Another way to approach AGI? (Very early, preliminary thoughts.)

Say you write an evolutionary algorithm, like the ones that have been written before. Then DD would argue it’ll get stuck because all it can do is explore a given landscape for its best features. Whereas real evolution creates new landscapes.

To address this issue, you subject your algorithm itself to variation and selection, by wrapping it in another evolutionary algorithm. But this approach just kicks the can down the road because now it’s the space of programs that’s limited.

How do you break out of this limitation?

You can’t just keep wrapping your programs in evolutionary algorithms like that because that only keeps kicking the can down the road. It’s like adding more and more entries to a multiplication table. It’s not the same as a multiplication algorithm. But for evolution, the problem is harder, in a way, because not even recursion solves the issue, and the starting point wasn’t as ‘flat’ as a multiplication table. The starting point is already an algorithm, not just a list.

What’s needed, in DD’s lingo, is a jump to universality. But a jump to what kind of universality, exactly?

cc @tyler-mills