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.
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
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.
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.
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
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.
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”.
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.
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 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
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.
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
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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getting there
Piped is a privacy-respecting open-source alternative frontend to YouTube.
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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.
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 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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deleted by creator
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…