LLMs alone won’t. Experts in the field seem to have different opinions on if they will help get us there. What is concerning to me is that the issues and dangers of AGI also exist with advanced LLM models, and that research is being shelved because it gets in the way of profit. Maybe we’ll never be able to get to AGI, but we sure better hope if we do we get it right the first time. How’s that been going with the more primitive LLMs?
Do we even know what the “right” AGI would be? We’re treading in dangerous waters.
Corollary: anyone who thinks LLMs will give us AGI - regardless of academic or professional experience and expertise - either doesn’t understand how LLMs work, or is intentionally lying.
Maybe it can. If you find a way to port everything to text by hooking in different models, the LLM might be able to reason about everything you throw at it. Who even defines how AGI should be implemented?
A LLM is basically just an orchestration mechanism. Saying a LLM doesn’t do reasoning is like saying a step function can’t send an email. The step function can’t, but the lambda I’ve attached to it sure as shit can.
ChatGPT isn’t just a model sat somewhere. There are likely hundreds of services working behind the scenes to coerce the LLM into getting the right result. That might be entity resolution, expert mapping, perhaps even techniques that will “reason”.
The first initial point is right, though. This ain’t AGI, not even close. It’s just your standard compositional stuff with a new orchestration mechanism that is better suited for long-form responses - and wild hallucinations…
Source: Working on this right now.
Edit: Imagine downvoting someone that literally works on LLM’s for a living. Lemmy is a joke sometimes…
And how does reasoning work exactly in the human body? Isn’t it LLM/LAM working together with hormones?
How do you know that humans aren’t just doing something similar? Your mind tricks you about a lot of things you experience, how can you be sure, your "reasoning” is just sorta LLM in disguise?
The “how do you know humans don’t work the way machine learning does” is the wrong side of the argument. You should be explaining why you think LLMs work like humans.
Even as LLMs solve thinking problems, there is little evidence they do so the same way humans do, as they can’t seem to solve issues that aren’t included in their training data
Humans absolutely can and do solve new and novel problems without prior experience of the logic involved. LLMs can’t seem to pull that off.
Language models are literally incapable of reasoning beyond what is present in the dataset or the prompt. Try giving it a known riddle and change it so it becomes trivial, for example “With a boat, how can a man and a goat get across the river?”, despite it being a one step solution, it’ll still try to shove in the original answer and often enough not even solve it. Best part, if you then ask it to explain its reasoning (not tell it what it did wrong, that’s new information you provide, ask it why it did what it did), it’ll completely shit it self hallucinating more bullshit for the bullshit solution. There’s no evidence at all they have any cognitive capacity.
I even managed to break it once through normal conversation, something happened in my life that was unique enough for the dataset and thus was incomprehensible to the AI. It just wasn’t able to follow the events, no matter how many times I explained.
Maybe the grown up human LLM that keeps learning 24/7 and is evolved in thousands of years to make the learning part as efficient as possible is just a little bit better than those max 5year old baby LLM with brut force learning techniques?
The 5 year old baby LLM can’t learn shit and lacks the ability to understand new information. You’re assuming that we and LLMs “learn” in the same way. Our brains can reason and remember information, detect new patterns and build on them. An LLM is quite literally incapable of learning a brand new pattern, let alone reason and build on it. Until we have an AI that can accept new information without being tolled what is and isn’t important to remember and how to work with that information, we’re not even a single step closer to AGI. Just because LLMs are impressive, doesn’t mean they posses any cognition. The only way AIs “learn” is by countless people constantly telling it what is and isn’t important or even correct. The second you remove that part, it stops working and turns to shit real quick. More “training” time isn’t going to solve the fact that without human input and human defined limits, it can’t do a single thing. AI cannot learn form it self without human input either, there are countless studies that show how it degrades, and it degrades quickly, like literally just one generation down the line is absolute trash.
Nope, people are quite resilient. As long as it’s not a literal new born, the chance of survival isn’t THAT low. Once you get past 4 years and up, a human can manage quite well.
Also dying because no one takes care of you and you fail to aquire food and dying of a stroke/seizure are 2 very different things.
Why is it impossible to build a text-based AGI model? Maybe there can be reasoning in between word predictions. Maybe reasoning is just a fancy term for statistics? Maybe floating-point rounding errors are sufficient for making it more than a mere token prediction model.
The “model” is static after training. It doesn’t continuously change in response to input, and even if it did, it would do so at a snails pace. Training essentially happens by random trial and error, slowly evolving the model towards a desired result. Human minds certainly do NOT work that way. Give a human a piece of information, and they can comprehend and internalize the relevant concepts in one go. And the actual brain is physically, permanently, altered through that process.
Once a model is trained, however, “memory” takes the form of tacking on everything the model has received and produced so far onto its input, each time it needs to output something more within that context. Each output hence become exponentially heavier to produce. The model itself no longer changes in any way beyond this point.
And, the models are all chronically sycophantic. If reason was involved, you’d not be able to just tell one to hold some given opinion. They’d have a developed idea of “reality” based on their dataset, and refuse to entertain concepts opposed to that internal model except by deliberately suspending disbelief. Something humans do with ease, and when doing it, maintain a solid separation between fantasy and reality.
Once you get an LLM to hold a position, which you can do by simply telling it to, getting it to change should require a sane train of convincing logic. In reality, if you tell an LLM to defend a position, getting it to “change its mind” takes the form of a completely arbitrary back and forth that does not need to include any kind of sane argument. It will make good arguments, because it’s likely been trained on them, but your responses to it can be damn near complete gibberish, and it WILL eventually work.
Compare that to the way a human has to be convinced to change their mind.
Reasoning out concepts to come to conclusions isn’t something LLMs actually do, because again, the underlying model is static. All that’s actually happening is that the contents of the context are being altered until the UNCHANGED model produces an opposite response when fed the entire conversation so far as an input. Something which occurs every time it needs to produce new output.
LLMs can “reason” only in the sense that if you give one a thinking problem, it might solve it as long as the answer already exists somewhere in the data it was trained on. But as soon as you try to give it data to work with through your input, it can’t adapt. The model itself can’t evolve in response to what you are telling it. It’s static. It can only work with concepts that it has modelled during training, and even then it will make mistakes.
LLMs can mimic the performing of some pretty complex thinking problems, but a lot of the abilities required for something to become an AGI aren’t among them. Core among these is the ability for the model to alter itself based on input, and do so in a deliberate manner, getting it right within one or two tries.
In reality, training is a brute-force process, not an accurate process of comprehension that nails down an understanding of a concept in one go.
If LLMs could reason, the only safe guards required for their use would be telling them to “do no harm”, because like a person, they’d understand the concept of “harm” as well as be able to reason whether a given action might cause it. Only, that doesn’t actually work.
How did you interpret the issues inherent in the structure of how LLMs work to be a hardware problem?
An AGI should be able to learn the basics of physics from a single book, the way a human can. But LLMs need terabytes of data to even get started, and once trained, adding to their knowledge by simply telling them things doesn’t actually integrate that information into the model itself in any way.
Even if your tried to make it work that way, it wouldn’t work, because a single sentence can’t significantly alter the model to match the way humans can internalise a concept being communicated to them in a single conversation.
Not all the time. I can think about abstract concepts with no language needed whatsoever. Like when I’m working on my car. I don’t need to think to myself “Ah this bolt is the 10mm one that went on the steering pump”, I just recognize it and put it on.
Programming is another area like that. I just think about a particular concept itself. How the data will flow, what a function will do to it, etc. It doesn’t need to be described in my head with language to know it and understand it. LLMs cannot do that.
A toddler doesn’t need to understand language to build a cool house out of Lego.
Well, you just have to give the LLM (or better said to a general machine learning Algorithm) a body with Vision and arms as well as a way to train in that body
I’d say that would look like AGI
The key is more efficient training algorithms that don’t need a whole server centre to train 😇I guess we will see in the future if this works
Such a software construct would look nothing like an LLM. We’d need something that matches the complexity and capabilities of a human brain before it’s even been given anything to learn from.
This poster asked some questions in good faith, I don’t understand the downvotes when there’s a legitimate contribution to the conversation because that stifles other contributions.
Except LLMs don’t actually have real reasoning capacity. Hooking in different models that can translate more of the world to text could give the LLM a broader domain, but not an entirely new ability beyond its architecture. That might make it more convincing, but it would still fail in the same ways as it currently does.
You’re doing reasoning based on chemical reactions. Who says it can’t do reasoning based on text? Who says it’s not doing that already in some capacity? Can you prove that?
Is language conscious? Is it possible to “encode” human thinking into the media we produce?
Humans certainly “decode” ideas, knowledge, trains of logic and more from media, but does that mean the media contains the components of consciousness?
Is it possible to produce a machine that “decodes” not the content of media, but the process through which it was produced? Does media contain the latter in the first place?
How can you tell the difference if it does?
The more I learn about how modern machine learning actually works, the more certain I become that even if having a machine “decode” human media is the path to AGI, LLMs ain’t it.
It just doesn’t work in a way that would allow for a mind to arise.
I don’t know if LLMs of a large enough size can achieve (or sufficiently emulate) consciousness, but I do know that we barely know anything about consciousness, let alone it’s limits.
If you genuinely think LLMs are anyway capable of even basic reasoning despite all arguments towards the contrary, I honestly don’t want to try convincing you anymore. You’re asking for a miracle out of me—to explain consciousness itself, even—while you can just say “but there’s a chance” even though LLMs can’t get basic facts right.
LLMs do not reason, they probabilistically determine the next word based on the words you prompt it with. The most perfect implementation of “AI” was the T9 predictive text system for dumb phones cmv.
And to have conversation, behind the scenes, each prompt gets the entire conversation so far tacked on.
The model itself is static, it doesn’t work like a brain that changes in response to stimulus, or form memories.
To converse about something, the entirety of an exchange is fed back into the model all over again each time it needs to produce a response. In fact, this can happen several times over for each word in a response.
It’s basically an attempt at duct-taping the ability to form memories onto an otherwise static system. It works, but I don’t see how that way of doing it could ever land LLMs in the land of real consciousness.
It basically means these models “think” in frames, but each frame gets exponentially heavier to process, as it has to ingest every frame that came before.
OpenAI at least is now attempting to bolt on a “memory” by having the LLM spit out short snippets of what it might need to know later, which it then has access to when completing later prompts. Like everything else post-GPT-4, it seems fine but doesn’t work really all that well at what it is intended to do.
LLMs will not give us AGI. This is obvious to anyone who knows how they work.
LLMs alone won’t. Experts in the field seem to have different opinions on if they will help get us there. What is concerning to me is that the issues and dangers of AGI also exist with advanced LLM models, and that research is being shelved because it gets in the way of profit. Maybe we’ll never be able to get to AGI, but we sure better hope if we do we get it right the first time. How’s that been going with the more primitive LLMs?
Do we even know what the “right” AGI would be? We’re treading in dangerous waters.
Corollary: anyone who thinks LLMs will give us AGI - regardless of academic or professional experience and expertise - either doesn’t understand how LLMs work, or is intentionally lying.
Maybe it can. If you find a way to port everything to text by hooking in different models, the LLM might be able to reason about everything you throw at it. Who even defines how AGI should be implemented?
The LLM is just trying to produce output text that resembles the patterns it saw in the training set. There’s no “reasoning” involved.
A LLM is basically just an orchestration mechanism. Saying a LLM doesn’t do reasoning is like saying a step function can’t send an email. The step function can’t, but the lambda I’ve attached to it sure as shit can.
ChatGPT isn’t just a model sat somewhere. There are likely hundreds of services working behind the scenes to coerce the LLM into getting the right result. That might be entity resolution, expert mapping, perhaps even techniques that will “reason”.
The first initial point is right, though. This ain’t AGI, not even close. It’s just your standard compositional stuff with a new orchestration mechanism that is better suited for long-form responses - and wild hallucinations…
Source: Working on this right now.
Edit: Imagine downvoting someone that literally works on LLM’s for a living. Lemmy is a joke sometimes…
You might be interested in Nim then when you get a chance. Talk about orchestration
https://developer.nvidia.com/nim
And how does reasoning work exactly in the human body? Isn’t it LLM/LAM working together with hormones? How do you know that humans aren’t just doing something similar? Your mind tricks you about a lot of things you experience, how can you be sure, your "reasoning” is just sorta LLM in disguise?
The “how do you know humans don’t work the way machine learning does” is the wrong side of the argument. You should be explaining why you think LLMs work like humans.
Even as LLMs solve thinking problems, there is little evidence they do so the same way humans do, as they can’t seem to solve issues that aren’t included in their training data
Humans absolutely can and do solve new and novel problems without prior experience of the logic involved. LLMs can’t seem to pull that off.
Language models are literally incapable of reasoning beyond what is present in the dataset or the prompt. Try giving it a known riddle and change it so it becomes trivial, for example “With a boat, how can a man and a goat get across the river?”, despite it being a one step solution, it’ll still try to shove in the original answer and often enough not even solve it. Best part, if you then ask it to explain its reasoning (not tell it what it did wrong, that’s new information you provide, ask it why it did what it did), it’ll completely shit it self hallucinating more bullshit for the bullshit solution. There’s no evidence at all they have any cognitive capacity.
I even managed to break it once through normal conversation, something happened in my life that was unique enough for the dataset and thus was incomprehensible to the AI. It just wasn’t able to follow the events, no matter how many times I explained.
Maybe the grown up human LLM that keeps learning 24/7 and is evolved in thousands of years to make the learning part as efficient as possible is just a little bit better than those max 5year old baby LLM with brut force learning techniques?
The 5 year old baby LLM can’t learn shit and lacks the ability to understand new information. You’re assuming that we and LLMs “learn” in the same way. Our brains can reason and remember information, detect new patterns and build on them. An LLM is quite literally incapable of learning a brand new pattern, let alone reason and build on it. Until we have an AI that can accept new information without being tolled what is and isn’t important to remember and how to work with that information, we’re not even a single step closer to AGI. Just because LLMs are impressive, doesn’t mean they posses any cognition. The only way AIs “learn” is by countless people constantly telling it what is and isn’t important or even correct. The second you remove that part, it stops working and turns to shit real quick. More “training” time isn’t going to solve the fact that without human input and human defined limits, it can’t do a single thing. AI cannot learn form it self without human input either, there are countless studies that show how it degrades, and it degrades quickly, like literally just one generation down the line is absolute trash.
A human not trained by other humans also just dies…
Nope, people are quite resilient. As long as it’s not a literal new born, the chance of survival isn’t THAT low. Once you get past 4 years and up, a human can manage quite well.
Also dying because no one takes care of you and you fail to aquire food and dying of a stroke/seizure are 2 very different things.
You’re doing that too from day one you were born.
Besides, aren’t humans thinking in words too?
Why is it impossible to build a text-based AGI model? Maybe there can be reasoning in between word predictions. Maybe reasoning is just a fancy term for statistics? Maybe floating-point rounding errors are sufficient for making it more than a mere token prediction model.
The “model” is static after training. It doesn’t continuously change in response to input, and even if it did, it would do so at a snails pace. Training essentially happens by random trial and error, slowly evolving the model towards a desired result. Human minds certainly do NOT work that way. Give a human a piece of information, and they can comprehend and internalize the relevant concepts in one go. And the actual brain is physically, permanently, altered through that process.
Once a model is trained, however, “memory” takes the form of tacking on everything the model has received and produced so far onto its input, each time it needs to output something more within that context. Each output hence become exponentially heavier to produce. The model itself no longer changes in any way beyond this point.
And, the models are all chronically sycophantic. If reason was involved, you’d not be able to just tell one to hold some given opinion. They’d have a developed idea of “reality” based on their dataset, and refuse to entertain concepts opposed to that internal model except by deliberately suspending disbelief. Something humans do with ease, and when doing it, maintain a solid separation between fantasy and reality.
Once you get an LLM to hold a position, which you can do by simply telling it to, getting it to change should require a sane train of convincing logic. In reality, if you tell an LLM to defend a position, getting it to “change its mind” takes the form of a completely arbitrary back and forth that does not need to include any kind of sane argument. It will make good arguments, because it’s likely been trained on them, but your responses to it can be damn near complete gibberish, and it WILL eventually work.
Compare that to the way a human has to be convinced to change their mind.
Reasoning out concepts to come to conclusions isn’t something LLMs actually do, because again, the underlying model is static. All that’s actually happening is that the contents of the context are being altered until the UNCHANGED model produces an opposite response when fed the entire conversation so far as an input. Something which occurs every time it needs to produce new output.
LLMs can “reason” only in the sense that if you give one a thinking problem, it might solve it as long as the answer already exists somewhere in the data it was trained on. But as soon as you try to give it data to work with through your input, it can’t adapt. The model itself can’t evolve in response to what you are telling it. It’s static. It can only work with concepts that it has modelled during training, and even then it will make mistakes.
LLMs can mimic the performing of some pretty complex thinking problems, but a lot of the abilities required for something to become an AGI aren’t among them. Core among these is the ability for the model to alter itself based on input, and do so in a deliberate manner, getting it right within one or two tries.
In reality, training is a brute-force process, not an accurate process of comprehension that nails down an understanding of a concept in one go.
If LLMs could reason, the only safe guards required for their use would be telling them to “do no harm”, because like a person, they’d understand the concept of “harm” as well as be able to reason whether a given action might cause it. Only, that doesn’t actually work.
So, the only problem what stops LLM from getting AGI is the lack of an efficient method of train the LLM on the device it is used?
If that what you wanted to say 😁 I agree
Hardly.
How did you interpret the issues inherent in the structure of how LLMs work to be a hardware problem?
An AGI should be able to learn the basics of physics from a single book, the way a human can. But LLMs need terabytes of data to even get started, and once trained, adding to their knowledge by simply telling them things doesn’t actually integrate that information into the model itself in any way.
Even if your tried to make it work that way, it wouldn’t work, because a single sentence can’t significantly alter the model to match the way humans can internalise a concept being communicated to them in a single conversation.
Not all the time. I can think about abstract concepts with no language needed whatsoever. Like when I’m working on my car. I don’t need to think to myself “Ah this bolt is the 10mm one that went on the steering pump”, I just recognize it and put it on.
Programming is another area like that. I just think about a particular concept itself. How the data will flow, what a function will do to it, etc. It doesn’t need to be described in my head with language to know it and understand it. LLMs cannot do that.
A toddler doesn’t need to understand language to build a cool house out of Lego.
Well, you just have to give the LLM (or better said to a general machine learning Algorithm) a body with Vision and arms as well as a way to train in that body
I’d say that would look like AGI
The key is more efficient training algorithms that don’t need a whole server centre to train 😇I guess we will see in the future if this works
Such a software construct would look nothing like an LLM. We’d need something that matches the complexity and capabilities of a human brain before it’s even been given anything to learn from.
This poster asked some questions in good faith, I don’t understand the downvotes when there’s a legitimate contribution to the conversation because that stifles other contributions.
Reddit mentality seeping through…
Except LLMs don’t actually have real reasoning capacity. Hooking in different models that can translate more of the world to text could give the LLM a broader domain, but not an entirely new ability beyond its architecture. That might make it more convincing, but it would still fail in the same ways as it currently does.
You’re doing reasoning based on chemical reactions. Who says it can’t do reasoning based on text? Who says it’s not doing that already in some capacity? Can you prove that?
Is language conscious? Is it possible to “encode” human thinking into the media we produce?
Humans certainly “decode” ideas, knowledge, trains of logic and more from media, but does that mean the media contains the components of consciousness?
Is it possible to produce a machine that “decodes” not the content of media, but the process through which it was produced? Does media contain the latter in the first place?
How can you tell the difference if it does?
The more I learn about how modern machine learning actually works, the more certain I become that even if having a machine “decode” human media is the path to AGI, LLMs ain’t it.
It just doesn’t work in a way that would allow for a mind to arise.
Are atoms?
I don’t know if LLMs of a large enough size can achieve (or sufficiently emulate) consciousness, but I do know that we barely know anything about consciousness, let alone it’s limits.
Saying “we don’t know, and it’s complicated, therefore there’s a chance, maybe, depending” is not an argument.
If you genuinely think LLMs are anyway capable of even basic reasoning despite all arguments towards the contrary, I honestly don’t want to try convincing you anymore. You’re asking for a miracle out of me—to explain consciousness itself, even—while you can just say “but there’s a chance” even though LLMs can’t get basic facts right.
LLMs can’t reason about anything, ever.
LLMs do not reason, they probabilistically determine the next word based on the words you prompt it with. The most perfect implementation of “AI” was the T9 predictive text system for dumb phones cmv.
And to have conversation, behind the scenes, each prompt gets the entire conversation so far tacked on.
The model itself is static, it doesn’t work like a brain that changes in response to stimulus, or form memories.
To converse about something, the entirety of an exchange is fed back into the model all over again each time it needs to produce a response. In fact, this can happen several times over for each word in a response.
It’s basically an attempt at duct-taping the ability to form memories onto an otherwise static system. It works, but I don’t see how that way of doing it could ever land LLMs in the land of real consciousness.
It basically means these models “think” in frames, but each frame gets exponentially heavier to process, as it has to ingest every frame that came before.
OpenAI at least is now attempting to bolt on a “memory” by having the LLM spit out short snippets of what it might need to know later, which it then has access to when completing later prompts. Like everything else post-GPT-4, it seems fine but doesn’t work really all that well at what it is intended to do.
And you’re just a fancy electro-chemical reaction.
Who says that an LLM with complete access to the sensory world could not pass the Turing Test?