I don’t believe that. At least from an implementation perspective we’re extremely early on, and I don’t see why the tech itself can’t be improved either.
Maybe it’s current iteration has hit a wall, but I don’t think anyone can really say what the future holds for it.
LLMs have been around since roughly 2016 2017 (comment below corrected me that Attention paper was 2017). While scaling the up has improved their performance/capabilities, there are fundamental limitations on the actual approach. Behind the scenes, LLMs (even multimodal ones like gpt4) are trying to predict what is most expected, while that can be powerful it means they can never innovate or be truth systems.
For years we used things like tf-idf to vectorize words, then embeddings, now transformers (supped up embeddings). Each approach has it limits, LLMs are no different. The results we see now are surprisingly good, but don’t overcome the baseline limitations in the underlying model.
I’m not trained in formal computer science, so I’m unable to evaluate the quality of this paper’s argument, but there’s a preprint out that claims to prove that current computing architectures will never be able to advance to AGI, and that rather than accelerating, improvements are only going to slow down due to the exponential increase in resources necessary for any incremental advancements (because it’s an NP-hard problem). That doesn’t prove LLMs are end of the line, but it does suggest that additional improvements are likely to be marginal.
AI as a general concept probably will at some point. But LLMs have all but reached the end of the line and they’re not nearly smart enough.
LLMs have already reached the end of the line 🤔
I don’t believe that. At least from an implementation perspective we’re extremely early on, and I don’t see why the tech itself can’t be improved either.
Maybe it’s current iteration has hit a wall, but I don’t think anyone can really say what the future holds for it.
LLMs have been around since roughly
20162017 (comment below corrected me that Attention paper was 2017). While scaling the up has improved their performance/capabilities, there are fundamental limitations on the actual approach. Behind the scenes, LLMs (even multimodal ones like gpt4) are trying to predict what is most expected, while that can be powerful it means they can never innovate or be truth systems.For years we used things like tf-idf to vectorize words, then embeddings, now transformers (supped up embeddings). Each approach has it limits, LLMs are no different. The results we see now are surprisingly good, but don’t overcome the baseline limitations in the underlying model.
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You’re right, I thought that paper came out in 2016.
I’m not trained in formal computer science, so I’m unable to evaluate the quality of this paper’s argument, but there’s a preprint out that claims to prove that current computing architectures will never be able to advance to AGI, and that rather than accelerating, improvements are only going to slow down due to the exponential increase in resources necessary for any incremental advancements (because it’s an NP-hard problem). That doesn’t prove LLMs are end of the line, but it does suggest that additional improvements are likely to be marginal.
Reclaiming AI as a theoretical tool for cognitive science
Oh really! The analysis has been established since the 80’s. Its so far from early on that statement is comical
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The next 10 years
“at some point” being like 400 years in the future? Sure.
Ok that’s probably a little bit of an exaggeration. 250 years.