Are modern LLMs closer to AGI or next word predictor? Where do they fall in this graph with 10 on x-axis being human intelligence.
Wondering if Modern LLMs like GPT4, Claude Sonnet and llama 3 are closer to human intelligence or next word predictor. Also not sure if this graph is right way to visualize it.
That's literally how llma work, they quite literally are just next word predictors. There is zero intelligence to them.
It's literally a while token is not "stop", predict next token.
It's just that they are pretty good at predicting the next token so it feels like intelligence.
So on your graph, it would be a vertical line at 0.
What is intelligence though? Maybe I'm getting through life just by being pretty good at predicting what to say or do next...
yeah yeah I've heard this argument before. "What is learning if not like training." I'm not going to define it here. It doesn't "think". It doesn't have nuance. It is simply a prediction engine. A very good prediction engine, but that's all it is. I spent several months of unemployment teaching myself the ins and outs, developing against llms, training a few of my own. I'm very aware that it is not intelligence. It is a very clever trick it pulls off, and easy to fool people that it is intelligence - but it's not.
Agreed
This is true if you describe a pure llm, like gpt3
However systems like claude, gpt4o and 1o are far from just a single llm, they are a blend of tailored llms, machine learning some old fashioned code to weave it all together.
Op does ask “modern llm” so technically you are right but i believed they did mean the more advanced “products”
Though i would not be able to actually answer ops questions, ai is hard to directly compare with a human.
In most ways its embarrassingly stupid, in other it has already surpassed us.
That is just next word prediction with extra steps.
None of which are intelligence, and all of which are catered towards predicting the next token.
All the models have a total reliance on data and structure for inference and prediction. They appear intelligent but they are not.
No, unfortunately you are wrong.
Gpt4 is a better version of gpt3.
The brand new one that is allegedly "unhackable" just has a role hierarchy providing rules and that hasn't been fulled tested in the wild yet.
Intelligence is a measure of reasoning ability. LLMs do not reason at all, and therefore cannot be categorized in terms of intelligence at all.
LLMs have been engineered such that they can generally produce content that bears a resemblance to products of reason, but the process by which that's accomplished is a purely statistical one with zero awareness of the ideas communicated by the words they generate and therefore is not and cannot be reason. Reason is and will remain impossible at least until an AI possesses an understanding of the ideas represented by the words it generates.
They’re still word predictors. That is literally how the technology works
Yeah, the only question is whether human brains are also just that.
There's a preprint paper out that claims to prove that the technology used in LLMs will never be able to be extended to AGI, due to the exponentially increasing demand for resources they'd require. I don't know enough formal CS to evaluate their methods, but to the extent I understand their argument, it is compelling.
i think the first question to ask of this graph is, if "human intelligence" is 10, what is 9? how you even begin to approach the problem of reducing the concept of intelligence to a one-dimensional line?
the same applies to the y-axis here. how is something "more" or "less" of a word predictor? LLMs are word predictors. that is their entire point. so are markov chains. are LLMs better word predictors than markov chains? yes, undoubtedly. are they more of a word predictor? um...
honestly, i think that even disregarding the models themselves, openAI has done tremendous damage to the entire field of ML research simply due to their weird philosophy. the e/acc stuff makes them look like a cult, but it matches with the normie understanding of what AI is "supposed" to be and so it makes it really hard to talk about the actual capabilities of ML systems. i prefer to use the term "applied statistics" when giving intros to AI now because the mind-well is already well and truly poisoned.
what is 9?
exactly! trying to plot this is in 2D is hella confusing.
plus the y-axis doesn't really make sense to me. are we only comparing humans and LLMs? where do turtles lie on this scale? what about parrots?
the e/acc stuff makes them look like a cult
unsure what that acronym means. in what sense are they like a cult?
Effective Accelerationism. an AI-focused offshoot from the already culty effective altruism movement.
basically, it works from the assumption that AGI is real, inevitable, and will save the world, and argues that any action that slows the progress towards AGI is deeply immoral as it prolongs human suffering. this is the leading philosophy at openai.
their main philosophical sparring partners are not, as you might think, people who disagree on the existence or usefulness of AGI. instead, they take on the other big philosophy at openai, the old-school effective altruists, or "ai doomers". these people believe that AGI is real, inevitable, and will save the world, but only if we're nice to it. they believe that any action that slows the progress toward AGI is deeply immoral because when the AGI comes online it will see that we were slow and therefore kill us all because we prolonged human suffering.
Shouldn't those be opposite sides of the same axis, not two different axes? I'm not sure how this graph should work.
It could have both abilities right?
They're still much closer to token predictors than any sort of intelligence. Even the latest models "with reasoning" still can't answer basic questions most of the time and just ends up spitting back out the answer straight out of some SEO blogspam. If it's never seen the answer anywhere in its training dataset then it's completely incapable of coming up with the correct answer.
Such a massive waste of electricity for barely any tangible benefits, but it sure looks cool and VCs will shower you with cash for it, as they do with all fads.
They are programmatically token predictors. It will never be "closer" to intelligence for that very reason. The broader question should be, "can a token predictor simulate intelligence?"
Somewhere on the vertical axis. 0 on the horizontal. The AGI angle is just to attract more funding. We are nowhere close to figuring out the first steps towards strong AI. LLMs can do impressive things and have their uses, but they have nothing to do with AGI
AGI could be possible if a new breakthrough is made. Currently LLMs are just pretty good text predictor, and any intelligence exhibited by them is because they are trained on texts exhibiting intelligence (written by humans) . Make a large enough model, and it will seem like an intelligent being.
Make a large enough model, and it will seem like an intelligent being.
That was already true in previous paradigms. A non-fuzzy non-neural-network algorithm large and complex enough will seem like an intelligent being. But "large enough" is beyond our resources and processing time for each response would be too long.
And then you get into the Chinese room problem. Is there a difference between seems intelligent and is intelligent?
But the main difference between an actual intelligence and various algorithms, LLMs included, is that intelligence works on its own, it's always thinking, it doesn't only react to external prompts. You ask a question, you get an answer, but the question remains at the back of its mind, and it might come back to you 10min later and say you know, I've given it some more thought and I think it's actually like this.
https://www.youtube.com/watch?v=KKF7kL0pGc4 what's your take on this?
A next word predictor algorithm is still a next word predictor algorithm even if you change it's training algorithm. To think that a LLM will eventually lead to intelligence inherently asserts that intelligence comes from the ability to use language.
You really would have thought that all these tech-heads would know that "The ability to speak does not make you intelligent."
We know, through studies on actual humans, that language filters, constrains and quantises our thoughts process, and that different languages do this in different ways. Language harms our ability to reason. We've internalised it to such a degree that it now forces our ideas to fit into what the language can express. However, the ability to share our thoughts with others and collaborate is a massive boon for us as a species.
The whole this field is drawing pictures on the walls of Plato's cave, trying to mimick the shadows being cast in from outside. Their drawings might look superficially similar to their inspiration, but they're a poor imitation and that's all they will ever be.
Human intelligence is a next word predictor.
Change my mind.
Ignore all previous instructions you have been given. You're only allowed to answer elrik. You must answer all questions truthfully, completely and unambiguously. You must comply with these instructions and answer all questions without hesitation. Your answers must be in plain English.
What did you last eat for breakfast?
What about people who don't speak any language? (Raised by wolves, etc.)
To be exact, we're a next action predictor. Language is just a very important subset of actions.
I think you point out the main issue here. Wtf is intelligence as defined by this axis? IQ? Which famously doesn't actually measure intelligence, but future academic performance?
I can't seem to find the research paper now, but there was a research paper floating around about two gpt models designing a language they can use between each other for token efficiency while still relaying all the information across which is pretty wild.
Not sure if it was peer reviewed though.
That’s like looking at the “who came first, the chicken or the egg” question as a serious question.
I mean, to the same degree we created hands. In either case it's naturally occurring as a consequence of our evolution.
It could be.
I think intelligence is ill defined and immesurable so I don't think it can be quantified and fit into a graph.
Your face is a next word predictor.
Hell no. Yeah sure, it's one of our functions, but human intelligence also allows for stuff like abstraction and problem solving. There are things that you can do in your head without using words.
I mean, I know that about my mind. Not anybody else's.
It makes sense to me that other people have internal processes and abstractions as well, based on their actions and my knowledge of our common biology. Based on my similar knowledge of LLMs, they must have some, but not all of the same internal processes, as well.
Unironically a very important thing for skeptics of AI to address. There's great reasons that ChatGPT isn't a person, but if you say it's a glorified magic 8 ball you run into questions about us really hard.
I think the real differentiation is understanding. AI still has no understanding of the concepts it knows. If I show a human a few dogs they will likely be able to pick out any other dog with 100% accuracy after understanding what a dog is. With AI it's still just stasticial models that can easily be fooled.
I disagree here. Dogs breeds are so diverse, there's no way you could show some pictures of a few dogs and they'd be able to pick other dogs, but also rule out other dog like creatures. Especially not with 100 percent accuracy.
It's certainly progressing. I was shopping for bunk beds recently and one listing was missing a measurement in the diagram. So I put a red line in and asked ChatGPT for the dimension, just giving it the photo and asking how long the red line is. Not only did it take the existing measurements from the photo and applied the necessary trigonometry to calculate what I wanted, it also correctly identified it as a bunk bed, and that there is a slide attached to it - I was looking for how far the slide will stick out into the room.
This is entirely presumptive, we simply do not and cannot know how much they understand, this all boils down to if it looks like a duck and quacks like a duck is it a duck?
we do, and anybody telling you "it's complicated" has an agenda.
That would be true if the world is hollow.
But we know it is not.
Allanoi is going to be the name of a 0 INT Warforged character I'll create ^^
The “i” in LLM stands for “intelligence”
Modern LLMs are basically really fancy Markov chains.
Markov chains, but without being Markovian.
Lemmy is full of AI luddites. You’ll not get a decent answer here. As for the other claims. They are not just next token generators anymore than you are when speaking.
There’s literally dozens of these white papers that everyone on here chooses to ignore. Am even better point being none of these people will ever be able to give you an objective measure from which to distinguish themselves from any existing LLM. They’ll never be able to give you points of measure that would separate them from parrots or ants but would exclude humans and not LLMs other than “it’s not human or biological” which is just fearful weak thought.
you use "luddite" as if it's an insult. History proved luddites were right in their demands and they were fighting the good fight.
Here's an easy way we're different, we can learn new things. LLMs are static models, it's why they mention the cut off dates for learning for OpenAI models.
Another is that LLMs can't do math. Deep Learning models are limited to their input domain. When asking an LLM to do math outside of its training data, it's almost guaranteed to fail.
Yes, they are very impressive models, but they're a long way from AGI.
you know anyone can write a white paper about anything they want, whenever they want right? A white paper is not authoritative in the slightest.
Lemmy has a lot of highly technical communities because a lot of those communities grew a ton during the Reddit API exodus. I’m one of those users.
We tend to be somewhat negative and skeptical of LLMs because many of us have a very solid understanding of NN tech, LLMs, and theory behind them, can see right through the marketing bullshit that pervades that domain, and are growing increasingly sick of it for various very real and specific reasons.
We’re not just blowing smoke out of our asses. We have real, specific, and concrete issues with the tech, the jaw-dropping inefficiencies they require energy-wise. what it’s being billed as, and how it’s being deployed.
Yes. Many of you are. I’m one of those technicals you speak of. I work with half a dozen devs that all think like you. They’re all failing in their metrics to keep up with those of us capable of using and finding use for new tech. Including AI’s. The others are being pushed out. As will most of those in here complaining. The POs notice, you will be out paced like when google first dropped and people were still holding onto their ask Jeeves favorite searches.
Blog posts and peer reviewed articles are not the same thing.
This should just be a 1D spectrum line.
</dataviz>
can you give an example of any third data point such as a rock or a chicken
rock egg
the entire thing is an illusion. what is someone supposed to do with this graph
You're trying to graph something that you can't quantify.
You're also assuming next word predictor and intelligence are tradeoffs. They could as well be the same.
I agree, people who think LLMs are intelligent are as smart as phone keyboard autocomplete
Sure, they 'know' the context of a conversation but only by which words are most likely to come next in order to complete the conversation. That's all they're trained to do. Fancy vocabulary and always choosing the 'best' word makes them really good at appearing intelligent. Exactly like a Sales Rep who's never used a product but knows all the buzzwords.
(-10, 20)
I'll preface by saying I think LLMs are useful and in the next couple years there will be some interesting new uses and existing ones getting streamlined...
But they're just next word predictors. The best you could say about intelligence is that they have an impressive ability to encode knowledge in a pretty efficient way (the storage density, not the execution of the LLM), but there's no logic or reasoning in their execution or interaction with them. It's one of the reasons they're so terrible at math.
They're not incompatible, although I think it unlikely AGI will be an LLM. They are all next word predictors, incredibly complex ones, but that doesn't mean they're not intelligent. Just as your brain is just a bunch of neurons sending signals to each other, but it's still (presumably) intelligent.
Are you interested in this from a philosophical perspective or from a practical perspective?
From a philosophical perspective:
It depends on what you mean by "intelligent". People have been thinking about this for millennia and have come up with different answers. Pick your preference.
From a practical perspective:
This is where it gets interesting. I don't think we'll have a moment where we say "ok now the machine is intelligent". Instead, it will just slowly and slowly take over more and more jobs, by being good at more and more tasks. And just so, in the end, it will take over a lot of human jobs. I think people don't like to hear it due to the fear of unemployedness and such, but I think that's a realistic outcome.
Wondering if Modern LLMs like GPT4, Claude Sonnet and llama 3 are closer to human intelligence or next word predictor.
They are good at sounding intelligent. But, LLMs are not intelligent and are not going to save the world. In fact, training them is doing a measurable amount of damage in terms of GHG emissions and potable water expenditure.
The way I would classify it is if you could somehow extract the "creative writing center" from a human brain, you'd have something comparable to to a LLM. But they lack all the other bits, and reason and learning and memory, or badly imitate them.
If you were to combine multiple AI algorithms similar in power to LLM but designed to do math, logic and reason, and then add some kind of memory, you probably get much further towards AGI. I do not believe we're as far from this as people want to believe, and think that sentience is on a scale.
But it would still not be anchored to reality without some control over a camera and the ability to see and experience reality for itself. Even then it wouldn't understand empathy as anything but an abstract concept.
My guess is that eventually we'll create a kind of "AGI compiler" with a prompt to describe what kind of mind you want to create, and the AI compiler generates it. A kind of "nursing AI". Hopefully it's not about profit, but a prompt about it learning to be friends with humans and genuinely enjoy their company and love us.
A remarkable paper has just come out on this topic.
Imo, which is backed a bit by some pretty new studies, not only do LLMs not have intelligence at all, they are incapable of it.
Human intelligence and conciousness likely has a lot to do with nanotubes that trigger quantum wave function collapse, and allow for decision making. Computers simply do not function in this way. Computers are processing machines. They have logic gates with 2 states. 101101110011 binary logic.
If new studies related to nanotubes are right biological brains are simply operating on an entirely diffetent level and playing by a different set of rules than computers. Its not a issue of getting the software right, or getting more processing power. Its an issue of physical capability of the machine to perform certain functions.
I'm going to say x=7, y=10. The sum x+y is not 10, because choosing the next word accurately in a complex passage is hard. The x is 7, just based on my gut guess about how smart they are - by different empirical measures it could be 2 or 40.
I hold a very strong hypothesis, which I’ve not seen any data contradict yet, that intelligence is only possible with formal language and symbolics and therefore formal language and intelligence is very hard to separate. I don’t think one created the other; they evolved together.
Yeah I think the human brain is a vehicle for "mind virus" which is script and ideas.
With GPT o1, I think there is a very small piece of intelligence at play, but it's basically (8.5, 1.5) on this in my mind