technology

That's my thinking as well. The LLM is basically an interface to the world that can handle ambiguity and novel contexts. Meanwhile, symbolic AI provides a really solid foundation for actual thinking. And LLMs solve the core problem of building ontologies on the fly that's been the main roadblock for symbolic engines. The really exciting part about using symbolic logic is that you can actually ask the model how it arrived at a solution, you can tell it that a specific step is wrong and change it, and have it actually learn things in a reliable way. It would be really neat if the LLM could spin up little VMs for a particular context, train the logic engine to solve that problem, and then save them in a library of skills for later user. Then when it encounters a similar problem, it could dust off an existing skill and apply it. And the LLM bit of the engine could also deal with stuff like transfer learning, where it could normalize inputs from different contexts into a common format used in the symbolic engine too. There are just so many possibilities here.