As a result, rather than indefinite and immediate exponential growth, I expect real-world AI growth to follow a series of sigmoidal curves, each eventually plateauing before different types of growth curves take over to increase capabilities based on different input resources (with all of this overlapping).
Hi Andy—how are you gauging the likely relative proportions of AI capability sigmoidal curves relative to the current ceiling of human capability? Unless I’m misreading your position, it seems like you are presuming that the sigmoidal curves will (at least initially) top out at a level that is on the same order as human capabilities. What informs this prior?
Due to the very different nature of our structural limitations (i.e. a brain that’s not too big for a mother’s hips to safely carry and deliver, specific energetic constraints, the not-very-precisely-directed nature of the evolutionary process) vs an AGI’s system’s limitations (which are simply different) it’s totally unclear to me why we should expect the AGI’s plateaus to be found at close-to-human levels.
Might LLMs help with this? You could have a 4.3 million word conversation with an LLM (with longer context windows than what’s currently available) which could then, in parallel, have similarly long conversations with arbitrarily many members of the organization, adequately addressing specific confusions individually, and perhaps escalating novel confusions to you for clarification. In practice, until the LLMs become entertaining enough, members of the organization may not engage for long enough, but perhaps this lack of seductiveness is temporary.