LLMs really can't be improved all
that much beyond what we currently have, because they're fundamentally limited by their architecture, which is what ultimately leads to this sort of behaviour.
Unfortunately the AI bubble seems to be predicated on just improving LLMs and really really hoping that they'll magically turn into even weakly general AIs (or even AGIs like the worst Kool-aid drinkers claim they will), so everybody is throwing absolutely bonkers amounts of money at incremental improvements to existing architectures, instead of doing the hard thing and trying to come up with better architectures.
I doubt static networks like LLMs (or practically all other neural networks that are currently in use) will ever be candidates for general AI. All they can do is react to external input, they don't have any sort of an "inner life" outside of that, ie. the network isn't active except when you throw input at it. They literally can't even learn, and (re)training them takes ridiculous amounts of money and compute.
I'd wager that for producing an actual AGI, spiking neural networks or something similar to them would be what you'd want to lean in to, maybe with some kind of neuroplasticity-like mechanism. Spiking networks already exist and they can do some pretty cool stuff, but nowhere near what LLMs can do right now (even if they do do it kinda badly). Currently they're harder to train than more traditional static NNs because they're not differentiable so you can't do backpropagation, and they're still relatively new so there's a lot of open questions about eg. the uses and benefits of different neural models and such.
Unfortunately the AI bubble seems to be predicated on just improving LLMs and really really hoping that they'll magically turn into even weakly general AIs (or even AGIs like the worst Kool-aid drinkers claim they will), so everybody is throwing absolutely bonkers amounts of money at incremental improvements to existing architectures, instead of doing the hard thing and trying to come up with better architectures.
I doubt static networks like LLMs (or practically all other neural networks that are currently in use) will ever be candidates for general AI. All they can do is react to external input, they don't have any sort of an "inner life" outside of that, ie. the network isn't active except when you throw input at it. They literally can't even learn, and (re)training them takes ridiculous amounts of money and compute.
I'd wager that for producing an actual AGI, spiking neural networks or something similar to them would be what you'd want to lean in to, maybe with some kind of neuroplasticity-like mechanism. Spiking networks already exist and they can do some pretty cool stuff, but nowhere near what LLMs can do right now (even if they do do it kinda badly). Currently they're harder to train than more traditional static NNs because they're not differentiable so you can't do backpropagation, and they're still relatively new so there's a lot of open questions about eg. the uses and benefits of different neural models and such.