[beardyw]

I made something that played noughts and crosses (same thing) perfectly. Knowing what responses you will make and taking into account reflections and rotations I had a state machine with barely more than 100 states. It really doesn't need AI, but an LLM is a language model for goodness sake, give it a job it can do.

[jfengel]

LLMs are supposed to be a step on the route to a general AI. This is a data point suggesting that, if it can't do this simple job, it may never be suited to a wide range of jobs that any human can do trivially. That's a serious crimp in its reputation as an AGI.

There may be a solution to that at some point. But right now it suggests we're more on the stochastic-parrot track.

[d_legs]

Totally agree that there are better ways to build a program to beat tic tac toe. I'd expect an LLM could probably write the code itself with a few turns of the crank (it wrote most of the code I used to test this). The point here is to test the G in AGI: how well models can generalize to new tasks.

[beardyw]

My quibble is with the use of language models. Human intelligence is not predicated on the written word. You wouldn't use an LLM to create something that plays tennis, yet our human general intelligence covers that.