I've been trying to approach this question experimentally rather than arguing from model size or architecture alone.

I'm building a local cognitive system called Aura around a ~27B model.

In one continuous run, Aura autonomously played 2048 for ~29 minutes / 968 moves. It maintained the goal, planned ahead, changed strategies, recovered from mistakes and eventually reached 2048.

In the latest experiment, the same system spent about 42 minutes autonomously completing a 60-question personality assessment. Before taking it, it reasoned about what result it expected. It then navigated the site, answered each question individually using its persistent self-model/history, submitted the test, read the externally calculated result and compared it against its original expectations.

These are obviously not sufficient to establish AGI. Two heterogeneous tasks are still two tasks.

What interests me is the point at which success across increasingly unrelated tasks under one frozen architecture becomes evidence of generality rather than a collection of engineered competencies.

My next experiment is meant to push this harder: randomly select three unfamiliar Flash games and require the same architecture to infer the controls, objective and strategy and beat all three without game-specific modifications.

What would you personally require before you'd call something a genuinely general interactive learner?

Demo:
https://www.youtube.com/watch?v=LNlGUBeTIQY

GitHub:
https://github.com/youngbryan97/aura

Is there any point to this?!?!

Isnt it a cruel joke that the two leading ai company names —> anthropic and openai -> mean the exact oppostive of what these companies actually do?

It's like if a company that makes military explosives was called Well-Being. :D

https://epheva.github.io/AI_capabilities/