I was thinking about how there isn't really an "executable" version of knowledge.
In software, we have declarative data (data notation like JSON and Edn, wire formats like Protobuf) and executable code (functions, expressions, procedures that operate on data). Both data "code" and executable "code" are first-class materials that we work with to describe software systems.
In the realm of less structured, more general knowledge work, though, it's not obvious that there is such a thing as "executable knowledge". Knowledge feels static and declarative -- a collection of statements of facts about the world.
| Declarative | Executable ------------------------------------ Software | Data | Programs Knowledge | Facts | ???
One way to fill this blank would be to notice that programs are transformations on data: programs take in some information and transforms it to modify it or output new information. By that logic, whatever goes in that "executable knowledge" blank, we could say, should enable transformations on knowledge: take in some knowledge about the world, and yield some new statements about the world. Another word for this might be inference; we begin with some base of knowledge, and infer what we may not have known before. Help us expand into the frontiers of knowledge.
It used to be that the only way to automate inference was using logic programming systems like Prolog or theorem provers, which required careful manual specification of known facts about the world, as well as an explicit enumeration of all the rules the system was allowed to use to derive conclusions from facts. Today, we have tools that feel more natural for humans to use, because they learn these inference rules of the world implicitly through observation and training.
One such kind of tool is GPT-style language models. These models can be plugged into conversational interfaces, where humans can give the model access to some base of knowledge and draw conclusions by asking questions or instructing the model directly to compute e.g. a summary or an analysis from existing data.
But to me, GPT-style conversational models don't feel like a robust, well-structured kind of runtime for structured thought that programs can often be. They're a little too squishy and probabilistic (though there's ongoing work to invent more structured ways of prompting GPTs). When I want more structure and composability, I still think there is interesting potential in expressing inference steps as movements in the latent space of these models. Latent space movements are just vector arithmetic, which opens up the possibility of expressing and manipulating complex chains of thought as crisp mathematical objects rather than soft human instructions. There are also hints of cool Lisp-like homoiconicity in the idea of expressing both ideas and transformations on ideas using the same type of data -- vectors in latent space.