They are both different parts of the same problem. Prolog can solve logical problems using symbolism. ChatGPT cannot solve logical problems, but it can approximate human language to an astonishing degree. If we ever create an AI, or what we now call an AGI, it will include elements of both these approaches.
In “Computing Machinery and Intelligence”, Turing made some really interesting observations about AI (“thinking machines” and “learning machines” as they were called then). It demonstrates stunning foresight:
An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside… This is in clear contrast with normal procedure when using a machine to do computations: one’s object is then to have a clear mental picture of the state of the machine at each moment in the computation. This object can only be achieved with a struggle.
Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation, but a rather slight one, which does not give rise to random behaviour, or to pointless repetitive loops.
You can view ChatGPT and Prolog as two ends of the spectrum Turing is describing here. Prolog is “thinking rationally”: It is predictable, logical. ChatGPT is “acting humanly”: It is an unpredictable, “undisciplined” model but does exhibit very human-like behaviours. We are “quite ignoerant of what is going on inside”. Neither approach is enough to achieve AGI, but they are such fundamentally different approaches that it is difficult to conceive of them working together except by some intermediary like Subsumption Architecture.
This is what I expect too. And hope - LLMs are way too unpredictable to control important things on their own.
I often say LLMs are doing for natural language what early computation did for mathematics. There’s still plenty of mathy jobs computers can’t do, but the really repetitive ones are gone and somewhat forgotten - nobody thinks of “computer” as a title.
They are both different parts of the same problem. Prolog can solve logical problems using symbolism. ChatGPT cannot solve logical problems, but it can approximate human language to an astonishing degree. If we ever create an AI, or what we now call an AGI, it will include elements of both these approaches.
In “Computing Machinery and Intelligence”, Turing made some really interesting observations about AI (“thinking machines” and “learning machines” as they were called then). It demonstrates stunning foresight:
You can view ChatGPT and Prolog as two ends of the spectrum Turing is describing here. Prolog is “thinking rationally”: It is predictable, logical. ChatGPT is “acting humanly”: It is an unpredictable, “undisciplined” model but does exhibit very human-like behaviours. We are “quite ignoerant of what is going on inside”. Neither approach is enough to achieve AGI, but they are such fundamentally different approaches that it is difficult to conceive of them working together except by some intermediary like Subsumption Architecture.
This is what I expect too. And hope - LLMs are way too unpredictable to control important things on their own.
I often say LLMs are doing for natural language what early computation did for mathematics. There’s still plenty of mathy jobs computers can’t do, but the really repetitive ones are gone and somewhat forgotten - nobody thinks of “computer” as a title.