[patientplatypus]

I read a NY Times article by Ted Chiang today in which he made a kind of "stochastic parrot" argument for chatGPT - https://www.newyorker.com/tech/annals-of-technology/chatgpt-.... I believe, on the other hand that chatGPT may eventually be able to generate AGI, but that this will occur emergently and spontaneously. In other words, it will be difficult to predict.

One of the conditions for this, is for chatGPT models to start being able to write their own code in order to produce models of themselves that are more accurate and more efficient. Given this ability, and some fitness criteria, genetic algorithms may be used to create new LLMs. This sounds like science fiction, but once the compute requirements come down for these models (by a couple orders of magnitude), I believe this may be possible.

To what extent does your model allow for semantic models to create semantic models that are themselves more efficient in relation to some fitness criteria? Can I tell a model "You (model) I want you to reproduce using interaction with these other models (some collection of other models) and have the child model offspring be more efficient according to this criteria [for example the resultant models will create short stories that are more likely to receive high ratings on a subreddit devoted to short stories]".

You would need to get around the "model pollution" problem in which LLM models pollute the space for which the models generate data because other models are producing web artifacts (Ted Chiang's Xerox of a Xerox problem). I call this the problem of alpha (direct experience). One of the ways I've thought of to fix this is to have models trained on direct user input (such as cell phone video and pictures from a single user) - I have to admit that I got this idea from Neal Stephenson's Snow Crash (see Gargoyle). If your platform can integrate with visual processing this may have a high information density - object detection in daily videos demonstrating how objects are related to each other in the real world of the user and correlating these into a semantic network.

I'd also suggest that Obsidian integration might be useful.

This is exciting, thanks for making the Fixie SDK public.