I've been fascinated with the prospect of building AGI based upon some finite set of first principles for decades. I studied in great detail Hegel's "Science of Logic," which despite its lunatic sounding writing nevertheless has always seemed to me a robust ontology. It basically starts from quality and quantity and works its way gradually through inner experience and outer reality, logic, and finally to the "idea." I spent a couple of years studying this and created my own understandable notes. I have partially built out a neuro-symbolic cog architecture basically structured on Hegel's ontology (paper still in progress).
Now, what you describe of Chalmers eg "our physical universe works" sounds to me like being built atop invariant first principles like causality and becoming. I see these in your ontology. The word no respectable scientist likes to use is "metaphysics," because it sounds like pompous BS. But the reality is that the universe seems to work on -- be governed by -- first principles, which "have justification independent of experience," which I regard as metaphysics. The principles seem to be taken together to form concrete objects and ideas.
I like this direction you are taking but it seems like it would be possible to minimize the first principles. You have a lot. In the last few years though with the explosion of LLM success I wonder if a base ontology is really necessary but still it seems that in order for the machine to "understand" a finite set of first priniples in an ontology should form the ground. The understanding bottoms out on the base of the ontology and can proceed no further.
This is an amazingly lucid overview of the challenges faced by formalization of knowledge.
It is no surprise that AI companies decided that they will focus on the much narrower task of cataloguing all useful problem-solving paradigms, and building a bot that can weakly generalize around them. Without trying to make sense of that.
If there are some unifying patterns, they will bubble up in the neural nets.
The industry is now working on the "reasoning" and "grounding of symbols" aspects, via ad-hoc methods such as chain-of-thought, generating runnable code, and invoking external tools as needed.
I've been fascinated with the prospect of building AGI based upon some finite set of first principles for decades. I studied in great detail Hegel's "Science of Logic," which despite its lunatic sounding writing nevertheless has always seemed to me a robust ontology. It basically starts from quality and quantity and works its way gradually through inner experience and outer reality, logic, and finally to the "idea." I spent a couple of years studying this and created my own understandable notes. I have partially built out a neuro-symbolic cog architecture basically structured on Hegel's ontology (paper still in progress).
Now, what you describe of Chalmers eg "our physical universe works" sounds to me like being built atop invariant first principles like causality and becoming. I see these in your ontology. The word no respectable scientist likes to use is "metaphysics," because it sounds like pompous BS. But the reality is that the universe seems to work on -- be governed by -- first principles, which "have justification independent of experience," which I regard as metaphysics. The principles seem to be taken together to form concrete objects and ideas.
I like this direction you are taking but it seems like it would be possible to minimize the first principles. You have a lot. In the last few years though with the explosion of LLM success I wonder if a base ontology is really necessary but still it seems that in order for the machine to "understand" a finite set of first priniples in an ontology should form the ground. The understanding bottoms out on the base of the ontology and can proceed no further.
Thanks, interesting read.
This is an amazingly lucid overview of the challenges faced by formalization of knowledge.
It is no surprise that AI companies decided that they will focus on the much narrower task of cataloguing all useful problem-solving paradigms, and building a bot that can weakly generalize around them. Without trying to make sense of that.
If there are some unifying patterns, they will bubble up in the neural nets.
The industry is now working on the "reasoning" and "grounding of symbols" aspects, via ad-hoc methods such as chain-of-thought, generating runnable code, and invoking external tools as needed.