I have thought this is how we get to really robust, high performance AGI. It seems so obvious.

The steps are:. Have a test environment with a diverse number of auto graded tasks requiring varying levels of skill and cognition. "Big bench" but bigger, call it AGI gym.

The "AGI hypothesis" is an architecture of architectures : it's a set of components interconnected in some way, and those components came from a "seed library" or were auto discovered in another step as a composition of seed components.

The files to define a possible "AGI candidate" are simple and made to be manipulable as an output on an AGI gym task....

Recursion....

You see the idea. So basically I think truly effective AGI architectures are going to be very complex and human hypotheses are wrong. So you find them recursively using prior AGIs that did well on "AGI gym" which includes tasks to design other AGIs among the graded challenges...

Note at the end of the day you end up with a model that does extremely well at "AGI gym". With careful selection of the score heuristic we can select for models that are, well, general and as simple as possible.

It doesn't necessarily have any science fiction abilities, only it will do extremely well at tasks that are mutations of the gym task. If some of them are robotics tasks with realistic simulated input from the real world, it would do well in the real world at those tasks also.

Some of the tasks would be to "read this description of what I want you to do in the simulated world and do it with this robot". And the descriptions are procedurally generated from a very large set.

The whole process would be ongoing - each commit makes AGI gym harder, and the population of successful models gets ever more capable. You fund this by selling the services of the current best models.