zlacker

>>iafish+(OP)
Cyc is one of those bad ideas that won't die, and which keeps getting rediscovered on HN. Lenat wasted decades of his life on it. Knowledge graphs like Cyc are labor intensive to build and difficult to maintain. They are brittle in the face of change, and useless if they cannot represent the underlying changes of reality.

>>blueye+Fp
I think before 2022 it was still an open question whether it was a good approach.

Now it's clear that knowledge graphs are far inferior to deep neural nets, but even still few people can explain the _root_ reason why.

I don't think Lenat's bet was a waste. I think it was sensible based on the information at the time.

The decision to research it largely in secret, closed source, I think was a mistake.

>>breck+1z
I assume the problem with symbolic inference is that from a single inconsistent premise logic can produce any statement possible.

If that is so then symbolic AI does not easily scale because you cannot feed inconsistent information into it. Compare this to how humans and LLMs learn, they both have no problem with inconsistent information. Yet statistically speaking humans can easily produce "useful" information.

>>galaxy+0q1
Based on the article, it seems like the Cyc had ways to deal with inconsistency. I don't know the details of how they did it, but Paraconsistant Logics [0] provide a general way to prevent any statement from being provable from an inconsistency.

[0] https://en.wikipedia.org/wiki/Paraconsistent_logic