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.
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.
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.
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.
They’re still word predictors. That is literally how the technology works
This should just be a 1D spectrum line.
</dataviz>
Modern LLMs are basically really fancy Markov chains.
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.
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.
All A no I
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)
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
