Comment on How LLMs can be Assisted to do Arithmetic Correctly by Mouse

In reply to Reactive Light.

It scales surprisingly well to a surprising number of problem domains.

The important thing to note is that the kind of neural networks that are used in LLMs is fundamentally unsuited to intelligence. It does, however, make a pretty good adaptor to probabilistic-pattern domains such as NLP.

Don't use the model to think. Use the model to adapt.

Use logic to code the rules, not neurons.

There are other kinds of networks that are potentially much closer to what feedforward neural networks with static weights have been hyped to be, and some of those might eventually be useful as intelligence engines. They may even be power-efficient and low-latency. We do not have those technologies available at the moment. But when/if we do get them, they probably won't be as good at language; linguistics and intelligence problems don't structure at all the same, and need different solutions. In a way, it's similar to how PID controllers aren't good choices for discontiguous domains.