Blog post review: LOVE in a simbox.

Jake Cannell has a very interesting post on LessWrong called LOVE in a simbox is all you need, with potentially important implications for AGI alignment. (LOVE stands for Learning Other’s Values or Empowerment.)

Alas, he organized it so that the most alignment-relevant ideas are near the end of a long-winded discussion of topics whose alignment relevance seems somewhat marginal. I suspect many people gave up before reaching the best sections.

I will summarize and review the post in roughly the opposite order, in hopes of appealing to a different audience. I’ll likely create a different set of misunderstandings from what Jake’s post has created. Hopefully this different perspective will help readers triangulate on some hypotheses that are worth further analysis.

Approximately a book review: Eric Drexler’s QNR paper.

[Epistemic status: very much pushing the limits of my understanding. I’ve likely made several times as many mistakes as in my average blog post. I want to devote more time to understanding these topics, but it’s taken me months to produce this much, and if I delayed this in hopes of producing something better, who knows when I’d be ready.]

This nearly-a-book elaborates on his CAIS paper (mainly chapters 37 through 39), describing a path for AI capability research enables the CAIS approach to remain competitive as capabilities exceed human levels.

AI research has been split between symbolic and connectionist camps for as long as I can remember. Drexler says it’s time to combine those approaches to produce systems which are more powerful than either approach can be by itself.

He suggests a general framework for how to usefully combine neural networks and symbolic AI. It’s built around structures that combine natural language words with neural representations of what those words mean.

Drexler wrote this mainly for AI researchers. I will attempt to explain it to a slightly broader audience.

This post is mostly a response to the Foresight Institute’s book Gaming the Future, which is very optimistic about AI’s being cooperative. They expect that creating a variety of different AI’s will enable us to replicate the checks and balances that the US constitution created.

I’m also responding in part to Eliezer’s AGI lethalities, points 34 and 35, which say that we can’t survive the creation of powerful AGI’s simply by ensuring the existence of many co-equal AGI’s with different goals. One of his concerns is that those AGI’s will cooperate with each other enough to function as a unitary AGI. Interactions between AGI’s might fit the ideal of voluntary cooperation with checks and balances, yet when interacting with humans those AGI’s might function as an unchecked government that has little need for humans.

I expect reality to be somewhere in between those two extremes. I can’t tell which of those views is closer to reality. This is a fairly scary uncertainty.

[Epistemic status: mostly writing to clarify my intuitions, with just a few weak attempts to convince others. It’s no substitute for reading Drexler’s writings.]

I’ve been struggling to write more posts relating to Drexler’s vision for AI (hopefully to be published soon), and in the process got increasingly bothered by the issue of whether AI researchers will see incentives to give AI’s broad goals that turn them into agents.

Drexler’s CAIS paper convinced me that our current trajectory is somewhat close to a scenario where human-level AI’s that are tool-like services are available well before AGI’s with broader goals.

Yet when I read LessWrong, I sympathize with beliefs that developers will want quite agenty AGI’s around the same time that CAIS-like services reach human levels.

I’m fed up with this epistemic learned helplessness, and this post is my attempt to reconcile those competing intuitions.