe gets its spots the same way all the hardware of our brain is built, a strand of code is converted into physical structures that interact and result in more complex behaviours - the actual reality is just math, and that math is almost entirely just probability when you get down to it. We’re all just next word guessing machines.
We don’t guess words like a Markov chain instead use a rather complex token system in our brain which then gets converted to words, LLMs do this too - that’s how they can learn about a subject in one language then explain it in another.
Calling an LLM predictive text is a fundamental misunderstanding of reality, it’s somewhat true on a technical level but only when you understand that predicting the next word can be a hugely complex operation which is the fundamental math behind all human thought also.
Plus they’re not really just predicting one word ahead anymore, they do structured generation much like how image generators do - first they get the higher level principles to a valid state then propagate down into structure and form before making word and grammar choices. You can manually change values in the different layers and see the output change, exploring the latent space like this makes it clear that it’s not simply guessing the next word but guessing the next word which will best fit into a required structure to express a desired point - I don’t know how other people are coming up with sentences but that feels a lot like what I do
LLMs don’t “learn” they literally don’t have the capacity to “learn”. We train them on an insane amount of text and then the LLMs job is to produce output that looks like that text. That’s why when you attempt to correct it nothing happens. It can’t learn, it doesn’t have the capacity to.
Humans aren’t “word guessing machines”. Humans produce language with intent and meaning. This is why you and I can communicate. We use language to represent things. When I say “Tree” you know what that is because it’s the word we use to describe an object we all know about. LLMs don’t know what a tree is. They can use “tree” in a sentence correctly but they don’t know what it means. They can even translate it to another language but they still don’t know what “tree” means. What they know is generating text that looks like what they were trained on.
Here’s a well made video by Kyle Hill that will teach you lot better than I could
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Did you mean “shouldn’t”? Otherwise I’m very confused by your response
No, i mean ‘should’, as in:
It’s like using a description for how covalent bonds are formed as an explanation for how it is you know when you need to take a shit.
Fair enough, that just seemed to be the opposite point that the rest of your post was making so seemed like a typo.
I don’t think so…
Yes. But the more advanced LLMs get, the less it matters in my opinion. I mean of you have two boxes, one of which is actually intelligent and the other is “just” a very advanced parrot - it doesn’t matter, given they produce the same output. I’m sure that already LLMs can surpass some humans, at least at certain disciplines. In a couple years the difference of a parrot-box and something actually intelligent will only merely show at the very fringes of massively complicated tasks. And that is way beyond the capability threshold that allows to do nasty stuff with it, to shed a dystopian light on it.
You’re making a huge assumption; that an advanced parrot produces the same output as something with general intelligence. And I reject that assumption. Something with general intelligence can produce something novel. An advanced parrot can only repeat things it’s already heard.
How do you define novel? Because LLMs absolutely have produced novel data.
LLMs can’t produce anything without being prompted by a human. There’s nothing intelligent about them. Imo it’s an abuse of the word intelligence since they have exactly zero autonomy.
I use LLMs to create things no human has likely ever said and it’s great at it, for example
‘while juggling chainsaws atop a unicycle made of marshmallows, I pondered the existential implications of the colour blue on a pineapples dream of becoming a unicorn’
When I ask it to do the same using neologisms the output is even better, one of the words was exquimodal which I then asked for it to invent an etymology and it came up with one that combined excuistus and modial to define it as something beyond traditional measures which fits perfectly into the sentence it created.
You can’t ask a parrot to invent words with meaning and use them in context, that’s a step beyond repetition - of course it’s not full dynamic self aware reasoning but it’s certainly not being a parrot
Producing word salad really isn’t that impressive. At least the art LLMs are somewhat impressive.
If you ask it to make up nonsense and it does it then you can’t get angry lol. I normally use it to help analyse code or write sections of code, sometimes to teach me how certain functions or principles work - it’s incredibly good at that, I do need to verify it’s doing the right thing but I do that with my code too and I’m not always right either.
As a research tool it’s great at taking a basic dumb description and pointing me to the right things to look for, especially for things with a lot of technical terms and obscure areas.
And yes they can occasionally make mistakes or invent things but if you ask properly and verify what you’re told then it’s pretty reliable, far more so than a lot of humans I know.
The difference is that you can throw enough bad info at it that it will start paroting that instead of factual information because it doesn’t have the ability to criticize the information it receives whereas an human can be told that the sky is purple with orange dots a thousand times a day and it will always point at the sky and tell you “No.”
To make the analogy actually comparable the human in question would need to be learning about it for the first time (which is analogous to the training data) and in that case you absolutely could convince the small child of that. Not only would they believe it if told enough times by an authority figure, you could convince them that the colors we see are different as well, or something along the lines of giving them bad data.
A fully trained AI will tell you that you’re wrong if you told it the sky was orange, it’s not going to just believe you and start claiming it to everyone else it interacts with. It’s been trained to know the sky is blue and won’t deviate from that outside of having its training data modified. Which is like brainwashing an adult human, in which case yeah you absolutely could have them convinced the sky is orange. We’ve got plenty of information on gaslighting, high control group and POW psychology to back that up too.
Feed LLMs all new data that’s false and it will regurgitate it as being true even if it had previously been fed information that contradicts it, it doesn’t make the difference between the two because there’s no actual analysis of what’s presented. Heck, even without intentionally feeding them false info, LLMs keep inventing fake information.
Feed an adult new data that’s false and it’s able to analyse it and make deductions based on what they know already.
We don’t compare it to a child or to someone that was brainwashed because it makes no sense to do so and it’s completely disingenuous. “Compare it to the worst so it has a chance to win!” Hell no, we need to compare it to the people that are references in their field because people will now be using LLMs as a reference!
Ha ha yeah humans sure are great at not being convinced by the opinions of other people, that’s why religion and politics are so simple and society is so sane and reasonable.
Helen Keller would belive you it’s purple.
If humans didn’t have eyes they wouldn’t know the colour of the sky, if you give an ai a colour video feed of outside then it’ll be able to tell you exactly what colour the sky is using a whole range of very accurate metrics.
This is one of the worst rebuttals I’ve seen today because you aren’t addressing the fact that the LLM has zero awareness of anything. It’s not an intelligence and never will be without additional technologies built on top of it.
Why would I rebut that? I’m simply arguing that they don’t need to be ‘intelligent’ to accurately determine the colour of the sky and that if you expect an intelligence to know the colour of the sky without ever seeing it then you’re being absurd.
The way the comment I responded to was written makes no sense to reality and I addressed that.
Again as I said in other comments you’re arguing that an LLM is not will smith in I Robot and or Scarlett Johansson playing the role of a usb stick but that’s not what anyone sane is suggesting.
A fork isn’t great for eating soup, neither is a knife required but that doesn’t mean they’re not incredibly useful eating utensils.
Try thinking of an LLM as a type of NLP or natural language processing tool which allows computers to use normal human text as input to perform a range of tasks. It’s hugely useful and unlocks a vast amount of potential but it’s not going to slap anyone for joking about it’s wife.
How come all LLMs keep inventing facts and telling false information then?
People do that too, actually we do it a lot more than we realise. Studies of memory for example have shown we create details that we expect to be there to fill in blanks and that we convince ourselves we remember them even when presented with evidence that refutes it.
A lot of the newer implementations use more complex methods of fact verification, it’s not easy to explain but essentially it comes down to the weight you give different layers. GPT 5 is already training and likely to be out around October but even before that we’re seeing pipelines using LLM to code task based processes - an LLM is bad at chess but could easily install stockfish in a VM and beat you every time.