zlacker

>>snewma+(OP)
Doug Lenat, RIP. I worked at Cycorp in Austin from 2000-2006. Taken from us way too soon, Doug none the less had the opportunity to help our country advance military and intelligence community computer science research.

One day, the rapid advancement of AI via LLMs will slow down and attention will again return to logical reasoning and knowledge representation as championed by the Cyc Project, Cycorp, its cyclists and Dr. Doug Lenat.

Why? If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.

>>symbol+nx
> If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.

This is the definition of a strawman. Who is claiming that NN inference is always the fastest way to run computation?

Instead of trying to bring down another technology (neural networks), how about you focus on making symbolic methods usable to solve real-world problems; e.g. how can I build a robust email spam detection system with symbolic methods?

>>halfli+Iz
The point is that symbolic computation as performed by Cycorp was held back by the need to train the Knowledge Base by hand in a supervised manner. NNs and LLMs in particular became ascendant when unsupervised training was employed at scale.

Perhaps LLMs can automate in large part the manual operations of building a future symbolic knowledge base organized by a universal upper ontology. Considering the amazing emergent features of sufficiently-large LLMs, what could emerge from a sufficiently large, reflective symbolic knowledge base?