Contra hard moral anti-realism: a rough sequence of claims

Epistemic and provenance note: This post should not be taken as an attempt at a complete refutation of moral anti-realism, but rather as a set of observations and intuitions that may or may not give one pause as to the wisdom of taking a hard moral anti-realist stance. I may clean it up to construct a more formal argument in the future. I wrote it on a whim as a Telegram message, in direct response to the claim 

> “you can't find "values" in reality”.

Yet, you can find valence in your own experiences (that is, you just know from direct experience whether you like the sensations you are experiencing or not), and you can assume other people are likely to have a similar enough stimulus-valence mapping. (Example: I'm willing to bet 2k USD on my part against a single dollar yours that that if I waterboard you, you'll want to stop before 3 minutes have passed.)^[1]

However, since we humans are bounded imperfect rationalists, trying to explicitly optimize valence is often a dumb strategy. Evolution has made us not into fitness-maximizers, nor valence-maximizers, but adaptation-executers.

"values" originate as (thus are) reifications of heuristics that reliably increase long term valence in the real world (subject to memetic selection pressures, among them social desirability of utterances, adaptativeness of behavioral effects, etc.)

If you find yourself terminally valuing something that is not someone's experienced valence, then either one of these propositions is likely true:

• A nonsentient process has at some point had write access to your values.

• What you value is a means to improving somebody's experienced valence, and so are you now.

   

crossposted from lesswrong

1. ^{^}

   In retrospect, making this proposition was a bit crass on my part.

EA Forum feature request: Can we get a bluesky profile link button for profile pages?

In a certain sense, an LLM's token embedding matrix is a machine ontology. Semantically similar tokens have similar embeddings in the latent space. However, different models may have learned different associations when their embedding matrix was trained. Every forward pass starts colored by ontological assumptions, an these may have alignment implications.

For instance, we would presumably not want a model to operate within an ontology that associates the concept of AI with the concept of evil, particularly if it is then prompted to instantiate a simulacrum that believes it is an AI.

Has someone looked into this? That is, the alignment implications of different token embedding matrices? I feel like it would involve calculating a lot of cosine similarities and doing some evals.