Limitations of LLMs in achieving true intelligence and innovation

Signals over time

2 observed signals across 1 sources, tracked for 0 days. Confidence: low.

What people are saying Observed

• “LLMs are very useful, I use them every day as a software engineer to solve problems and search for information represented within the data available to them. But they are a specific type of intelligence, with many advantages and disadvantages vs human intelligence and it's not clear that just scaling or tweaking them without a theoretical, architectural change will make them more generally intelligent than humans (despite all US AI companies promising exactly that). They are fundamentally based in language, and achieving deeper models of the world through language alone is deeply inefficient compared to the way humans model the world for years without any language at all. They do not learn at inference time. They don't have semantic understanding of the difference between their own output and other sources. etc etc. That depth is the key for me. Of course they are capable of producing novel sentences that aren't in their training data, but the depth of that novelty is basically within the bounds of language itself. They are capable of more serious depth and more abstract reasoning than that, but I have experienced limits, which it then tries to surpass with tools to ”

  Hacker News · frustration

• “Where some see intelligence, others see token prediction. It's a very good question how token prediction could achieve this, but I think there's a simple explanation. No human can hold more than a negligible percent of all knowledge in his mind at once. LLMs have no such limits and so can reliably connect 'obvious' dots that we miss simply for lack of storage capability. Well isn't that just semantics? Surely connecting dots in a novel and meaningful is intelligence regardless of how it's achieved. The thing is that humans didn't get to where we are by connecting obvious dots. Go back to before humans had invented language and when bleeding edge tech was literally that - 'poke him with the pointy end.' Train an LLM on that corpus of knowledge. Even given infinite processing power and infinite time - it's not going to discover the secrets of the atom, put a man on the Moon, or do much of anything besides remix what we'd already done at the time. I expect there's still much LLMs can achieve simply because of this initial problem. But I expect that they will ultimately start to plateau once these dots have been mostly matched”

  Hacker News · frustration