ChatGPT is damned impressive, but I hesitate to apply the word "reasoning" to its output. It's also not clear that the backend can be generalized to be able to plug the input into a symbolic manipulation program, or to gather the output and explain it. Only OpenAI knows for certain right now. Powerful if possible though!

Here's why I can't call it "reasoning":

> what is next in the series: a,b,c

> Without additional context, it is impossible to determine what the next element in the series "a,b,c" would be. The series could potentially continue with "d," "e," or any other letter of the alphabet, depending on the pattern or rule being followed. It is also possible that the series does not follow any pattern and is simply a random or arbitrary sequence of letters. Without more information, it is impossible to determine what the next element in the series would be.

> why isn't it d?

> As mentioned in my previous response, without additional context or information about the pattern or rule being followed, it is impossible to determine what the next element in the series "a,b,c" would be. It is not necessarily true that the next element in the series would be "d," as the series could potentially continue with any other letter of the alphabet or follow a different pattern altogether. Without more information, it is impossible to determine what the next element in the series would be.

> The next step would be to combine the two, i.e. tell chatGPT to explain the result of a logic reasoning program in natural language. It could of course also be asked to translate a natural language query into Prolog code.

From what I remember, the very initial prototype of AlphaGo just had a neural net trained on historical games; effectively saying, "what kind of move would a traditional grandmaster make here?" with no planning whatsoever. This was good enough to beat the person who wrote the prototype (who wasn't a master but wasn't a complete novice either); and to make it able to defeat grandmasters, they added Markov chains for planning (which also necessitated a separate neural net for evaluating board positions).

It sounds similar to your suggestion: A model which simply generates realistic-looking sentences is accurate maybe 85% of the time; to make it truly human (or super-human), it needs to be paired with some sort of formal structure -- the analog of the Markov chain. The difficulty being, of course, that the world and its knowledge isn't as simple to represent as a go board.

That said, making coding answers more reliable, by adding a logical structure explicitly designed to support search & testing, should be within reach.