The obsession with whether LLMs can out perform classical AI, algorithms, solvers, and other optimizers fascinates me. Intuitively of course they aren’t doing s

The obsession with whether LLMs can out perform classical AI, algorithms, solvers, and other optimizers fascinates me. Intuitively of course they aren’t doing something similar to a solver. They will never play chess better than the best for purpose chess playing system. They will never reason better than a classical reasoning system. That misses the point entirely. They are a fascinating augmentation to existing capabilities that fills an ability to operate and do some approximation to reason and plan in an abstract semantic space, using natural language in a semantic way, to perform an abductive style of “reasoning” that has alluded us to date. Need to play chess? Use a chess playing system. But you can glue together special purpose systems for planning, reasoning, optimizing, calculating, etc, with an LLM in ways that are much more flexible and adaptable in a real world context than we’ve ever been able to achieve. That’s the magic of them. The fact they can do pretty alright at some of these tasks is interesting and shows how powerful language is that embedded in its semantic structure is an awful lot of what you need to reason, plan, etc. But beyond the academic question, why would you not just use a provably optimal algorithm for a specific task? Interestingly, given a set of operations in context, LLMs are pretty good at identifying a situation where an operation is applicable and making the API call, and as we learn how to embed them, they’ll be able to defer to and be deferred to more accurately.