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.
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’re still word predictors. That is literally how the technology works
Yeah, the only question is whether human brains are also just that.
no, they are not. try showing an ai a huge number of pictures of cars from the front. Then show them one car from the side, and ask them what it is.
Show a human one picture of a car from the front, then the one from the side and ask them what it is.
What if the human had never seen or heard of anything similar to cars?
I bet it’d be confused as much as the llm.
That’s why you show him one, before asking what that same car viewed from a different angle is.
I had never seen a recumbent bike before. I only needed to see one to know and recognize one whenever I see one. Even one with a different color or make and model. The human brain definitely works differently.
You know what bicycle are though. And you’re heard of recumbent bikes or things similar to it.
If you had never heard of anything similar at all to bikes, and saw a picture of a recumbent bike from the front only, you’d probably think “ I have no fucking idea what that is”.
Idk man, weird for you to think humans can kinda learn fully about something without all the required context.
you keep missing the fact that I don’t know out of nowhere. You would have just shown me one and told me what it was. Yes of course I’d be able to tell you what it was. You just taught me. With one example.
To understand a recumbent bicycle you have to understand bicycles. To understand bicycles you have to understand wheels. You have to understand humans, and human transportation. What IS transportation. What are roads. What is a pedal. What is steering. How physics works for objects in motion. Etc etc etc etc.
You truly underestimate the amount of context and previous knowledge you need to understand even the simplest things.
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.
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.
But how do you know that the human brain is not just a super sophisticated next-thing predictor that by being super sophisticated manages to incorporate nuance and all that stuff to actually be intelligent? Not saying it is but still.
Because we have reason, understanding. Take something as simple as the XY problem. Humans understand that there are nuances to prompts and questions. I like the XY because a human knows to step back and ask “what are you really trying to do?”. AI doesn’t have that capability, it doesn’t have reasoning to say “maybe your approach is wrong”.
So, I’m not the one to define what it is or on what scale. But I can say that it’s not human intelligence.
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 llm’s other machine learning (like image recognition) some old fashioned code.
Op does ask “modern llm” so technically you are right but i believed they did mean the more advanced “products”
That is just next word prediction with extra steps.
Now that is fair.
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.
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.
How is good old fashioned code comparing outputs to a database of factual knowledge “predicting the next token” to you. Or reinforcement relearning and token rewards baked into models.
I can tell you have not actually tried to work with professional ai or looked at the research papers.
Yes none of it is “intelligent” but i would counter that with neither are human beings, we dont even know how to define intelligence.
can you give an example of any third data point such as a rock or a chicken
rockegg
Modern LLMs are basically really fancy Markov chains.
This should just be a 1D spectrum line.
</dataviz>
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 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.
(-10, 20)
Shouldn’t those be opposite sides of the same axis, not two different axes? I’m not sure how this graph should work.
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
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.
That just seems like someone read about Roko’s basilisk and decided to rebrand that nightmare as the mission/vision of a company.
What a time to be alive!
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.
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.
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 them. 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.
you use “luddite” as if it’s an insult. History proved luddites were right in their demands and they were fighting the good fight.
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.
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.
I know lots of humans who can’t do maths. At least I think they’re human. Maybe there LLMs, by your definition.
I think you’re missing the point. No LLM can do math, most humans can. No LLM can learn new information, all humans can and do (maybe to varying degrees, but still).
AMD just to clarify by not able to do math. I mean that there is a lack of understanding in how numbers work where combining numbers or values outside of the training data can easily trip them up. Since it’s prediction based, exponents/tri functions/etc. will quickly produce errors when using large values.
