We have taken a noticeable step forwards towards AGI in the last few years, but AGI is still a long way off.
We have taken a noticeable step forwards towards AGI in the last few years, but AGI is still a long way off.
https://general-pattern-machines.github.io/
>but we haven't got game playing sorted yet
4 can play chess, Minecraft fine. So I imagine a lot of games fall down to data.
>and there's nothing in these models for movement or robotics
The point is that there doesn't need to be.
There are many projects using LLMs to pilot robots off text prediction ability.
https://tidybot.cs.princeton.edu/ https://innermonologue.github.io/ https://www.deepmind.com/blog/rt-2-new-model-translates-visi...
I think most from the 2000s would be calling what we have today AGI.
Proactive models are simple — just generate the “internal monologue” periodically from the system prompt, mix it with the compressed state, then feed it to another model (the outputs are used as state updates). I think this is how human agency works as well.
Movement is a different story (LLMs do not work here, except as planners), but it pretty much solved too. Robot hardware is too expensive, though, and it is not clear how to power it autonomously.
You should read the article. It talks about exactly this use case, using an LLM as part of robotics control systems.