Any correlation between current models and AGI is, IMO, hype.
GenAI is a remarkably useful tool, but its not one step away from an AGI.
GenAI is a remarkably useful tool, but its not one step away from an AGI.
I guess the challenge is more to agree on a fitness function to measure the "AGI"-progress" against, but thats a different topic. But in general scaling up the current GenAI tech and parallelize/specialize the models in a multi-generational way _should_ be a safe ticket to AGI, but the time scale is inknown of course (since we can't even agree on the goal definition)
The current LLM's get stuck in loops when a problem is too hard for it. They just keep doing the wrong thing over and over. It's not obvious this sort of ai can "build novel attempts" at hard problems.
I think you'll find that AGI cynics do not agree at all that "engineering a 10x/100x version" of what we have and making it attempt "AGI algorithms 24/7 in an evolutionary setting" is a "safe ticket" to AGI.