> In many ways the great quest of Doug Lenat’s life was an attempt to follow on directly from the work of Aristotle and Leibniz.
Such a wonderful, respectful retrospective of Lenat's ideas and work.
> I think Doug viewed CYC as some kind of formalized idealization of how he imagined human minds work: providing a framework into which a large collection of (fairly undifferentiated) knowledge about the world could be “poured”. At some level it was a very “pure AI” concept: set up a generic brain-like thing, then “it’ll just do the rest”. But Doug still felt that the thing had to operate according to logic, and that what was fed into it also had to consist of knowledge packaged up in the form of logic.
I've always wanted CYC, or something like it, to be correct. Like somehow it'd fulfill my need for the universe to be knowable, legible. If human reason & logic could be encoded, then maybe things could start to make sense, if only we try hard enough.
Alas.
Back when SemanticWeb was the hotness, I was a firm ontology partisan. After working on customer's use cases, and given enough time to work thru the stages of grief, I grudgingly accepted the folksonomy worldview is probably true.
Since then, of course, the "fuzzy" strategies have prevailed. (Also, most of us have accepted humans aren't rational.)
To this day, statistics based approaches make me uncomfortable, perhaps even anxious. My pragmatism motivated holistic worldview is always running up against my reductionist impulses. Paradox in a nutshell.
Enough about me.
> Doug’s starting points were AI and logic, mine were ... computation writ large.
I do appreciate Wolfram placing their respective theories in the pantheon. It's a nice reminder of their lineages. So great.
I agree with Wolfram that encoding heuristics was an experiment that had to be done. Negative results are super important. I'm so, so glad Lenat (and crews) tried so hard.
And I hope the future holds some kind of synthesis of these strategies.
Recently I’ve been involved in discussions about using an LLM to generate JSON according to a schema, as in OpenAI’s function calling or Jsonformer-LLMs do okay for generating code in mainstream languages like SQL or Python, but what if you have some proprietary query language? Maybe have a JSON schema for the AST, have the LLM generate JSON conforming to that schema, then serialise the JSON to the proprietary query language syntax?
And it makes me think - what if one used an LLM to generate or evaluate assertions in a Cyc-style ontology language? And that might be a bridge between the logic/ontology approach and the statistical/neural approach