Comment by oldge
7 days ago
Today’s llms are fancy autocomplete but lack test time self learning or persistent drive. By contrast, an AGI would require: – A goal-generation mechanism (G) that can propose objectives without external prompts – A utility function (U) and policy π(a│s) enabling action selection and hierarchy formation over extended horizons – Stateful memory (M) + feedback integration to evaluate outcomes, revise plans, and execute real-world interventions autonomously Without G, U, π, and M operating llms remain reactive statistical predictors, not human level intelligence.
I'd say we're not far off.
Looking at the human side, it takes a while to actually learn something. If you've recently read something it remains in your "context window". You need to dream about it, to think about, to revisit and repeat until you actually learn it and "update your internal model". We need a mechanism for continuous weight updating.
Goal-generation is pretty much covered by your body constantly drip-feeding your brain various hormones "ongoing input prompts".
> I'd say we're not far off.
How are we not far off? How can LLMs generate goals and based on what?
You just train it on the goal. Then it has that goal.
Alternately, you can train it on following a goal and then you have a system where you can specify a goal.
At sufficient scale, a model will already contain goal-following algorithms because those help predict the next token when the model is basetrained on goal-following entities, ie. humans. Goal-driven RL then brings those algorithms to prominence.
5 replies →
Minimize prediction errors.
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Yes, you're right, that's what we're doing.
https://github.com/dmf-archive/PILF
Very interesting, thanks for the link.
we're closer than you think...
In fact, there is no technical threshold anymore. As long as the theory is in place, you can see such AGI at most half a year. It will even be more energy efficient than the current dense models.
https://dmf-archive.github.io/docs/posts/beyond-snn-plausibl...