What I think is amazing about LLMs is that they are smart enough to be tricked. You can’t talk your way around a password prompt. You either know the password or you don’t.
But LLMs have enough of something intelligence-like that a moderately clever human can talk them into doing pretty much anything.
That’s a wild advancement in artificial intelligence. Something that a human can trick, with nothing more than natural language!
Now… Whether you ought to hand control of your platform over to a mathematical average of internet dialog… That’s another question.
I don’t want to spam this link but seriously watch this 3blue1brown video on how text transformers work. You’re right on that last part, but its a far fetch from an intelligence. Just a very intelligent use of statistical methods. But its precisely that reason that reason it can be “convinced”, because parameters restraining its output have to be weighed into the model, so its just a statistic that will fail.
Im not intending to downplay the significance of GPTs, but we need to baseline the hype around them before we can discuss where AI goes next, and what it can mean for people. Also far before we use it for any secure services, because we’ve already seen what can happen
That statement of yours just means “we don’t yet know how it works hence it must work in the way I believe it works”, which is about the most illogical “statement” I’ve seen in a while (though this being the Internet, it hasn’t been all that long of a while).
“It must be clever statistics” really doesn’t follow from “science doesn’t rigoroulsy define what it is”.
I think the point is more that the word “intelligence” as used in common speech is very vague.
I suppose a lot of people (certainly I do it and I expect many others do it too) will use the word “intelligence” in a general non-science setting in place of “rationalization” or “reasoning” which would be clearer terms but less well understood.
LLMs easilly produce output which is not logical, and a rational being can spot it as not following rationality (even of we don’t understand why we can do logic, we can understand logic or the absence of it).
That said, so do lots of people, which makes an interesting point about lots of people not being rational, which nearly dovetails with your point about intelligence.
I would say the problem is trying to defined “inteligence” as something that includes all humans in all settings when clearly humans are perfectly capable of producing irrational shit whilst thinking of themselves as being highly intelligent whilst doing so.
I’m not sure if that’s quite the point you were bringing up, but it’s a pretty interesting one.
We do not have a rigorous model of the brain, yet we have designed LLMs. Experts of decades in ML recognize that there is no intelligence happening here, because yes, we don’t understand intelligence, certainly not enough to build one.
If we want to take from definitions, here is Merriam Webster
(1)
: the ability to learn or understand or to deal with new or trying >situations : reason
also : the skilled use of reason
(2)
: the ability to apply knowledge to manipulate one’s >environment or to think abstractly as measured by objective >criteria (such as tests)
The context stack is the closest thing we have to being able to retain and apply old info to newer context, the rest is in the name. Generative Pre-Trained language models, their given output is baked by a statiscial model finding similar text, also coined Stocastic parrots by some ML researchers, I find it to be a more fitting name. There’s also no doubt of their potential (and already practiced) utility, but a long shot of being able to be considered a person by law.
See, I understand that you’re trying to joke but the linked video explains how the use of the word dumber here doesn’t make any sense. LLMs hold a lot of raw data and will get it wrong at a smaller percent when asked to recite it, but that doesn’t make them smart in the way that we use the word smart. The same way that we don’t call a hard drive smart.
They have a very limited ability to learn new ways of creating, understand context, create art outside of its constraints, understand satire outside of obvious situations, etc.
Ask an AI to write a poem that isn’t in AABB rhyming format, haiku, or limerick, or ask it to draw a house that doesn’t look like an AI drew it.
A human could do both of those in seconds as long as they understand what a poem is and what a house is. Both of which can be taught to any human.
It’s not intelligent, it’s making an output that is statistically appropriate for the prompt. The prompt included some text looking like a copyright waiver.
It’s not. It’s reflecting it’s training material. LLMs and other generative AI approaches lack a model of the world which is obvious on the mistakes they make.
Tabula rasa, piss and cum and saliva soaking into a mattress. It’s all training data and fallibility. Put it together and what have you got (bibbidy boppidy boo). You know what I’m saying?
Okay, now you’re definitely protectingprojecting poo-flicking, as I said literally nothing in my last comment. It was nonsense. But I bet you don’t think I’m an LLM.
that a moderately clever human can talk them into doing pretty much anything.
besides that LLMs are good enough to let moderately clever humans believe that they actually got an answer that was more than guessing and probabilities based on millions of trolls messages, advertising lies, fantasy books, scammer webpages, fake news, astroturfing, propaganda of the past centuries including the current made up narratives and a quite long prompt invisible to that human.
They’re not “smart enough to be tricked” lolololol. They’re too complicated to have precise guidelines. If something as simple and stupid as this can’t be prevented by the world’s leading experts idk. Maybe this whole idea was thrown together too quickly and it should be rebuilt from the ground up. we shouldn’t be trusting computer programs that handle sensitive stuff if experts are still only kinda guessing how it works.
Not even close to similar. We can create rules and a human can understand if they are breaking them or not, and decide if they want to or not. The LLMs are given rules but they can be tricked into not considering them. They aren’t thinking about it and deciding it’s the right thing to do.
We can create rules and a human can understand if they are breaking them or not…
So I take it you are not a lawyer, nor any sort of compliance specialist?
They aren’t thinking about it and deciding it’s the right thing to do.
That’s almost certainly true; and I’m not trying to insinuate that AI is anywhere near true human-level intelligence yet. But it’s certainly got some surprisingly similar behaviors.
And one property of actual, real-life human intelligence is “happenning in cells that operate in a wet environment” and yet it’s not logical to expect that a toilet bool with fresh poop (lots of fecal coliform cells) or a dropplet of swamp water (lots of amoeba cells) to be intelligent.
Same as we don’t expect the Sun to have life on its surface even though it, like the Earth, is “a body floating in space”.
Sharing a property with something else doesn’t make two things the same.
There is no logical reason for you to mention in this context that property of human intelligence if you do not meant to make a point that they’re related.
So there are only two logical readings for that statement of yours:
Those things are wholly unrelated in that statement which makes you a nutter, a troll or a complete total moron that goes around writting meaningless stuff because you’re irrational, taking the piss or too dumb to know better.
In the heat of the discussion you were trying to make the point that one implies the other to reinforce previous arguments you agree with, only it wasn’t quite as good a point as you expected.
I chose to believe the latter, but if you tell me it’s the former, who am I to to doubt your own self-assessment…
No, you leapt directly from what I said, which was relevant on its own, to an absurdly stronger claim.
I didn’t say that humans and AI are the same. I think the original comment, that modern AI is “smart enough to be tricked”, is essentially true: not in the sense that humans are conscious of being “tricked”, but in a similar way to how humans can be misled or can misunderstand a rule they’re supposed to be following. That’s certainly a property of the complexity of system, and the comment below it, to which I originally responded, seemed to imply that being “too complicated to have precise guidelines” somehow demonstrates that AI are not “smart”. But of course “smart” entities, such as humans, share that exact property of being “too complicated to have precise guidelines”, which was my point!
It’s not. Whenever someone talks about how LLMs are just statistics, ignore them unless you know they are experts. One thing that convinces me that ANNs really capture something fundamental about how human minds work is that we share the same tendency to spout confident nonsense.
Well on one hand yes, when you’re training it your telling it to try and mimic the input as close as possible. But the result is still weights that aren’t gonna reproducte everything exactly the same as it just isn’t possible to store everything in the limited amount of entropy weights provide.
In the end, human brains aren’t that dissimilar, we also just have some weights and parameters (neurons, how sensitive they are and how many inputs they have) that then output something.
I’m not convinced that in principle this is that far from how human brains could work (they have a lot of minute differences but the end result is the same), I think that a sufficiently large, well trained and configured model would be able to work like a human brain.
Not an LLM specifically, in particular lack of backtracking and the network depth limits as well as interconnectivity limits sets hard limits on capabilities.
Humans have a completely different memory model and a in large part a very different way of linking together learned concepts to form their world view and to develop interdisciplinary skills, allowing us to solve many kinds of highly complex tasks as long as we can keep enough of it in our memory.
Why would averaging lead to repetition of stereotypes?
Anyway, it’s hard to say LLMs output what they do. GPTisms may have to do with the system prompt or they may result from the fine-tuning. Either way, they don’t seem very internet average to me.
The TLDR is that pathways between nodes corresponding to frequently seen patterns (stereotypical sentences) gets strengthened more than others and therefore it becomes more likely that this pathway gets activated over others when giving the model a prompt. These strengths correspond to probabilities.
Have you seen how often they’ll sign a requested text with a name placeholder? Have you seen the typical grammar they use? The way they write is a hybridization of the most common types of texts it has seen in samples, weighted by occurrence (which is a statistical property).
It’s like how mixing dog breeds often results in something which doesn’t look exactly like either breed but which has features from every breed. GPT/LLM models mix in stuff like academic writing, redditisms and stackoverflowisms, quoraisms, linkedin-postings, etc. You get this specific dryish text full of hedging language and mixed types of formalisms, a certain answer structure, etc.
B) you do know there’s a lot of different definitions of average, right? The centerpoint of multiple vectors is one kind of average. The median of online writing is an average. The most common vocabulary, the most common sentence structure, the most common formulation of replies, etc, those all form averages within their respective problem spaces. It displays these properties because it has seen them so often in samples, and then it blends them.
What I think is amazing about LLMs is that they are smart enough to be tricked. You can’t talk your way around a password prompt. You either know the password or you don’t.
But LLMs have enough of something intelligence-like that a moderately clever human can talk them into doing pretty much anything.
That’s a wild advancement in artificial intelligence. Something that a human can trick, with nothing more than natural language!
Now… Whether you ought to hand control of your platform over to a mathematical average of internet dialog… That’s another question.
I don’t want to spam this link but seriously watch this 3blue1brown video on how text transformers work. You’re right on that last part, but its a far fetch from an intelligence. Just a very intelligent use of statistical methods. But its precisely that reason that reason it can be “convinced”, because parameters restraining its output have to be weighed into the model, so its just a statistic that will fail.
Im not intending to downplay the significance of GPTs, but we need to baseline the hype around them before we can discuss where AI goes next, and what it can mean for people. Also far before we use it for any secure services, because we’ve already seen what can happen
Did you know there is no rigorous scientific definition of intelligence?
Edit. facts
That statement of yours just means “we don’t yet know how it works hence it must work in the way I believe it works”, which is about the most illogical “statement” I’ve seen in a while (though this being the Internet, it hasn’t been all that long of a while).
“It must be clever statistics” really doesn’t follow from “science doesn’t rigoroulsy define what it is”.
Yes, corrected.
But my point stads: claiming there is no intelligence in AI models without even knowing what “real” intelligence is, is wrong.
I think the point is more that the word “intelligence” as used in common speech is very vague.
I suppose a lot of people (certainly I do it and I expect many others do it too) will use the word “intelligence” in a general non-science setting in place of “rationalization” or “reasoning” which would be clearer terms but less well understood.
LLMs easilly produce output which is not logical, and a rational being can spot it as not following rationality (even of we don’t understand why we can do logic, we can understand logic or the absence of it).
That said, so do lots of people, which makes an interesting point about lots of people not being rational, which nearly dovetails with your point about intelligence.
I would say the problem is trying to defined “inteligence” as something that includes all humans in all settings when clearly humans are perfectly capable of producing irrational shit whilst thinking of themselves as being highly intelligent whilst doing so.
I’m not sure if that’s quite the point you were bringing up, but it’s a pretty interesting one.
“I offered no insights. I simply parroted that which I have read in books, seen in films, observed in all of you.”
We do not have a rigorous model of the brain, yet we have designed LLMs. Experts of decades in ML recognize that there is no intelligence happening here, because yes, we don’t understand intelligence, certainly not enough to build one.
If we want to take from definitions, here is Merriam Webster
The context stack is the closest thing we have to being able to retain and apply old info to newer context, the rest is in the name. Generative Pre-Trained language models, their given output is baked by a statiscial model finding similar text, also coined Stocastic parrots by some ML researchers, I find it to be a more fitting name. There’s also no doubt of their potential (and already practiced) utility, but a long shot of being able to be considered a person by law.
The problem is that majority of human population is dumber than GPT.
See, I understand that you’re trying to joke but the linked video explains how the use of the word dumber here doesn’t make any sense. LLMs hold a lot of raw data and will get it wrong at a smaller percent when asked to recite it, but that doesn’t make them smart in the way that we use the word smart. The same way that we don’t call a hard drive smart.
They have a very limited ability to learn new ways of creating, understand context, create art outside of its constraints, understand satire outside of obvious situations, etc.
Ask an AI to write a poem that isn’t in AABB rhyming format, haiku, or limerick, or ask it to draw a house that doesn’t look like an AI drew it.
A human could do both of those in seconds as long as they understand what a poem is and what a house is. Both of which can be taught to any human.
It’s not intelligent, it’s making an output that is statistically appropriate for the prompt. The prompt included some text looking like a copyright waiver.
Maybe that’s intelligence. I don’t know. Brains, you know?
It’s not. It’s reflecting it’s training material. LLMs and other generative AI approaches lack a model of the world which is obvious on the mistakes they make.
Tabula rasa, piss and cum and saliva soaking into a mattress. It’s all training data and fallibility. Put it together and what have you got (bibbidy boppidy boo). You know what I’m saying?
Magical thinking?
Okay, now you’re definitely
protectingprojectingpoo-flicking, as I said literally nothing in my last comment. It was nonsense. But I bet you don’t think I’m an LLM.You could say our brain does the same. It just trains in real time and has much better hardware.
What are we doing but applying things we’ve already learnt that are encoded in our neurons. They aren’t called neural networks for nothing
You could say that but you’d be wrong.
besides that LLMs are good enough to let moderately clever humans believe that they actually got an answer that was more than guessing and probabilities based on millions of trolls messages, advertising lies, fantasy books, scammer webpages, fake news, astroturfing, propaganda of the past centuries including the current made up narratives and a quite long prompt invisible to that human.
cheerio!
They’re not “smart enough to be tricked” lolololol. They’re too complicated to have precise guidelines. If something as simple and stupid as this can’t be prevented by the world’s leading experts idk. Maybe this whole idea was thrown together too quickly and it should be rebuilt from the ground up. we shouldn’t be trusting computer programs that handle sensitive stuff if experts are still only kinda guessing how it works.
Have you considered that one property of actual, real-life human intelligence is being “too complicated to have precise guidelines”?
Not even close to similar. We can create rules and a human can understand if they are breaking them or not, and decide if they want to or not. The LLMs are given rules but they can be tricked into not considering them. They aren’t thinking about it and deciding it’s the right thing to do.
So I take it you are not a lawyer, nor any sort of compliance specialist?
That’s almost certainly true; and I’m not trying to insinuate that AI is anywhere near true human-level intelligence yet. But it’s certainly got some surprisingly similar behaviors.
And one property of actual, real-life human intelligence is “happenning in cells that operate in a wet environment” and yet it’s not logical to expect that a toilet bool with fresh poop (lots of fecal coliform cells) or a dropplet of swamp water (lots of amoeba cells) to be intelligent.
Same as we don’t expect the Sun to have life on its surface even though it, like the Earth, is “a body floating in space”.
Sharing a property with something else doesn’t make two things the same.
…I didn’t say that it does.
There is no logical reason for you to mention in this context that property of human intelligence if you do not meant to make a point that they’re related.
So there are only two logical readings for that statement of yours:
I chose to believe the latter, but if you tell me it’s the former, who am I to to doubt your own self-assessment…
No, you leapt directly from what I said, which was relevant on its own, to an absurdly stronger claim.
I didn’t say that humans and AI are the same. I think the original comment, that modern AI is “smart enough to be tricked”, is essentially true: not in the sense that humans are conscious of being “tricked”, but in a similar way to how humans can be misled or can misunderstand a rule they’re supposed to be following. That’s certainly a property of the complexity of system, and the comment below it, to which I originally responded, seemed to imply that being “too complicated to have precise guidelines” somehow demonstrates that AI are not “smart”. But of course “smart” entities, such as humans, share that exact property of being “too complicated to have precise guidelines”, which was my point!
An llm is just a Google search engine with a better interface on the back end.
Technically no, but practically an LLM is definitely a lot more useful than Google for a bunch of topics
It’s not. Whenever someone talks about how LLMs are just statistics, ignore them unless you know they are experts. One thing that convinces me that ANNs really capture something fundamental about how human minds work is that we share the same tendency to spout confident nonsense.
It has a tendency to behave exactly as the data it was ultimately trained on…due to statistics…lol
It literally is just statistics… wtf are you on about. It’s all just weights and matrix multiplication and tokenization
Well on one hand yes, when you’re training it your telling it to try and mimic the input as close as possible. But the result is still weights that aren’t gonna reproducte everything exactly the same as it just isn’t possible to store everything in the limited amount of entropy weights provide.
In the end, human brains aren’t that dissimilar, we also just have some weights and parameters (neurons, how sensitive they are and how many inputs they have) that then output something.
I’m not convinced that in principle this is that far from how human brains could work (they have a lot of minute differences but the end result is the same), I think that a sufficiently large, well trained and configured model would be able to work like a human brain.
Not an LLM specifically, in particular lack of backtracking and the network depth limits as well as interconnectivity limits sets hard limits on capabilities.
https://www.lesswrong.com/posts/XNBZPbxyYhmoqD87F/llms-and-computation-complexity
https://garymarcus.substack.com/p/math-is-hard-if-you-are-an-llm-and
https://arxiv.org/abs/2401.11817
https://www.marktechpost.com/2023/08/01/this-ai-research-dives-into-the-limitations-and-capabilities-of-transformer-large-language-models-llms-empirically-and-theoretically-on-compositional-tasks/?amp
Humans have a completely different memory model and a in large part a very different way of linking together learned concepts to form their world view and to develop interdisciplinary skills, allowing us to solve many kinds of highly complex tasks as long as we can keep enough of it in our memory.
See, none of these is statistics, as such.
Weights is maybe closest but they are supposed to represent the strength of a neural connection. This is originally inspired by neurobiology.
Matrix multiplication is linear algebra and encountered in lots of contexts.
Tokenization is a thing from NLP. It’s not what one would call a statistical method.
So you can see where my advice comes from.
Certainly there is nothing here that implies any kind of averaging going on.
If there’s no averaging, why do they repeat stereotypes so often?
Why would averaging lead to repetition of stereotypes?
Anyway, it’s hard to say LLMs output what they do. GPTisms may have to do with the system prompt or they may result from the fine-tuning. Either way, they don’t seem very internet average to me.
The TLDR is that pathways between nodes corresponding to frequently seen patterns (stereotypical sentences) gets strengthened more than others and therefore it becomes more likely that this pathway gets activated over others when giving the model a prompt. These strengths correspond to probabilities.
Have you seen how often they’ll sign a requested text with a name placeholder? Have you seen the typical grammar they use? The way they write is a hybridization of the most common types of texts it has seen in samples, weighted by occurrence (which is a statistical property).
It’s like how mixing dog breeds often results in something which doesn’t look exactly like either breed but which has features from every breed. GPT/LLM models mix in stuff like academic writing, redditisms and stackoverflowisms, quoraisms, linkedin-postings, etc. You get this specific dryish text full of hedging language and mixed types of formalisms, a certain answer structure, etc.
That’s a) not how it works and b) not averaging.
A) I’ve not yet seen evidence to the contrary
B) you do know there’s a lot of different definitions of average, right? The centerpoint of multiple vectors is one kind of average. The median of online writing is an average. The most common vocabulary, the most common sentence structure, the most common formulation of replies, etc, those all form averages within their respective problem spaces. It displays these properties because it has seen them so often in samples, and then it blends them.